Manuel Mazzara

Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan et al.

Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffer from inefficiencies, as their computational complexity scales quadratically with sequence length. To address these challenges, we propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba), which combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers. In MorpMamba, a novel token generation module first converts HSI patches into spatial-spectral tokens. These tokens are then processed through morphological operations such as erosion and dilation, utilizing depthwise separable convolutions to capture structural and shape information. A token enhancement module refines these features by dynamically adjusting the spatial and spectral tokens based on central HSI regions, ensuring effective feature fusion within each block. Subsequently, multi-head self-attention is applied to further enrich the feature representations, allowing the model to capture complex relationships and dependencies within the data. Finally, the enhanced tokens are fed into a state space module, which efficiently models the temporal evolution of the features for classification. Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models while maintaining high accuracy. The code will be made publicly available at \url{https://github.com/mahmad000/MorpMamba}.

Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama et al.

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention and token enhancement (MHSSMamba). This model integrates spectral and spatial information by enhancing spectral tokens and using multi-head attention to capture complex relationships between spectral bands and spatial locations. It also manages long-range dependencies and the sequential nature of HSI data, preserving contextual information across spectral bands. MHSSMamba achieved remarkable classification accuracies of 97.62\% on Pavia University, 96.92\% on the University of Houston, 96.85\% on Salinas, and 99.49\% on Wuhan-longKou datasets. The source code is available at \href{https://github.com/MHassaanButt/MHA\_SS\_Mamba}{GitHub}.

Muhammad Ahmad, Muhammad Usama, Manuel Mazzara et al.

Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This paper introduces WaveMamba, a novel approach that integrates wavelet transformation with the spatial-spectral Mamba architecture to enhance HSI classification. WaveMamba captures both local texture patterns and global contextual relationships in an end-to-end trainable model. The Wavelet-based enhanced features are then processed through the state-space architecture to model spatial-spectral relationships and temporal dependencies. The experimental results indicate that WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5\% on the University of Houston dataset and a 2.0\% increase on the Pavia University dataset.

Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Manuel Mazzara et al.

The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical transformer (PyFormer). This innovative approach organizes input data hierarchically into segments, each representing distinct abstraction levels, thereby enhancing processing efficiency for lengthy sequences. At each level, a dedicated transformer module is applied, effectively capturing both local and global context. Spatial and spectral information flow within the hierarchy facilitates communication and abstraction propagation. Integration of outputs from different levels culminates in the final input representation. Experimental results underscore the superiority of the proposed method over traditional approaches. Additionally, the incorporation of disjoint samples augments robustness and reliability, thereby highlighting the potential of our approach in advancing HSIC. The source code is available at https://github.com/mahmad00/PyFormer.

Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano et al.

Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. In this work, we propose the Differential Spatial-Spectral Transformer (DiffFormer), a novel framework designed to address the inherent challenges of HSIC, such as spectral redundancy and spatial discontinuity. The DiffFormer leverages a Differential Multi-Head Self-Attention (DMHSA) mechanism, which enhances local feature discrimination by introducing differential attention to accentuate subtle variations across neighboring spectral-spatial patches. The architecture integrates Spectral-Spatial Tokenization through three-dimensional (3D) convolution-based patch embeddings, positional encoding, and a stack of transformer layers equipped with the SWiGLU activation function for efficient feature extraction (SwiGLU is a variant of the Gated Linear Unit (GLU) activation function). A token-based classification head further ensures robust representation learning, enabling precise labeling of hyperspectral pixels. Extensive experiments on benchmark hyperspectral datasets demonstrate the superiority of DiffFormer in terms of classification accuracy, computational efficiency, and generalizability, compared to existing state-of-the-art (SOTA) methods. In addition, this work provides a detailed analysis of computational complexity, showcasing the scalability of the model for large-scale remote sensing applications. The source code will be made available at \url{https://github.com/mahmad000/DiffFormer} after the first round of revision.

Muhammad Ahmad, Manuel Mazzara, Salvatore Distifano

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model's true ability to generalize to new examples. This paper presents an innovative disjoint sampling approach for training SOTA models on Hyperspectral image classification (HSIC) tasks. By separating training, validation, and test data without overlap, the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was not exposed to during training or validation. Experiments demonstrate the approach significantly improves a model's generalization compared to alternatives that include training and validation data in test data. By eliminating data leakage between sets, disjoint sampling provides reliable metrics for benchmarking progress in HSIC. Researchers can have confidence that reported performance truly reflects a model's capabilities for classifying new scenes, not just memorized pixels. This rigorous methodology is critical for advancing SOTA models and their real-world application to large-scale land mapping with Hyperspectral sensors. The source code is available at https://github.com/mahmad00/Disjoint-Sampling-for-Hyperspectral-Image-Classification.

Muhammad Ahmad, Francesco Mauro, Manuel Mazzara et al.

Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To address these issues, we propose a novel Active Transfer Learning (ATL) framework built upon a Spatial-Spectral Transformer (SST) backbone. The framework integrates multistage transfer learning with an uncertainty-diversity-driven active learning mechanism that strategically selects highly informative and diverse samples for annotation, thereby significantly reducing labeling costs and mitigating sample redundancy. A dynamic layer freezing strategy is introduced to enhance transferability and computational efficiency, enabling selective adaptation of model layers based on domain shift characteristics. Furthermore, we incorporate a self-calibrated attention mechanism that dynamically refines spatial and spectral weights during adaptation, guided by uncertainty-aware feedback. A diversity-promoting sampling strategy ensures broad spectral coverage among selected samples, preventing overfitting to specific classes. Extensive experiments on benchmark cross-domain HSI datasets demonstrate that the proposed SST-ATL framework achieves superior classification performance compared to conventional approaches. The source code is publicly available at https://github.com/mahmad000/ATL-SST.

Nikolay Buldakov, Timur Khalilev, Salvatore Distefano et al.

This paper discusses an open source solution to smart-parking in highly urbanized areas. Interviews have been conducted with domain experts, user stories defined and a system architecture has been proposed with a case study. Our solution allows independent owners of parking space to be integrated into one unified system, that facilitates the parking situation in a smart city. The utilization of such a system raises the issues of trust and transparency among several actors of the parking process. In order to tackle those, we propose a smart contract-based solution, that brings in trust by encapsulating sensitive relations and processes into transparent and distributed smart contracts.

Denis Usachev, Azat Khusnutdinov, Manuel Mazzara et al.

Nowadays Digital Personal Assistants (DPA) become more and more popular. DPAs help to increase quality of life especially for elderly or disabled people. In this paper we develop an open source DPA and smart home system as a 3-rd party extension to show the functionality of the assistant. The system is designed to use the DPA as a learning platform for engineers to provide them with the opportunity to create and test their own hypothesis. The DPA is able to recognize users' commands in natural language and transform it to the set of machine commands that can be used to control different 3rd-party application. We use smart home system as an example of such 3rd-party. We demonstrate that the system is able to control home appliances, like lights, or to display information about the current state of the home, like temperature, through a dialogue between a user and the Digital Personal Assistant.

Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan et al.

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. Comprehensive experimental results were conducted on three HS datasets to substantiate the efficacy of various conventional DL models and Transformers. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and efficiency of HSC.

Muhammad Ahmad, Manuel Mazzara

Transformer-based methods have improved hyperspectral image classification (HSIC) by modeling long-range spatial-spectral dependencies; however, their attention mechanisms typically rely on dot-product similarity, which mixes feature magnitude and orientation and may be suboptimal for hyperspectral data. This work revisits attention scoring from a geometric perspective and introduces a cosine-normalized attention formulation that aligns similarity computation with the angular structure of hyperspectral signatures. By projecting query and key embeddings onto a unit hypersphere and applying a squared cosine similarity, the proposed method emphasizes angular relationships while reducing sensitivity to magnitude variations. The formulation is integrated into a spatial-spectral Transformer and evaluated under extremely limited supervision. Experiments on three benchmark datasets demonstrate that the proposed approach consistently achieves higher performance, outperforming several recent Transformer- and Mamba-based models despite using a lightweight backbone. In addition, a controlled analysis of multiple attention score functions shows that cosine-based scoring provides a reliable inductive bias for hyperspectral representation learning.

Muhammad Ahmad, Manuel Mazzara, Salvatore Distifano

3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based processing, excels in capturing intricate spatial relationships within images. Spatial-spectral Transformer (SST), meanwhile, specializes in modeling long-range dependencies through self-attention mechanisms. Therefore, this paper introduces a novel method: an attentional fusion of these two transformers to significantly enhance the classification performance of Hyperspectral Images (HSIs). What sets this approach apart is its emphasis on the integration of attentional mechanisms from both architectures. This integration not only refines the modeling of spatial and spectral information but also contributes to achieving more precise and accurate classification results. The experimentation and evaluation of benchmark HSI datasets underscore the importance of employing disjoint training, validation, and test samples. The results demonstrate the effectiveness of the fusion approach, showcasing its superiority over traditional methods and individual transformers. Incorporating disjoint samples enhances the robustness and reliability of the proposed methodology, emphasizing its potential for advancing hyperspectral image classification.

Raghad Salameh, Mohamad Al Mdfaa, Nursultan Askarbekuly et al.

This paper addresses the challenge of learning to recite the Quran for non-Arabic speakers. We explore the possibility of crowdsourcing a carefully annotated Quranic dataset, on top of which AI models can be built to simplify the learning process. In particular, we use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets. We integrated the API into an existing mobile application called NamazApp to collect audio recitations. We developed a crowdsourcing platform called Quran Voice for annotating the gathered audio assets. As a result, we have collected around 7000 Quranic recitations from a pool of 1287 participants across more than 11 non-Arabic countries, and we have annotated 1166 recitations from the dataset in six categories. We have achieved a crowd accuracy of 0.77, an inter-rater agreement of 0.63 between the annotators, and 0.89 between the labels assigned by the algorithm and the expert judgments.

Saad Sohail, Muhammad Usama, Usman Ghous et al.

Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination in applications such as environmental monitoring, agriculture, and urban analysis. However, the high dimensionality and spectral variability of HSI data pose significant challenges for feature extraction and classification. This paper presents EnergyFormer, a transformer-based framework designed to address these challenges through three key innovations: (1) Multi-Head Energy Attention (MHEA), which optimizes an energy function to selectively enhance critical spectral-spatial features, improving feature discrimination; (2) Fourier Position Embedding (FoPE), which adaptively encodes spectral and spatial dependencies to reinforce long-range interactions; and (3) Enhanced Convolutional Block Attention Module (ECBAM), which selectively amplifies informative wavelength bands and spatial structures, enhancing representation learning. Extensive experiments on the WHU-Hi-HanChuan, Salinas, and Pavia University datasets demonstrate that EnergyFormer achieves exceptional overall accuracies of 99.28\%, 98.63\%, and 98.72\%, respectively, outperforming state-of-the-art CNN, transformer, and Mamba-based models. The source code will be made available at https://github.com/mahmad000.

Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano et al.

Hyperspectral image (HSI) classification remains a challenging task due to the intricate spatial-spectral correlations. Existing transformer models excel in capturing long-range dependencies but often suffer from information redundancy and attention inefficiencies, limiting their ability to model fine-grained relationships crucial for HSI classification. To overcome these limitations, this work proposes MemFormer, a lightweight and memory-enhanced transformer. MemFormer introduces a memory-enhanced multi-head attention mechanism that iteratively refines a dynamic memory module, enhancing feature extraction while reducing redundancy across layers. Additionally, a dynamic memory enrichment strategy progressively captures complex spatial and spectral dependencies, leading to more expressive feature representations. To further improve structural consistency, we incorporate a spatial-spectral positional encoding (SSPE) tailored for HSI data, ensuring continuity without the computational burden of convolution-based approaches. Extensive experiments on benchmark datasets demonstrate that MemFormer achieves superior classification accuracy, outperforming state-of-the-art methods.

Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama et al.

Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges due to the high-dimensional nature of the data and the complex spectral-spatial relationships inherent in HSI. Traditional methods, including conventional machine learning and convolutional neural networks (CNNs), often struggle to effectively capture these intricate spectral-spatial features and global contextual information. Transformer-based models, while powerful in capturing long-range dependencies, often demand substantial computational resources, posing challenges in scenarios where labeled datasets are limited, as is commonly seen in HSI applications. To overcome these challenges, this work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms. The model introduces a novel hybridization of state-space modeling and Gated Recurrent Units (GRU), capturing both linear and nonlinear spatial-spectral dynamics. GraphMamba enhances the ability to model complex spatial-spectral relationships while maintaining scalability and computational efficiency across diverse HSI datasets. Through comprehensive experiments, we demonstrate that GraphMamba outperforms existing state-of-the-art models, offering a scalable and robust solution for complex HSI classification tasks.

Muhammad Ahmad, Adil Mehmood Khan, Manuel Mazzara et al.

Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models. The proposed pipeline achieved, indeed, an overall accuracy of 97\% for the Indian Pines, 100\% for Botswana, 99\% for Pavia University, Pavia Center, and Salinas datasets.

Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Several regularization techniques have been used to overcome the aforesaid issues. However, sometimes models learn to predict the samples extremely confidently which is not good from a generalization point of view. Therefore, this paper proposed an idea to enhance the generalization performance of a hybrid CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels. The proposed method helps to prevent CNN from becoming over-confident. We empirically show that in improving generalization performance, label smoothing also improves model calibration which significantly improves beam-search. Several publicly available Hyperspectral datasets are used to validate the experimental evaluation which reveals improved generalization performance, statistical significance, and computational complexity as compared to the state-of-the-art models. The code will be made available at https://github.com/mahmad00.

Muhammad Ahmad, Sidrah Shabbir, Rana Aamir Raza et al.

Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.

Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy et al.

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies on the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral features, spatial features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.

Barakat. J. Akinsanya, Luiz J. P. Araújo, Mariia Charikova et al.

Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. This work is meant to serve as a starting point for practitioners willing to add ML to their software development toolbox. It categorises recent literature and identifies trends and limitations. The survey shows as some authors have agreed that industrial applications of ML for SD have not been as popular as the reported results would suggest. The conducted investigation shows that, despite having promising findings for a variety of SD tasks, most of the studies yield vague results, in part due to the lack of comprehensive datasets in this problem domain. The paper ends with concluding remarks and suggestions for future research.

Swati Megha, Joseph Lamptey, Hamza Salem et al.

The energy industry needs to shift to a new paradigm from its classical model of energy generation, distribution, and management. This shift is necessary to handle digitization, increased renewable energy generation, and to achieve goals of environmental sustainability. This shift has several challenges on its way and has been seen through research and development that blockchain which is one of the budding technology in this era could be suitable for addressing those challenges. This paper is aimed at the survey of all the research and development related to blockchain in the energy industry and uses a software engineering approach to categories all the existing work in several clusters such as challenges addressed, quality attribute promoted, the maturity level of the solutions, etc. This survey provides researchers in this field a well-defined categorization and insight into the existing work in this field from 3 different perspectives (challenges, quality attributes, maturity).

Jean-Michel Bruel, Sophie Ebersold, Florian Galinier et al.

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, good for understandability but lacking in precision. To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as "formal". Many exist, differing in their style, scope and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods. The analysis uses a set of 9 complementary criteria, such as level of abstraction, tool availability, traceability support. It classifies the approaches into five categories: general-purpose, natural-language, graph/automata, other mathematical notations, seamless (programming-language-based). It presents approaches in all of these categories, altogether 22 different ones, including for example SysML, Relax, Eiffel, Event-B, Alloy. The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches. (This is the full version of the survey, including some sections and two appendices which, because of length restrictions, do not appear in the submitted version.)

Antonio Capizzi, Salvatore Distefano, Manuel Mazzara

DevOps is a quite effective approach for managing software development and operation, as confirmed by plenty of success stories in real applications and case studies. DevOps is now becoming the main-stream solution adopted by the software industry in development, able to reduce the time to market and costs while improving quality and ensuring evolvability and adaptability of the resulting software architecture. Among the aspects to take into account in a DevOps process, data is assuming strategic importance, since it allows to gain insights from the operation directly into the development, the main objective of a DevOps approach. Data can be therefore considered as the fuel of the DevOps process, requiring proper solutions for its management. Based on the amount of data generated, its variety, velocity, variability, value and other relevant features, DevOps data management can be mainly framed into the BigData category. This allows exploiting BigData solutions for the management of DevOps data generated throughout the process, including artefacts, code, documentation, logs and so on. This paper aims at investigating data management in DevOps processes, identifying related issues, challenges and potential solutions taken from the BigData world as well as from new trends adopting and adapting DevOps approaches in data management, i.e. DataOps.

Antonio Capizzi, Salvatore Distefano, Manuel Mazzara et al.

The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only in particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the last release" or "failures detected in the staging environment" are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the staging environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a "proof of concept". The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.

Ilya Afanasyev, Alexander Kolotov, Ruslan Rezin et al.

Decentralization, immutability and transparency make of Blockchain one of the most innovative technology of recent years. This paper presents an overview of solutions based on Blockchain technology for multi-agent robotic systems, and provide an analysis and classification of this emerging field. The reasons for implementing Blockchain in a multi-robot network may be to increase the interaction efficiency between agents by providing more trusted information exchange, reaching a consensus in trustless conditions, assessing robot productivity or detecting performance problems, identifying intruders, allocating plans and tasks, deploying distributed solutions and joint missions. Blockchain-based applications are discussed to demonstrate how distributed ledger can be used to extend the number of research platforms and libraries for multi-agent robotic systems.

Ilya Afanasyev, Manuel Mazzara, Subham Chakraborty et al.

Internet of Things (IoT) and robotics cannot be considered two separate domains these days. Internet of Robotics Things (IoRT) is a concept that has been recently introduced to describe the integration of robotics technologies in IoT scenarios. As a consequence, these two research fields have started interacting, and thus linking research communities. In this paper we intend to make further steps in joining the two communities and broaden the discussion on the development of this interdisciplinary field. The paper provides an overview, analysis and challenges of possible solutions for the Internet of Robotic Things, discussing the issues of the IoRT architecture, the integration of smart spaces and robotic applications.

Mansur Khazeev, Manuel Mazzara, Daniel De Carvalho et al.

Formal methods yet advantageous, face challenges towards wide acceptance and adoption in software development practices. The major reason being presumed complexity. The issue can be addressed by academia with a thoughtful plan of teaching and practise. The user study detailed in this paper is examining AutoProof tool with the motivation to identify complexities attributed to formal methods. Participants' (students of Masters program in Computer Science) performance and feedback on the experience with formal methods assisted us in extracting specific problem areas that effect tool usability. The study results infer, along with improvements in verification tool functionalities, teaching program must be modified to include pre-requisite courses to make formal methods easily adapted by students and promoting their usage in software development process.

Manuel Mazzara, Antonio Bucchiarone, Nicola Dragoni et al.

In this chapter we offer an overview of microservices providing the introductory information that a reader should know before continuing reading this book. We introduce the idea of microservices and we discuss some of the current research challenges and real-life software applications where the microservice paradigm play a key role. We have identified a set of areas where both researcher and developer can propose new ideas and technical solutions.

Evgeny Bobrov, Antonio Bucchiarone, Alfredo Capozucca et al.

DevOps processes comply with principles and offer practices with main objective to support efficiently the evolution of IT systems. To be efficient a DevOps process relies on a set of integrated tools. DevOps is the first required competency together with Agile Method required by the industry. DevOps processes are sharing many aspects with microservices approaches especially the modularity and flexibility which enables continuous change and delivery. As a new approach it is necessary to developp and offer to the academy and to the industry training programs to prepare our engineers in the best possible way. In this chapter we present the main aspects of the educational effort made in the recent years to educate to the concepts and values of the DevOps philosophy. This includes principles, practices, tools and architectures, primarily the Microservice architectural style. Two experiences have been made, one at academic level as a master program course and the other, as an industrial training. Based on those two experiences, we provide a comparative analysis and some proposals in order to develop and improve DevOps education for the future.

Evgeny Bobrov, Antonio Bucchiarone, Alfredo Capozucca et al.

This paper describes our experience of delivery educational programs in academia and in industry on DevOps, compare the two approaches and sum-up the lessons learnt. We also propose a vision to implement a shift in the Software Engineering Higher Education curricula.

Manuel Mazzara, Ilya Afanasyev, Smruti R. Sarangi et al.

The vision encompassing Smart and Software-defined Buildings (SSDB) is becoming more and more popular and its implementation is now more accessible due to the widespread adoption of the IoT infrastructure. Some of the most important applications sustaining this vision are energy management, environmental comfort, safety and surveillance. This paper surveys IoT and SSB technologies and their cooperation towards the realization of Smart Spaces. We propose a four-layer reference architecture and we organize related concepts around it. This conceptual frame is useful to identify the current literature on the topic and to connect the dots into a coherent vision of the future of residential and commercial buildings.

Vivek Kumar, Brojo Kishore Mishra, Manuel Mazzara et al.

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms

Vivek Kumar, Manuel Mazzara, Maj. Gen. et al.

Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Naïve Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.

Nikita Lozhnikov, Leon Derczynski, Manuel Mazzara

Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language.

Manuel Mazzara, Alexandr Naumchev, Larisa Safina et al.

This paper describes our experience of training a team of developers of an East-European phone service provider. The training experience was structured in two sessions of two days each conducted in different weeks with a gap of about fifteen days. The first session was dedicated to the Continuous Integration Delivery Pipeline, and the second on Agile methods. We summarize the activity, its preparation and delivery and draw some conclusions out of it on our mistakes and how future session should be addressed.

Vladimir Marochko, Leonard Johard, Manuel Mazzara et al.

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting.

Ruslan Rezin, Ilya Afanasyev, Manuel Mazzara et al.

Multiplayer computer games play a big role in the ever-growing entertainment industry. Being competitive in this industry means releasing the best possible software, and reliability is a key feature to win the market. Computer games are also actively used to simulate different robotic systems where reliability is even more important, and potentially critical. Traditional software testing approaches can check a subset of all the possible program executions, and they can never guarantee complete absence of errors in the source code. On the other hand, during more than twenty years, Model Checking has demonstrated to be a powerful instrument for formal verification of large hardware and software components. In this paper, we contribute with a novel approach to formally verify computer games. We propose a method of model construction that starts from a computer game description and utilizes Model Checking technique. We apply the method on a case study: the game Penguin Clash. Finally, an approach to game model reduction (and its implementation) is introduced in order to address the state explosion problem.

Daniel de Carvalho, Rasheed Hussain, Adil Khan et al.

This paper summarizes the experience of teaching an introductory course to programming by using a correctness by construction approach at Innopolis University, Russian Federation. In this paper we claim that division in beginner and advanced groups improves the learning outcomes, present the discussion and the data that support the claim.

Alexandr Naumchev, Bertrand Meyer, Manuel Mazzara et al.

The considerable effort of writing requirements is only worthwhile if the result meets two conditions: the requirements reflect stakeholders' needs, and the implementation satisfies them. In usual approaches, the use of different notations for requirements (often natural language) and implementations (a programming language) makes both conditions elusive. AutoReq, presented in this article, takes a different approach to both the writing of requirements and their verification. Applying the approach to a well-documented example, a landing gear system, allowed for a mechanical proof of consistency and uncovered an error in a published discussion of the problem.

Kizilov Mikhail, Antonio Bucchiarone, Manuel Mazzara et al.

This paper discusses a roadmap to investigate Domain Objects being an adequate formalism to capture the peculiarity of microservice architecture, and to support Software development since the early stages. It provides a survey of both Microservices and Domain Objects, and it discusses plans and reflections on how to investigate whether a modeling approach suited to adaptable service-based components can also be applied with success to the microservice scenario.

Alberto Giaretta, Nicola Dragoni, Manuel Mazzara

Cloud computing is steadily growing and, as IaaS vendors have started to offer pay-as-you-go billing policies, it is fundamental to achieve as much elasticity as possible, avoiding over-provisioning that would imply higher costs. In this paper, we briefly analyse the orchestration characteristics of PaaSSOA, a proposed architecture already implemented for Jolie microservices, and Kubernetes, one of the various orchestration plugins for Docker; then, we outline similarities and differences of the two approaches, with respect to their own domain of application. Furthermore, we investigate some ideas to achieve a federation of the two technologies, proposing an architectural composition of Jolie microservices on Docker Container-as-a-Service layer.

Nicola Dragoni, Alberto Giaretta, Manuel Mazzara

The Internet of Things makes possible to connect each everyday object to the Internet, making computing pervasive like never before. From a security and privacy perspective, this tsunami of connectivity represents a disaster, which makes each object remotely hackable. We claim that, in order to tackle this issue, we need to address a new challenge in security: education.

Michele De Donno, Nicola Dragoni, Alberto Giaretta et al.

The 2016 is remembered as the year that showed to the world how dangerous Distributed Denial of Service attacks can be. Gauge of the disruptiveness of DDoS attacks is the number of bots involved: the bigger the botnet, the more powerful the attack. This character, along with the increasing availability of connected and insecure IoT devices, makes DDoS and IoT the perfect pair for the malware industry. In this paper we present the main idea behind AntibIoTic, a palliative solution to prevent DDoS attacks perpetrated through IoT devices.

Manuel Mazzara, Kevin Khanda, Ruslan Mustafin et al.

In this paper we offer an overview on the topic of Microservices Science and Engineering (MSE) and we provide a collection of bibliographic references and links relevant to understand an emerging field. We try to clarify some misunderstandings related to microservices and Service-Oriented Architectures, and we also describe projects and applications our team have been working on in the recent past, both regarding programming languages construction and intelligent buildings.

Leonard Johard, Victor Rivera, Manuel Mazzara et al.

In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes. It simplifies existing network structures by removing intralayer weights, essentially cutting the number of weights that need to be trained in half.

Victor Rivera, JooYoung Lee, Manuel Mazzara et al.

Formal modelling languages play a key role in the development of software since they enable users to prove correctness of system properties. However, there is still not a clear understanding on how to map a formal model to a specific programming language. In order to propose a solution, this paper presents a source-to-source mapping between Event- B models and Eiffel programs, therefore enabling the proof of correctness of certain system properties via Design-by-Contract (natively supported by Eiffel), while still making use of all features of O-O programming.

Muhammad Ahmad, Ali Kashif Bashir, Adil Mehmood Khan et al.

Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by a smartphone. A set of preprocessing schemes is applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized using a non-linear unsupervised feature selection method. The selected features create an underlying gait biometric representation able to discriminate among individuals and identify them uniquely. Different classifiers (i.e. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Bagging, and Extreme Learning Machine (ELM)) are adopted to achieve accurate legitimate user identification. Extensive experiments on a group of $16$ individuals in an indoor environment show the effectiveness of the proposed solution: with $5$ to $70$ samples per window, KNN and bagging classifiers achieve $87-99\%$ accuracy, $82-98\%$ for ELM, and $81-94\%$ for SVM. The proposed pipeline achieves a $100\%$ true positive and $0\%$ false-negative rate for almost all classifiers.

Claudio Guidi, Ivan Lanese, Manuel Mazzara et al.

Microservices is an emerging development paradigm where software is obtained by composing autonomous entities, called (micro)services. However, microservice systems are currently developed using general-purpose programming languages that do not provide dedicated abstractions for service composition. Instead, current practice is focused on the deployment aspects of microservices, in particular by using containerization. In this chapter, we make the case for a language-based approach to the engineering of microservice architectures, which we believe is complementary to current practice. We discuss the approach in general, and then we instantiate it in terms of the Jolie programming language.

Mansur Khazeev, Victor Rivera, Manuel Mazzara et al.

In this paper we report the experience of using AutoProof to statically verify a small object oriented program. We identified the problems that emerged by this activity and we classified them according to their nature. In particular, we distinguish between tool-related and methodology-related issues, and propose necessary changes to simplify both tool and method.