Reviewerly/exHarmony huggingface dataset:
Multi-SPMiner: A Deep Learning Framework for Multi-Graph Frequent Pattern Mining with Application to spatiotemporal Graphs

Mining frequent patterns in multigraphs is a challenging task in graph analysis with numerous real-world applications. This paper introduces a novel framework for frequent pattern mining on multi-graphs using the multi-SPMiner method. The approach is inspired by SPMiner, which was the first approach to employ deep learning in graph motif mining tasks. Multi-SPMiner builds on this foundation and focuses on the extraction of frequent motifs in single multi-graphs, specifically spatiotemporal graphs. Multi-SPMiner employs a two-step approach to extract the most frequent motifs in a graph with a high support value. In the first step, it embeds the nodes into an embedding order space, and in the second step, it performs a walk in the space to obtain the frequent motifs by iteratively growing the motif starting from a single node. The results obtained highlight the effectiveness of the proposed approach in identifying frequent motifs in single multigraphs, which is a crucial task in many real-world applications. Moreover, we demonstrate that our method is a generalization of SPMiner by testing it on single connection graphs.
A student modeling method combining Deep Learning and forgetting models with Knowledge Tracing

Educational Data Mining (EDM) aims to enhance education by analyzing learners’ skills and question difficulty levels using machine learning methods. Knowledge Tracing (KT), a subfield of EDM, utilizes Hidden Markov Models to estimate learners’ abilities and predict their performance on unseen questions. While deep learning methods, such as bi-directional RNNs, have improved KT's accuracy, they may lack interpretability from an educational psychology perspective. Item Response Theory (IRT), widely used in educational statistics, offers greater explanatory potential. This study proposes a model that integrates the concept of forgetting into IRT for improved accuracy and explainability in Knowledge Tracing using bi-directional RNNs. The forgetting concept is based on Ebbinghaus’ forgetting curve theory. Three experiments were conducted using synthetic data to compare a model from a previous study, a model based on the proposed method, and a model that combines the previous study's model with IRT but excludes the forgetting concept.
Explainable Deep Learning for Face Mask Detection

The COVID-19 pandemic has radically changed our daily habits. One of these habits introduced is the use of a face mask, to avoid the spread of the infection, to be used especially in closed and crowded environments. To protect people and to counter the spread of COVID-19, in this paper we propose a method to automatically verify from images whether people are wearing a face mask. We designed a deep learning network to classify whether an image under analysis is with a mask or without a face mask. With the aim to provide explainability to the classifier decision, the proposed method is able to localise the areas of the image under analysis symptomatic of a certain prediction: in this way it is possible to understand the reason why the model predicts a certain label. The experimental analysis considers 7553 (with mask and without mask) images showing that the proposed approach is able to obtain interesting performances in face mask detection.
Proof-of-Variable-Authority: A Blockchain Consensus Mechanism for Securing IoT Networks

In today’s world, Internet of Things (IoT) has become an integral part of our daily lives. As IoT networks continue to gain importance, the need for efficient network management mechanisms becomes crucial. Currently, the prevalent approach involves utilizing a centralized server as the core management device, along with interfaces provided by major corporations. In this paper, we explore blockchain technology for IoT network management, aiming to empower individuals to retain control over their data and eliminate the risks associated with a single point of failure. However, implementing a consensus mechanism for IoT devices in distributed systems is challenging due to the high computational demands of existing options. This poses a significant challenge in securing IoT networks, especially when most devices are low-powered. To overcome this challenge, we propose modification to the Proof-of-Authority (PoA) consensus mechanism by introducing a decentralized approach to the original solution.
π-means: Granular Approach towards Interactive Data Exploration

In this paper, we examine the possibility of employing the idea of progressive-inductive (π) aggregation in the k-means algorithm. We base our work on the interactive visualization framework called Skydive which is a tightly coupled system that combines data aggregation and visual aggregation for smooth user interaction. It is based on an aggregate pyramid data structure (π-cube), allowing a visualization client to quickly filter the data. Skydive was designed for interactive data visualization in terms of expressiveness and efficiency. Therefore, the underlying data structure enabled access to data on desired levels of granularity. In this paper, we utilize this concept to speed up the process of clustering proposing π-means - a modified version of the k-means algorithm. This will allow us to explore further possible applications of π-cube towards interactive data exploration.
A systematic review on the use of augmented reality in management and business

Augmented reality (AR) is a technology increasingly used by organizations in various areas of their operations. The purpose of this article is to identify research directions related to the application of AR in management and business. An additional purpose is to develop a conceptual framework as a basis for future research on this topic. Using a systematic literature review method, the main research directions for AR were identified. The researchers analysed the use of AR technology in the areas of good sector, tourism industry, marketing, user experience, construction industry, ICT, education, food sector, service sector, warehouse, disaster risk management, architecture. Then, using the method of virtual ethnography, future research directions were identified, which include the use of augmented reality technology in the areas of performance management, data management, small business and business development.
SailGenie: SAiling expertIse to knowLedge Graph through opEN Information Extraction

This work is focused on the sailing domain, for which several innovative technologies are being adopted to improve sailing efficiency, performance, and safety. In this context a knowledge graph could be used, for example, to represent information about different types of boats, sailing techniques, maritime safety, or weather conditions. Although numerous construction methods or ready-to-go knowledge graphs have been proposed in many fields, the sailing domain still needs to be explored. As the most effective methods rely on domain-specific datasets, the absence of suitable and available sailing datasets is one of the main challenges. Although several Open Information Extraction (OpenIE) methods may generate relevant triplets (the elementary units composing a knowledge graph) from arbitrary text without any additional information about its topic, such methods usually generate many incorrect triplets. In this paper, we aim (i) to address the aforementioned problem by proposing an innovative method that combines in an improved and strengthened way different OpenIE tools to generate proper triplets from domain-specific sources and, in particular, (ii) to build and release a suitable dataset for the sailing domain. Results confirm that our proposal can maximize the extracted information and infer unique information irretrievable by the classical OpenIE tools and, furthermore, that the generated dataset is significantly valuable for the sailing scenario.
BlockchainPedia: A Comprehensive Framework for Blockchain Network Comparison

Since the release of blockchain technology back in 2008, a huge number of blockchain-based systems have been developed. As of Sep’22, there are at least 1,000 blockchain systems ready to be used, with at least four types of networks: public, private, hybrid, and consortium. With the many options you can choose from, a valid question arises: How can we choose a system that fits best with our needs? The purpose of Blockchain Pedia is to help users make the best decision in choosing the right blockchain system, giving the possibility to see the differences between multiple options. The application is designed for anyone who wants to build a project based on blockchain technology, giving recommendations based on the needs of the developer.
Object Detection and Localisation in Thermal Images by means of UAV/Drone

Object detection is one of the crucial tasks that has made deep learning of fundamental importance in last years, also thanks to the use of drones and unmanned aerial vehicles able to obtain images and videos in real-time from any location. In the absence of daylight or artificial light for object detection, it is necessary to resort to thermal images, obtained by converting infrared radiation (i.e., heat) into visible images that depict the spatial distribution of temperature differences in a scene viewed by a thermal camera. However, object detection in this type of image and video stream is still challenging due to the complicated scene information and coarse resolution compared to a visible image or video. In this paper, we propose a method aimed to detect objects in thermal images, in particular, the proposed method is aimed to identify persons and dogs acquired from a thermal camera installed, for instance, on drones or unmanned aerial vehicles. We employ an object detection model, i.e., the ”You only look once” one, for the automatic localization of objects in thermal images. In the evaluation of a dataset composed of 203 images with 257 annotations, the proposed method obtains a precision of 0.897, a recall equal to 0.904, and a mean Average Precision value (with an Intersection over Union greater than 0.5) equal to 0.924, showing the effectiveness of the proposed method for the identification and location of persons and dogs from images directly acquired with thermal cameras.
Predictive Accuracy Index in evaluating the dataset shift (case study)

A dataset shift takes place in a situation, where the joint distribution of inputs and outputs differs between the stages of training, testing, and using predictive models. If the distribution of current data for the implemented forecasting model changed significantly compared to the distribution of data used to develop it, them it could lead to its incorrect operation. The aim of the study was to compare the properties of two indicators, the Univariate Predictive Accuracy Index (UPAI) and Multivariate Predictive Accuracy Index (MPAI). The research procedure was carried out in 2 scenarios. The first involved a comparison of UPAI and MPAI for the distributions of categorical variables, while the second for distributions of continuous variables. The obtained MPAI values for both scenarios were summarized and compared with the UPAI and PSI values calculated and published in our previous article [1]. The results of the experiment proved that basing the decision on the need to calibrate the model or build a completely new model on the basis of MPAI, a measure that takes into account the multivariate distribution of variables, is superior to one-dimensional measures.
Mapping the environment in a closed room using a mobile robot

This paper proposes the author's design of a mobile robot intended for creating a two-dimensional map of the environment. The first part of the paper describes the scope of automated systems called robots. On this basis, the design assumptions of the author's robot were defined, the components used in the construction are shown, the operation of the Robot Operating System (ROS) and the packages used in this robot control system are presented. The main idea, which was to govern the project was its low manufacturing cost. Thus, the use of the optical system of the Kinect device. As a result. the operation of the algorithm controlling the robot via the ATmega328 microcontroller is demonstrated. The results of the experimental verification of the robot's performance are presented in the form of two-dimensional maps of the rooms in which the experiments were carried out.
Towards the use of post-hoc explainable methods to define and detect semantic situations of importance in medical data

With the emergence of Internet of Things (IoT), it is now possible to precisely monitor various properties, thus creating huge amount of data to integrate and to analyse. In the case of medical data, this may enable to detect weak signals, values slightly abnormal, on a monitored patient in order to act before the trigger of a serious health condition problem. The issue is then to interpret these data in real-time to raise explainable alerts which would then help physicians to make medical decisions and diagnosis. We previously developed a stream reasoning system based on an ontology to capture the context of a patient in order to provide the notion of situation of importance and to detect them. However, such system needs to be initialised with personalized thresholds depending on each patient. To do so and to help in identifying new situations of importance or refine existing ones, we propose an approach that uses data-driven approaches and post-hoc explainability methods, in particular SHapley Additive exPlanations (SHAP) algorithm. The first results are shown and discussed over two different medical use cases.
Approximation of functions by a wavelet neural network built using a hybrid approach based on the genetic algorithm and the Gram-Schmidt Selection method

The performance and power of Wavelet Neural Networks (WNNs) rely on the proper structure of the WNN. In this study, a hybrid approach is suggested to build a Wavelet Neural Network. We design a network based on the genetic algorithm (GA), the Gram-Schmidt Selection (GSS) method, and the multilibrary wavelet function (MLWF). The initialization of the WNN is performed by the GSS method, which aims to select the candidate library wavelet functions (MLWF) to develop the WNN. The GA is used to solve the structure and the learning of the WNN and the GSS algorithm is applied to select the important wavelets. The GA was used to calculate the suitable values of the Network parameters, to solve the structure and learning of the WNN. In this research, the GSS method is used to determine a set of best wavelets whose centres and dilation parameters are used as initial values for subsequent training. Experimental tests proved that the proposed approach is very efficient and accurate.
Hierarchical Support Vector Machine based Classifier for Autocoding

This paper proposes a hierarchical support vector machine based classifier for autocoding. The purpose of this method is to improve classification accuracy utilizing numerical features obtained from probability scores by support vector machine (SVM). The proposed method captures the tendency to be incorrectly predicated for each label from the probability scores obtained from SVM. Using the captured information, we train new support vector machines for each targeted label considering the feature of the targeted label to improve the classification accuracy. The numerical examples with governmental survey data show a better performance of the proposed method.
A Novel Recommendation Approach For Groups Based On Aggregating Top-k Lists

One of the most challenging issues in group recommendation is how to consider the dispersion in members’ preferences to reach a joint recommendation satisfying all the group. The group recommendation is generated by aggregating either the users’ preferences or the individual recommendations. In this paper, we focus on the recommendations aggregation strategy which consists in generating a recommendation list for each group member, then combining these individual lists to produce a single group recommendation list. Many aggregation functions (such as average, least misery, Borda, etc.) were used to resolve the ranking aggregation problem. However, they cannot support the partial information presented over the top-k recommendation lists which may not contain the same set of alternatives. To this end, we propose a group recommendation method based on a novel aggregation function that considers the partial rankings and computes each item's relevance to the target group based on both the users’ preferences and the items’ positions. We conduct a deep experiment to study the impact of some parameters (such as the individual recommendation algorithms, the aggregation functions, and the group size) on the group recommendations quality. We have shown that the accuracy of the group recommendations does not depend on the algorithm used to generate individual recommendations. However, it is strongly affected by the method used to aggregate data and the group size. Experimental results show a considerable improvement in the group recommendation performance using the proposed solution.
A Multi-stage Multi-modal Classification Model for DeepFakes Combining Deep Learned and Computer Vision Oriented Features

Recent advances in deep learning have empowered media synthesis and alteration to achieve levels of realism that were previously unheard of. Artificial intelligence is a potent tool that may be used to modify digital data, such as images, videos, and audio files, through the use of emerging deepfake technologies. Deepfake technology has the potential to significantly affect the reliability of multimedia data through the synthesis of fake media. Significant ramifications arise from this for individuals, organizations, and society at large. With the pace and accessibility of social media, convincing deepfakes can swiftly reach millions of people and adversely influence public opinion. To this end, we propose a multi-modal feature-based classification model that can distinguish between deepfake and real videos efficiently. We have used prefabricated image features as well as a variety of Convolutional Neural Network (CNN) model-generated features, including ResNet50, ResNet101, VGG16, and VGG19. The fake videos are taken up for further investigation to detect their source of origin. We propose a CNN-based classifier for deepfake detection and also explore the efficiency of multiple feature-based classifiers in this respect. This enables us to evaluate the comparative performance of both. The proposed model achieves an accuracy of 99.06% on deepfake classification and 98.75% on source identification when tested on a publicly available FaceForensics++ dataset.
Ensuring Data Security in the Context of IoT Forensics Evidence Preservation with Blockchain and Self-Sovereign Identities

As Internet of Things (IoT) networks expand, significant challenges related to the secure management of data generated by these devices emerge. The integrity and reliability of this data are critical in sensitive sectors, such as forensic evidence preservation. In this context, we present an innovative architecture based on Self-Sovereign Identity (SSI) tailored for resource-constrained IoT devices. Our proposal addresses the intrinsic limitations of current systems, which often fail to ensure the integrity, reliability, and traceability of data originating from IoT devices. To tackle this issue, we propose using decentralized identifiers (DIDs) to establish unique identities for IoT devices, accompanied by verifiable credentials (VCs) that attest to data ownership. To implement this solution, we have developed an application that serves as a gateway for resource-constrained devices, typically certified and connected to a broker. Our application utilizes Hyperledger Aries and Indy libraries, providing essential resources to address these challenges. Furthermore, we conducted comprehensive simulations and a performance analysis to validate the effectiveness of our approach. Integrating these technologies enables the certification of data collected by IoT devices, offering a robust framework for the data custody chain. Consequently, this substantially contributes to preserving this data’s integrity, reliability, and traceability in critical environments.
Big Data Forensics on Apache Kafka

There is a growing demand for information exchange in the age of the Internet of Things. One common scenario involves transferring data from distributed devices in the field to central servers or cloud environments. However, little research has been done on the possibilities for forensic investigation of supporting infrastructure such as Apache Kafka, which plays a crucial role in modern big data architectures. In this paper, we present our work on the forensic investigation of Apache Kafka. We use methodologies of reverse engineering to infer the data formats that Apache Kafka uses server-side. The results help us to implement a new module that is able to read Apache Kafka log files. An investigator can load the module in the open-source forensic platform “Autopsy”. We highlight possibilities and limitations regarding encryption and data retention in Apache Kafka and suggest to store data decentralized when it comes to sensitive data. As a result of these measures, applications become more resilient to attacks and are able to provide increased security, ethical standards, and freedom for the application users. This can be a unique selling point in future data driven applications.
AI4HR Recruiter: a job recommender system for internal recruitment in consulting companies

Recruiting job candidates is a crucial activity for consulting companies to fulfill their consulting activities for customers or internal projects. Due to the complexity of the recruitment process, companies adopt standardized resume formats to be able to identify or compare candidates. However, this process requires significant involvement from human resource specialists and project leaders. In this paper, we present AI4HR Recruiter, a recommender system designed to improve the internal recruitment process of consultants. The system uses artificial intelligence-based techniques, specifically pre-trained sentence transformers. It aims at supporting recruiters in identifying and selecting candidates, rather than automating the process. We measure improvement in efficiency in terms of gains in recruiters time induced by the recommendation explanation presence. We also measure the impact on users’ satisfaction of two different types of algorithm to generate recommendations. To do so, we have been conducting a two fold study in a real-life setting for six months, where on the one hand, telemetry data was gathered while recommendation process user interaction was being performed and on the other hand, surveys about users’ satisfaction were conducted afterward. Our results evaluation demonstrates the effectiveness of the system in assisting recruiters in candidates selection process.
DGCN: Learning Graph Representations Via Dense Connections

In the last decades, learning over graph data has became one of the most challenging tasks in deep learning. The generally proposed Graph Neural Network (GNN) framework computes a hidden state for every node in the graph by applying nonlinear transformations to its neighborhood. Then updates the node of interest's hidden state. Nevertheless, the node features can contain discriminative information. That can get lost over GNN transformations. In this work, we present a new variant of GNN architecture where we combine node features and GNN activations to learn node representations in the graph. We conduct extensive experiments on two graph prediction tasks (node classification and link prediction) and show that our method can match and outperforms state-of-the-art results on five challenging datasets.