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José Alberto Hernández

Publications and source records attributed to José Alberto Hernández.

28 records · Page 2Linked to original sources
arXiv2023

How many words does ChatGPT know? The answer is ChatWords

The introduction of ChatGPT has put Artificial Intelligence (AI) Natural Language Processing (NLP) in the spotlight. ChatGPT adoption has been exponential with millions of users experimenting with it in a myriad of tasks and application domains with impressive results. However, ChatGPT has limitations and suffers hallucinations, for example producing answers that look plausible but they are completely wrong. Evaluating the performance of ChatGPT and similar AI tools is a complex issue that is being explored from different perspectives. In this work, we contribute to those efforts with ChatWords, an automated test system, to evaluate ChatGPT knowledge of an arbitrary set of words. ChatWords is designed to be extensible, easy to use, and adaptable to evaluate also other NLP AI tools. ChatWords is publicly available and its main goal is to facilitate research on the lexical knowledge of AI tools. The benefits of ChatWords are illustrated with two case studies: evaluating the knowledge that ChatGPT has of the Spanish lexicon (taken from the official dictionary of the "Real Academia Española") and of the words that appear in the Quixote, the well-known novel written by Miguel de Cervantes. The results show that ChatGPT is only able to recognize approximately 80% of the words in the dictionary and 90% of the words in the Quixote, in some cases with an incorrect meaning. The implications of the lexical knowledge of NLP AI tools and potential applications of ChatWords are also discussed providing directions for further work on the study of the lexical knowledge of AI tools.

cs.CL↗
arXiv2023

Launch Power Optimization for Dynamic Elastic Optical Networks over C+L Bands

We propose an algorithm for calculating the optimum launch power over the entire C+L bands by maximizing the cumulative link GSNR of a channel plan built upon multiple modulation formats, with application to dynamic EONs. Exact last-fit spectrum assignment proves to outperform exact first-fit in terms of average GSNR at arrival time.

eess.SP↗
arXiv2023

Round Trip Time (RTT) Delay in the Internet: Analysis and Trends

Both capacity and latency are crucial performance metrics for the optimal operation of most networking services and applications, from online gaming to futuristic holographic-type communications. Networks worldwide have witnessed important breakthroughs in terms of capacity, including fibre introduction everywhere, new radio technologies and faster core networks. However, the impact of these capacity upgrades on end-to-end delay is not straightforward as traffic has also grown exponentially. This article overviews the current status of end-to-end latency on different regions and continents worldwide and how far these are from the theoretical minimum baseline, given by the speed of light propagation over an optical fibre. We observe that the trend in the last decade goes toward latency reduction (in spite of the ever-increasing annual traffic growth), but still there are important differences between countries.

cs.NI↗
arXiv2023

Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet

The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already contributing to the data that is available to train future models. Therefore, future versions of generative AI tools will be trained with a mix of human-created and AI-generated content, causing a potential feedback loop between generative AI and public data repositories. This interaction raises many questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve and improve with the new data sets or on the contrary will they degrade? Will evolution introduce biases or reduce diversity in subsequent generations of generative AI tools? What are the societal implications of the possible degradation of these models? Can we mitigate the effects of this feedback loop? In this document, we explore the effect of this interaction and report some initial results using simple diffusion models trained with various image datasets. Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.

cs.AI↗
arXiv2023

Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?

In the span of a few months, generative Artificial Intelligence (AI) tools that can generate realistic images or text have taken the Internet by storm, making them one of the technologies with fastest adoption ever. Some of these generative AI tools such as DALL-E, MidJourney, or ChatGPT have gained wide public notoriety. Interestingly, these tools are possible because of the massive amount of data (text and images) available on the Internet. The tools are trained on massive data sets that are scraped from Internet sites. And now, these generative AI tools are creating massive amounts of new data that are being fed into the Internet. Therefore, future versions of generative AI tools will be trained with Internet data that is a mix of original and AI-generated data. As time goes on, a mixture of original data and data generated by different versions of AI tools will populate the Internet. This raises a few intriguing questions: how will future versions of generative AI tools behave when trained on a mixture of real and AI generated data? Will they evolve with the new data sets or degenerate? Will evolution introduce biases in subsequent generations of generative AI tools? In this document, we explore these questions and report some very initial simulation results using a simple image-generation AI tool. These results suggest that the quality of the generated images degrades as more AI-generated data is used for training thus suggesting that generative AI may degenerate. Although these results are preliminary and cannot be generalised without further study, they serve to illustrate the potential issues of the interaction between generative AI and the Internet.

cs.CV↗
arXiv2018

Meeting the traffic requirements of residential users in the next decade with current FTTH standards: how much? how long?

Traffic demand in the access has grown in the last years, and service providers need to upgrade their infrastructure to the latest access standards. While fiber has become the preferred technology of choice in access networks, there are many fibre access technologies available in the market. This poses a challenging question to operators not always easy to answer: how to upgrade? what technology and for how long it will cope with the demands? In this paper we model the traffic forecast in the access for the next decade and analyze possible upgrade scenarios of fibre access networks, concluding which of the NG-PON flavors could better fit the demand.

cs.NI↗
arXiv2018

Design and Analysis of 5G Scenarios with 'simmer': An R Package for Fast DES Prototyping

Simulation frameworks are important tools for the analysis and design of communication networks and protocols, but they can result extremely costly and/or complex (for the case of very specialized tools), or too naive and lacking proper features and support (for the case of ad-hoc tools). In this paper, we present an analysis of three 5G scenarios using 'simmer', a recent R package for discrete-event simulation that sits between the above two paradigms. As our results show, it provides a simple yet very powerful syntax, supporting the efficient simulation of relatively complex scenarios at a low implementation cost.

cs.NI↗
arXiv2017

Android Malware Characterization using Metadata and Machine Learning Techniques

Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.

cs.CR↗
arXiv2017

On labeling Android malware signatures using minhashing and further classification with Structural Equation Models

Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after analyzing 82 thousand different Android malware applications. We identify 41 different malware classes grouped into three major categories, namely Adware, Harmful Threats and Unknown or Generic signatures. We further investigate the relationships between such 41 classes using community detection algorithms from graph theory to identify similarities between them; and we finally propose a Structure Equation Model to identify which Anti-Virus engines are more powerful at detecting each macro-category. As an application, we show how such models can help in identifying whether Unknown malware applications are more likely to be of Harmful or Adware type.

cs.CR↗