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Future Internet
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Embracing Artificial Intelligence (AI) for Network and Service
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Embracing Artificial Intelligence (AI) for Network and Service

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 7111

Special Issue Editors

Prof. Dr. Emiliano Tramontana

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Guest Editor
Department of Mathematics and Computer Science (DMI), University of Catania, 95038 Catania, Italy
Interests: software engineering; refactoring; blockchain; design patterns; program analysis
Prof. Dr. Hironori Washizaki

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku City, Tokyo 169-8555, Japan
Interests: smart systems and software engineering for business and society; education technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A big shift is occurring before our eyes thanks to the recent advancements in Artificial Intelligence (AI) and the power of generative models. Such models could have a significant impact on how we develop software services and in turn on the ability of software services themselves.

Several decades of research had previously reported that the development speed of a large software system is approximately constant, due to the limited gain achieved by adding more developers, known as the Self-Regulation Law or Lehman's Third Law of Software Evolution. Additionally, Brook's Law states that adding manpower to a late software project makes it later, as new members have to be trained therefore slowing down the existing team. The Self-Regulation Law expresses that large and complex software systems have a limited growth over time, and the ability to improve their behavior is also limited.

Nowadays, differently than what was done before, a team of developers can employ AI generative models to assist the analysis and changes in a large software system, mitigating the implications of the Self-Regulation Law. Software evolution could then be performed at a quicker rate and with more automation than ever before. As a result, the development of new versions of software services could quickly gain pace and accelerate the production of advanced services.

For more complex services to emerge, aside from their development, a further issue to address is the configuration management of the several constituting components that are often distributed across a network of hosts. The configuration, monitoring, and repair of software components, platforms, and services tend to be time-consuming given their ad hoc nature due to the great amount of possible combinations available. AI could be employed to finally achieve a greater level of autonomic computing, by analyzing a large amount of data related to configurations and monitoring.

Future advanced software systems could comprise a set of services that improve human life by analyzing data gathered from the monitoring of health and the natural environment, and then give insights and adjust eating habits, the use of natural resources, the state of a city infrastructure, pollution trends, climate changes, etc.

Dr. Emiliano Tramontana
Prof. Dr. Hironori Washizaki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-powered software development tools and environments
  • AI support for analyzing big data for health, natural resources, pollution, etc.
  • AI and autonomic computing
  • AI-supported network management
  • AI-enabled virtualization environments
  • AI and blockchain-based systems
  • AI management of cloud services

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Published Papers (4 papers)

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Research

17 pages, 1604 KiB  
Article
Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers
by Daniela Gifu and Covaci Silviu-Vasile
Future Internet 2025, 17(1), 38; https://doi.org/10.3390/fi17010038 - 16 Jan 2025
Viewed by 1186
Abstract
This paper presents a comprehensive study on detecting AI-generated text using transformer models. Our research extends the existing RODICA dataset to create the Enhanced RODICA for Human-Authored and AI-Generated Text (ERH) dataset. We enriched RODICA by incorporating machine-generated texts from various large language [...] Read more.
This paper presents a comprehensive study on detecting AI-generated text using transformer models. Our research extends the existing RODICA dataset to create the Enhanced RODICA for Human-Authored and AI-Generated Text (ERH) dataset. We enriched RODICA by incorporating machine-generated texts from various large language models (LLMs), ensuring a diverse and representative corpus. Methodologically, we fine-tuned several transformer architectures, including BERT, RoBERTa, and DistilBERT, on this dataset to distinguish between human-written and AI-generated text. Our experiments examined both monolingual and multilingual settings, evaluating the model’s performance across diverse datasets such as M4, AICrowd, Indonesian Hoax News Detection, TURNBACKHOAX, and ERH. The results demonstrate that RoBERTa-large achieved superior accuracy and F-scores of around 83%, particularly in monolingual contexts, while DistilBERT-multilingual-cased excelled in multilingual scenarios, achieving accuracy and F-scores of around 72%. This study contributes a refined dataset and provides insights into model performance, highlighting the transformative potential of transformer models in detecting AI-generated content. Full article
(This article belongs to the Special Issue Embracing Artificial Intelligence (AI) for Network and Service)
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24 pages, 2069 KiB  
Article
Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
by Fadi Mohsen, Bedir Chaushi, Hamed Abdelhaq, Dimka Karastoyanova and Kevin Wang
Future Internet 2024, 16(10), 352; https://doi.org/10.3390/fi16100352 - 27 Sep 2024
Cited by 1 | Viewed by 1891
Abstract
The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news. This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various [...] Read more.
The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news. This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various methodologies. We evaluate existing benchmarks, introduce a novel hybrid word embedding model, and implement a web framework for text classification. Our approach integrates traditional frequency–inverse document frequency (TF–IDF) methods with sophisticated feature extraction techniques, considering linguistic, psychological, morphological, and grammatical aspects of the text. Through a series of experiments on diverse datasets, applying transfer and incremental learning techniques, we demonstrate the effectiveness of our hybrid model in surpassing benchmarks and outperforming alternative experimental setups. Furthermore, our findings emphasize the importance of dataset alignment and balance in transfer learning, as well as the utility of incremental learning in maintaining high detection performance while reducing runtime. This research offers promising avenues for further advancements in fake news detection methodologies, with implications for future research and development in this critical domain. Full article
(This article belongs to the Special Issue Embracing Artificial Intelligence (AI) for Network and Service)
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16 pages, 1963 KiB  
Article
Cross-Domain Fake News Detection Using a Prompt-Based Approach
by Jawaher Alghamdi, Yuqing Lin and Suhuai Luo
Future Internet 2024, 16(8), 286; https://doi.org/10.3390/fi16080286 - 8 Aug 2024
Cited by 1 | Viewed by 2070
Abstract
The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing interest in strategies for detecting cross-domain misinformation. However, traditional machine [...] Read more.
The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing interest in strategies for detecting cross-domain misinformation. However, traditional machine learning (ML) approaches often struggle with the nuanced contextual understanding required for accurate news classification. To address these challenges, we propose a novel contextualized cross-domain prompt-based zero-shot approach utilizing a pre-trained Generative Pre-trained Transformer (GPT) model for fake news detection (FND). In contrast to conventional fine-tuning methods reliant on extensive labeled datasets, our approach places particular emphasis on refining prompt integration and classification logic within the model’s framework. This refinement enhances the model’s ability to accurately classify fake news across diverse domains. Additionally, the adaptability of our approach allows for customization across diverse tasks by modifying prompt placeholders. Our research significantly advances zero-shot learning by demonstrating the efficacy of prompt-based methodologies in text classification, particularly in scenarios with limited training data. Through extensive experimentation, we illustrate that our method effectively captures domain-specific features and generalizes well to other domains, surpassing existing models in terms of performance. These findings contribute significantly to the ongoing efforts to combat fake news dissemination, particularly in environments with severely limited training data, such as online platforms. Full article
(This article belongs to the Special Issue Embracing Artificial Intelligence (AI) for Network and Service)
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14 pages, 6422 KiB  
Article
Discovery of Cloud Applications from Logs
by Ashot Harutyunyan, Arnak Poghosyan, Tigran Bunarjyan, Andranik Haroyan, Marine Harutyunyan, Lilit Harutyunyan and Nelson Baloian
Future Internet 2024, 16(6), 216; https://doi.org/10.3390/fi16060216 - 18 Jun 2024
Cited by 1 | Viewed by 1101
Abstract
Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches [...] Read more.
Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data–network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate not only diverse kinds of applications, but also their specific deployments, thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in cloud management scenarios. Full article
(This article belongs to the Special Issue Embracing Artificial Intelligence (AI) for Network and Service)
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