CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction

The right choice of features to be extracted from individual or aggregated observations is an extremely critical factor for the success of modern network traffic classification approaches based on machine learning. Such activity, usually in charge of the designers of the classification scheme is strongly related to their experience and skills, and definitely characterizes the whole approach, implementation strategy as well as its performance. The main aim of this work is supporting this process by mining new and more expressive, meaningful and discriminating features from the basic ones without human intervention. For this purpose, a novel autoencoder-based deep neural network architecture is proposed where multiple autoencoders are embedded with convolutional and recurrent neural networks to elicit relevant knowledge about the relations existing among the basic features (spatial-features) and their evolution over time (temporal-features). Such knowledge, consisting in new properties that are not immediately evident and better represent the most hidden and representative traffic dynamics can be successfully exploited by machine learning-based classifiers. Different network combinations are analyzed both from a theoretical perspective, and through specific performance evaluation experiments on a real network traffic dataset. We show that the traffic classifier obtained by stacking the autoencoder with a fully-connected neural network, achieves up to a 28% improvement in average accuracy over state-of-the-art machine learning-based approaches, up to a 10% over pure convolutional and recurrent stacked neural networks, and 18% over pure feed-forward networks. It is also able to maintain high accuracy even in the presence of unbalanced training datasets

Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

The continuous emergence of new and sophisticated malware specifically targeting Android-based Internet of Things devices is causing significant security hazards and is consequently fostering the need for effective detection models and strategies able to work with these hardware-constrained devices. In addition, since such models are often trained on confidential application data, many involved subjects are reluctant to share their data for this purpose. Accordingly, several Federated Learning-based solutions are emerging, which rely on the capabilities of Machine Learning models in malware detection/classification without sharing user data. However, Federated Learning methods are often adversely affected by non-independent and identically distributed data in terms of both the required training time and classification results. Therefore, a promising solution could be to overcome the Federated Learning-related issues by preserving the privacy of end-user data. In this direction, the capabilities of Markov chains and associative rules are extended within a federated environment to face malware classification tasks in the IoT scenario. The presented approach, evaluated on several malware families, has achieved an average accuracy of 99% in the presence of centralized and decentralized unbalanced training/testing data by overcoming the most common state-of-the-art approaches. Also, its runtime performance is comparable with centralized ones by considering several non independent and identically distributed dataset partitions, splitting criteria, and clients, respectively

Chatbot: An education support system for student

In the last few years there has been a fast growing up of the use of Chatbots in various fields, such as Health Care, Marketing, Educational, Supporting Systems, Cultural Heritage, Entertainment and many others. This paper presents the realization of a prototype of a Chatbot in educational domain: the purpose has focused on the design of the specific architecture, model to manage communication and furnish the right answers to the student. For this aim, it has been realized a system that can detect the questions and thanks to the use of natural language processing techniques and the ontologies of domain, gives the answers to student. Finally, after the implementation of the designed model, experimental campaign was conducted in order to demonstrate its utility

Current State and Future Challenges for PI3K Inhibitors in Cancer Therapy

The phosphoinositide 3 kinase (PI3K)-protein kinase B (PKB/AKT)-mammalian target of the rapamycin (mTOR) axis is a key signal transduction system that links oncogenes and multiple receptor classes which are involved in many essential cellular functions. Aberrant PI3K signalling is one of the most commonly mutated pathways in cancer. Consequently, more than 40 compounds targeting key components of this signalling network have been tested in clinical trials among various types of cancer. As the oncogenic activation of the PI3K/AKT/mTOR pathway often occurs alongside mutations in other signalling networks, combination therapy should be considered. In this review, we highlight recent advances in the knowledge of the PI3K pathway and discuss the current state and future challenges of targeting this pathway in clinical practice

Current State and Future Challenges for PI3K Inhibitors in Cancer Therapy

The phosphoinositide 3 kinase (PI3K)-protein kinase B (PKB/AKT)-mammalian target of the rapamycin (mTOR) axis is a key signal transduction system that links oncogenes and multiple receptor classes which are involved in many essential cellular functions. Aberrant PI3K signalling is one of the most commonly mutated pathways in cancer. Consequently, more than 40 compounds targeting key components of this signalling network have been tested in clinical trials among various types of cancer. As the oncogenic activation of the PI3K/AKT/mTOR pathway often occurs alongside mutations in other signalling networks, combination therapy should be considered. In this review, we highlight recent advances in the knowledge of the PI3K pathway and discuss the current state and future challenges of targeting this pathway in clinical practice

Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach

Diabetes mellitus is a global health problem, recognized as the seventh cause of death in the world. One of the most debilitating complications of diabetes mellitus is the diabetic foot (DF), resulting in an increased risk of hospitalization and significant morbidity and mortality. Amputation above or below the knee is a feared complication and the mortality in these patients is higher than for most forms of cancer. Identifying and interpreting relationships existing among the factors involved in DF diagnosis is still challenging. Although machine learning approaches have proven to achieve great accuracy in DF prediction, few advances have been performed in understanding how they make such predictions, resulting in mistrust of their use in real contexts. In this study, we present an approach based on Genetic Programming to build a simple global explainable classifier, named X-GPC, which, unlike existing tools such as LIME and SHAP, provides a global interpretation of the DFU diagnosis through a mathematical model. Also, an easy consultable 3d graph is provided, which could be used by the medical staff to figure out the patients’ situation and take decisions for patients’ healing. Experimental results obtained by using a real-world dataset have shown the ability of the proposal to diagnose DF with an accuracy of 100% outperforming other techniques of the state-of-the-art

A ROS-Based GNC Architecture for Autonomous Surface Vehicle Based on a New Multimission Management Paradigm

This paper presents the design and implementation of BAICal (Intelligent Autonomous Buoy by the University of Calabria), an autonomous surface vehicle (ASV) developed at the Autonomous Systems Lab (LASA) of the Department of Computer Science, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria. The basic project was born as a research program in marine robotics with multiple applications, either in the sea or in lake/river environments, for data monitoring, search and rescue operations and diver support tasks. Mechanical and hardware designs are discussed by considering a three-degree-of-freedom (3DoF) dynamical model of the vehicle. An extension to the typical guidance, navigation, and control (GNC) software architecture is presented. The software design and the implementation of a manager module (M-GNC architecture) that allows the vehicle to autonomously coordinate missions are described. Indeed, autonomous guidance and movement are only one of several more complex tasks that mobile robots have to perform in a real scenario and that allow a long-term life cycle. Module-based software architecture is developed by using the Robot Operating System (ROS) framework that is suitable for different kinds of autonomous vehicles, such as aerial, ground, surface or underwater drones

A machine learning evolutionary algorithm-based formula to assess tumor markers and predict lung cancer in cytologically negative pleural effusions

Malignant pleural effusion is diagnostically challenging in presence of negative cytology. The assessment of tumor markers in serum has become a standard tool in cancer diagnosis, while pleural fluid sampling has not met universal consensus. The evaluation of a panel of markers both in serum and pleural fluid may be crucial to improve the diagnostic accuracy. Using a machine learning-based approach, we provide a mathematical formula capable to express the complex relation existing among the expressed markers in serum and pleural effusion and the presence of lung cancer. The formula indicates CEA and CYFRA21-1 in pleural fluid as the best diagnostic markers, with 97% accuracy, 98% sensitivity, 95% specificity, 96% area under curve, 98% positive predictive value, and 92% MCC (Matthews correlation coefficient)