37 research outputs found
Multilevel User Credibility Assessment in Social Networks
Online social networks are one of the largest platforms for disseminating
both real and fake news. Many users on these networks, intentionally or
unintentionally, spread harmful content, fake news, and rumors in fields such
as politics and business. As a result, numerous studies have been conducted in
recent years to assess the credibility of users. A shortcoming of most of
existing methods is that they assess users by placing them in one of two
categories, real or fake. However, in real-world applications it is usually
more desirable to consider several levels of user credibility. Another
shortcoming is that existing approaches only use a portion of important
features, which downgrades their performance. In this paper, due to the lack of
an appropriate dataset for multilevel user credibility assessment, first we
design a method to collect data suitable to assess credibility at multiple
levels. Then, we develop the MultiCred model that places users at one of
several levels of credibility, based on a rich and diverse set of features
extracted from users' profile, tweets and comments. MultiCred exploits deep
language models to analyze textual data and deep neural models to process
non-textual features. Our extensive experiments reveal that MultiCred
considerably outperforms existing approaches, in terms of several accuracy
measures
SAgric-IoT: an IoT-based platform and deep learning for greenhouse monitoring
The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%
Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML
The main cause of fatal accidents in the construction sector are falls from height (FFH)
and the inappropriate use of a harness is commonly associated with these fatalities. Traditional
methods, such as onsite inspections, safety communication, or safety training, are not enough to
mitigate accidents caused by FFH associated with a poor management in the use of a harness.
Although some technological solutions for the automated monitoring of workers could improve
safety conditions, their use is not frequent due to the particularities of construction sites: complexity,
dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with
technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many
potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the
current research, the development and test of an IoT system integrated with the Java Fuzzy Markup
Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal
was checked in four construction scenarios based on working conditions with different levels of risk
of FFH and obtained promising results.Universidad de Malaga
Plan Propio-Universidad de MalagaSpanish GovernmentEuropean Commission RTI2018-098371-B-I0
Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML
The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results
A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities
Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed
FaaSdom: A Benchmark Suite for Serverless Computing
Serverless computing has become a major trend among cloud providers. With
serverless computing, developers fully delegate the task of managing the
servers, dynamically allocating the required resources, as well as handling
availability and fault-tolerance matters to the cloud provider. In doing so,
developers can solely focus on the application logic of their software, which
is then deployed and completely managed in the cloud. Despite its increasing
popularity, not much is known regarding the actual system performance
achievable on the currently available serverless platforms. Specifically, it is
cumbersome to benchmark such systems in a language- or runtime-independent
manner. Instead, one must resort to a full application deployment, to later
take informed decisions on the most convenient solution along several
dimensions, including performance and economic costs. FaaSdom is a modular
architecture and proof-of-concept implementation of a benchmark suite for
serverless computing platforms. It currently supports the current mainstream
serverless cloud providers (i.e., AWS, Azure, Google, IBM), a large set of
benchmark tests and a variety of implementation languages. The suite fully
automatizes the deployment, execution and clean-up of such tests, providing
insights (including historical) on the performance observed by serverless
applications. FaaSdom also integrates a model to estimate budget costs for
deployments across the supported providers. FaaSdom is open-source and
available at https://github.com/bschitter/benchmark-suite-serverless-computing.Comment: ACM DEBS'2
Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented
Retrieve Anything To Augment Large Language Models
Large language models (LLMs) face significant challenges stemming from their
inherent limitations in knowledge, memory, alignment, and action. These
challenges cannot be addressed by LLMs alone, but should rely on assistance
from the external world, such as knowledge base, memory store, demonstration
examples, and tools. Retrieval augmentation stands as a vital mechanism for
bridging the gap between LLMs and the external assistance. However,
conventional methods encounter two pressing issues. On the one hand, the
general-purpose retrievers are not properly optimized for the retrieval
augmentation of LLMs. On the other hand, the task-specific retrievers lack the
required versatility, hindering their performance across the diverse retrieval
augmentation scenarios.
In this work, we present a novel approach, the LLM-Embedder, which
comprehensively supports the diverse retrieval augmentation needs of LLMs with
one unified embedding model. Training such a unified model is non-trivial, as
various retrieval tasks aim to capture distinct semantic relationships, often
subject to mutual interference. To address this challenge, we systematically
optimize our training methodology. This includes reward formulation based on
LLMs' feedback, the stabilization of knowledge distillation, multi-task
fine-tuning with explicit instructions, and homogeneous in-batch negative
sampling. These optimization strategies contribute to the outstanding empirical
performance of the LLM-Embedder. Notably, it yields remarkable enhancements in
retrieval augmentation for LLMs, surpassing both general-purpose and
task-specific retrievers in various evaluation scenarios. Our checkpoint and
source code are publicly available at
https://github.com/FlagOpen/FlagEmbedding