1,721,020 research outputs found
The implementation of middleware services for QoS-aware distributed multimedia applications
No full textIn this paper we address issues of design of middleware services that can meet effectively application-level (i.e., end-to-end) QoS requirements of Internet-based, latency-sensitive multimedia applications. In this context, we describe a set of adaptive middleware services that we have developed, based on a commercial middleware platform, in order to support an IP-based telephony application over the Internet; in addition, we summarize the performance results we have obtained from a prototype implementation of these services
On the Use of Heterogeneous Graph Neural Networks for Detecting Malicious Activities: a Case Study with Cryptocurrencies
Full text linkThis paper presents a study on the application of Heterogeneous Graph Neural Networks (HGNNs) for enhancing the security of complex social systems by identifying illicit and malicious behaviors. We focus on digital asset tokenization, a key component in the construction of many innovative social services, with the aim of classifying token exchanges and identifying illicit activities. Utilizing the Elliptic++ dataset, we demonstrate the efficacy of HGNNs in identifying illicit activities in token-based exchanging applications. In particular, we evaluate four different HGNN architectures, i.e. Heterogeneous GAT, Heterogeneous SAGE, HGT (Heterogeneous Graph Transformer), and HAN (Heterogeneous Attention Network). Our results underscore the importance of characterizing and describing interactions in these complex systems, both for studying the system dynamics and for activating mechanisms to cope with cybersecurity issues, like misuses and usurpation of resources in social systems
Simulative analysis of an adaptive control mechanism for packetized voice across the internet
No full textProposed new control mechanisms for the transmission of packetized voice across the Internet utilize the presence of silent intervals in conversational speech in order to dynamically adapt the behaviour of the audio application to the network fluctuating traffic conditions so as to minimize the effect of packet loss and varying delays on the quality of audio delivered to the destinations. An accurate model of the on-off characteristics of the conversational speech is thus necessary to analyze the performance of those audio communication systems. In this paper, an eight-state Markov model of voice activity in conversational speech has been used in order to assess the adequacy of an Internet audio mechanism that dynamically sets the playout delay value of packet audio in Internet voice-based connections. Based on this model, several simulation experiments have been carried out that show that a sufficient number of silence periods (of sufficiently long duration) occur in a typical human conversation that permit an adequate application of the proposed audio mechanism. In addition, a number of simulative/experimental trials are reported that show that the proposed Internet audio mechanism strikes a favourable balance between the average playout delay and the packet loss percentage experienced during audio conversations over the Internet
Client-centered load distribution: A mechanism for constructing responsive Web services
No full textIn this paper we describe the design, implementation and experimental evaluation of a software mechanism that supports responsive (i.e. highly available and timely) Web services, constructed out of replicated servers. Specifically, this mechanism operates by engaging all the available replicas in supplying a fragment of the Web document that a client requires. The size of the fragment a replica is requested to supply is dynamically evaluated on the basis of the response time that replica can provide its client with. In addition, the proposed mechanism can dynamically adapt to changes in both the network and the replica servers' status, thus tolerating possible replica or communication failures that may occur at run-time. The performance results we have obtained from our experimental evaluation illustrate the adequacy of the mechanism we propose
Fast Session Resumption in DTLS for Mobile Communications
Full text linkDTLS is a protocol that provides security guarantees to Internet communications. It can operate on top of both TCP and UDP transport protocols. Thus, it is particularly suited for peer-to-peer and distributed multimedia applications. The same holds if the endpoints are mobile devices. In this scenario, mechanisms are needed to surmount possible network disconnections, often arising due to the mobility or the scarce resources of devices, that can jeopardize the quality of the communications. Session resumption is thus a main issue to deal with. To this aim, we propose a fast reconnection scheme that employs non-connected sockets to quickly resume DTLS communication sessions. The proposed scheme is assessed in a performance evaluation that confirms its viability
Emergency Management in Smart Campus: Case Studies and Future Directions
Full text linkDisaster management plays a crucial role in ensuring the safety and well-being of individuals and infrastructure during emergencies. The rapid advancement of Internet of Things (IoT) technologies offers innovative solutions for disaster management in various domains, including smart campuses. A smart campus integrates IoT devices, sensors, and data analytics to create an intelligent environment that can efficiently respond to and mitigate the impact of disasters. In this paper, we describe some scenarios for the management of emergencies in the context of a Smart Campus. The Smart Campus under consideration has a people counting system able to detect and count the number of people in the classrooms and in the laboratories. Several emergencies are considered, including fires, earthquakes, and floods. In the end, we present the potentialities and the main limitations of such a system in the management of emergencies
Toward a Digital Twin: Combining Sensing, Machine Learning, and Data Visualization for the Effective Management of a Coastal Lagoon Environment
No full textCoastal lagoons serve as highly productive ecosystems, delivering essential ecosystem services contributing to human welfare and well-being. Unfortunately, these intricate systems are particularly vulnerable to climatic and anthropogenic pressures, such as intensive agriculture and extensive urbanization. This paper presents a Digital Twin prototype of the Mar Menor lagoon in Spain, that utilizes environmental data collected from sensors and predicted data generated through machine learning algorithms. It covers the prototype's architecture, the corresponding API layer designed for interoperability with other systems, and its data visualization dashboard aimed at enhancing decision-making processes
On Using an Emulative Middleware to Model Wireless Networks: Simulation Results and Validation
No full textDetecting Smart Contract Vulnerabilities using Transformers and LLMs
No full textThis study investigates the detection of vulnerabilities in smart contracts using various transformer models and Large Language Model (LLM) systems. We evaluated BERT, CodeBERT, DistilBERT, and the Gemini model, employing techniques such as aggregation of chunks to enhance performance. The results indicate that simple transformers applied to source code generally perform worse than when applied to byte-code. However, the use of aggregation techniques on the source code significantly improved the model performance. We also evaluate the use of meta-classifiers for multimodal data, by stacking multiple transformers working on source code and byte-code. The Random Forest meta-classifier achieved the highest performance but exhibited significant overfitting. The Gemini model demonstrates limited performance, highlighting the necessity of proper training for LLM systems
The University Learning Experience: A Dual Approach to Understanding Study Habits and Attendance Patterns
No full textThe pandemic has transformed the traditional perception of university life, revealing alternative learning methods and prompting a shift in students’ attitudes towards education. With emptied classrooms, students now navigate flexible schedules, recorded lectures, and increased autonomy in learning. This departure from traditional learning motivators, such as intellectual curiosity, is attributed to economic and social pressures. Distance learning has become integral, challenging the assumption of mandatory classroom attendance. Students, embracing a blend of online and in-person classes, prioritize value over physical presence. This paradigm shift presents universities with the dilemma of reengaging students while balancing tradition and innovation. To address this, understanding student perspectives is crucial. Through a comprehensive questionnaire involving 344 students across scientific and humanities disciplines, this study explores preferences, challenges, and expectations, aiming to inform future educational models
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