Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence

Recent developments in Large Language Models (LLMs) have significantly expanded their applications across various domains. However, the effectiveness of LLMs is often constrained when operating individually in complex environments. This paper introduces a transformative approach by organizing LLMs into community-based structures, aimed at enhancing their collective intelligence and problem-solving capabilities. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. Within these structured communities, LLMs are designed to specialize in distinct cognitive tasks, employ advanced interaction mechanisms such as direct communication, voting systems, and market-based approaches, and dynamically adjust their governance structures to meet changing demands. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI, prompting an in-depth examination of their ethical considerations, management strategies, and scalability potential. This position paper seeks to lay the groundwork for future research, advocating a paradigm shift from isolated to synergistic operational frameworks in AI research and application

SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding

In the evolving landscape of artificial intelligence, multimodal and Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on the identification and interaction with entities and their relations across diverse modalities. Addressing the need for complex querying and interaction in this context, we introduce SNeL (Structured Neuro-symbolic Language), a versatile query language designed to facilitate nuanced interactions with neural networks processing multimodal data. SNeL's expressive interface enables the construction of intricate queries, supporting logical and arithmetic operators, comparators, nesting, and more. This allows users to target specific entities, specify their properties, and limit results, thereby efficiently extracting information from a scene. By aligning high-level symbolic reasoning with low-level neural processing, SNeL effectively bridges the Neuro-Symbolic divide. The language's versatility extends to a variety of data types, including images, audio, and text, making it a powerful tool for multimodal scene understanding. Our evaluations demonstrate SNeL's potential to reshape the way we interact with complex neural networks, underscoring its efficacy in driving targeted information extraction and facilitating a deeper understanding of the rich semantics encapsulated in multimodal AI models

Performance evaluation of a compression algorithm for wireless sensor networks in monitoring applications

Wireless sensor network (WSN) is an emerging technology that targets multiple applications in the different environments. Its infrastructure is composed of a large number of sensor nodes with a limited physical capacity and low cost. The energy consumption must be as optimized as possible in order to extend its lifetime. The use of data compression techniques can be an advantage in the WSN context, once these techniques eliminate transmission of redundant information and consequently can be adopted to minimize the consumption of energy in the sensor nodes. WSN for monitoring applications can benefit from this technique as it may maximize the lifetime of batteries. The main motivation of this paper is to investigate the performance of a data compression algorithm for WSN in the context of monitoring applications. To validate the proposal, simulation experiments have been performed using the Network Simulator (NS-2) tool

Performance evaluation of a vehicular edge device for customer feedback in Industry 4.0

Industry 4.0 is the term used to specify the current industrial revolution, not only from a technological point of view but also from economical, sociological and strategical points of view. The revolution involves several traditional economic sectors, as is the case with the industrial ecosystem. The main benefits are related to creating value during the entire product lifecycle and in terms of customer feedback, which is particularly relevant to the automotive industry. Its disruptive diffusion is due to various enabling technologies, such as the Internet of Things (IoT), and, as such, it is a vision rather than a technological step forward. Thus, this paper investigates a performance evaluation of an Edge OBD-II device, which collects data from vehicles in an autonomous way in order to provide customer feedback and tracking. The metrics evaluated were different sets of OBD-II Parameter IDs (PIDS), responsiveness, driver behaviour and CO2 pollution estimates. The experiments were performed using three vehicles in urban and highway areas in the city of Natal, Brazil. For validation purposes, the results obtained from the vehicles were compared with an OBD-II Emulator, which demonstrated the accuracy of the experiments.</p

Data set for automatic detection of online misogynistic speech

The data set is composed of 2285 definitions posted on the Urban Dictionary platform from 1999 to May 2016. The data was classified as misogynistic and non-misogynistic by three independent researchers with domain knowledge. The data set is available in public repository in a table containing two columns: the text-based definition from Urban Dictionary and its respective classification (1 for misogynistic and 0 for non-misogynistic)

Analysing dependability and performance of a real-world Elastic Search application

—Increased complexity in IT, big data, and advanced analytical techniques are some of the trends driving demand for more sophisticated and scalable search technology. Despite Quality of Service (QoS) being a critical success factor in most enterprise software service offerings, it is often not a generic component of the enterprise search software stack. In this paper, we explore enterprise search engine dependability and performance using a real-world company architecture and associated data sourced from an ElasticSearch implementation on Linknovate.com. We propose a Fault Tree model to assess the availability and reliability of the Linknovate.com architecture. The results of the Fault Tree model are fed into a Stochastic Petri Net (SPN) model to analyze how failures and redundancy impact application performance of the use case system. Availability and MTTF were used to evaluate the reliability and throughput was used to evaluate the performance of the target system. The best results for all three metrics were returned in scenarios with high levels of redundancy

A comparison of machine learning approaches for detecting misogynistic Speech in urban dictionary

—Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined. Dublin, Ireland [email protected] it was announced that the UK Law Commission would review whether misogynistic conduct should be treated as a hate crime [6]. Index Terms—misogyny, hate speech, recurrent neural networks, deep learning, LSTM, machine learning, urban dictionar

Reliability and Availability Evaluation of Wireless Sensor Networks for Industrial Applications

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements

TAC: A Python package for IoT-focused Tiny Anomaly Compression

The Tiny Anomaly Compression (TAC), a vital component of the Python package Conect2AI, is engineered for real-time data compression in Internet of Things (IoT) devices. TAC is an innovative data compression algorithm that leverages the concept of data eccentricity, operating without the need for pre-established mathematical models or assumptions about the underlying data distribution. Furthermore, it utilizes recursive equations, enabling efficient computation with low computational overhead, thereby minimizing memory usage and processing power requirements. This approach renders TAC particularly suitable for resource-constrained environments such as IoT devices, offering an effective and optimized solution for data compression in large volumes and continuous data scenarios