17,544 research outputs found
Secure data sharing and processing in heterogeneous clouds
The extensive cloud adoption among the European Public Sector Players empowered them to own and operate a range of cloud infrastructures. These deployments vary both in the size and capabilities, as well as in the range of employed technologies and processes. The public sector, however, lacks the necessary technology to enable effective, interoperable and secure integration of a multitude of its computing clouds and services. In this work we focus on the federation of private clouds and the approaches that enable secure data sharing and processing among the collaborating infrastructures and services of public entities. We investigate the aspects of access control, data and security policy languages, as well as cryptographic approaches that enable fine-grained security and data processing in semi-trusted environments. We identify the main challenges and frame the future work that serve as an enabler of interoperability among heterogeneous infrastructures and services. Our goal is to enable both security and legal conformance as well as to facilitate transparency, privacy and effectivity of private cloud federations for the public sector needs. © 2015 The Authors
Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques
Understanding the multifaceted effects of climate change across diverse
geographic locations is crucial for timely adaptation and the development of
effective mitigation strategies. As the volume of scientific literature on this
topic continues to grow exponentially, manually reviewing these documents has
become an immensely challenging task. Utilizing Natural Language Processing
(NLP) techniques to analyze this wealth of information presents an efficient
and scalable solution. By gathering extensive amounts of peer-reviewed articles
and studies, we can extract and process critical information about the effects
of climate change in specific regions. We employ BERT (Bidirectional Encoder
Representations from Transformers) for Named Entity Recognition (NER), which
enables us to efficiently identify specific geographies within the climate
literature. This, in turn, facilitates location-specific analyses. We conduct
region-specific climate trend analyses to pinpoint the predominant themes or
concerns related to climate change within a particular area, trace the temporal
progression of these identified issues, and evaluate their frequency, severity,
and potential development over time. These in-depth examinations of
location-specific climate data enable the creation of more customized
policy-making, adaptation, and mitigation strategies, addressing each region's
unique challenges and providing more effective solutions rooted in data-driven
insights. This approach, founded on a thorough exploration of scientific texts,
offers actionable insights to a wide range of stakeholders, from policymakers
to engineers to environmentalists. By proactively understanding these impacts,
societies are better positioned to prepare, allocate resources wisely, and
design tailored strategies to cope with future climate conditions, ensuring a
more resilient future for all
MetaGPT: Meta Programming for Multi-Agent Collaborative Framework
Recently, remarkable progress has been made in automated task-solving through
the use of multi-agents driven by large language models (LLMs). However,
existing works primarily focuses on simple tasks lacking exploration and
investigation in complicated tasks mainly due to the hallucination problem.
This kind of hallucination gets amplified infinitely as multiple intelligent
agents interact with each other, resulting in failures when tackling
complicated problems.Therefore, we introduce MetaGPT, an innovative framework
that infuses effective human workflows as a meta programming approach into
LLM-driven multi-agent collaboration. In particular, MetaGPT first encodes
Standardized Operating Procedures (SOPs) into prompts, fostering structured
coordination. And then, it further mandates modular outputs, bestowing agents
with domain expertise paralleling human professionals to validate outputs and
reduce compounded errors. In this way, MetaGPT leverages the assembly line work
model to assign diverse roles to various agents, thus establishing a framework
that can effectively and cohesively deconstruct complex multi-agent
collaborative problems. Our experiments conducted on collaborative software
engineering tasks illustrate MetaGPT's capability in producing comprehensive
solutions with higher coherence relative to existing conversational and
chat-based multi-agent systems. This underscores the potential of incorporating
human domain knowledge into multi-agents, thus opening up novel avenues for
grappling with intricate real-world challenges. The GitHub repository of this
project is made publicly available on: https://github.com/geekan/MetaGP
Understanding (Un)Intended Memorization in Text-to-Image Generative Models
Multimodal machine learning, especially text-to-image models like Stable
Diffusion and DALL-E 3, has gained significance for transforming text into
detailed images.
Despite their growing use and remarkable generative capabilities, there is a
pressing need for a detailed examination of these models' behavior,
particularly with respect to memorization. Historically, memorization in
machine learning has been context-dependent, with diverse definitions emerging
from classification tasks to complex models like Large Language Models (LLMs)
and Diffusion models. Yet, a definitive concept of memorization that aligns
with the intricacies of text-to-image synthesis remains elusive. This
understanding is vital as memorization poses privacy risks yet is essential for
meeting user expectations, especially when generating representations of
underrepresented entities. In this paper, we introduce a specialized definition
of memorization tailored to text-to-image models, categorizing it into three
distinct types according to user expectations. We closely examine the subtle
distinctions between intended and unintended memorization, emphasizing the
importance of balancing user privacy with the generative quality of the model
outputs. Using the Stable Diffusion model, we offer examples to validate our
memorization definitions and clarify their application
TSGBench: Time Series Generation Benchmark
Synthetic Time Series Generation (TSG) is crucial in a range of applications,
including data augmentation, anomaly detection, and privacy preservation.
Although significant strides have been made in this field, existing methods
exhibit three key limitations: (1) They often benchmark against similar model
types, constraining a holistic view of performance capabilities. (2) The use of
specialized synthetic and private datasets introduces biases and hampers
generalizability. (3) Ambiguous evaluation measures, often tied to custom
networks or downstream tasks, hinder consistent and fair comparison.
To overcome these limitations, we introduce \textsf{TSGBench}, the inaugural
Time Series Generation Benchmark, designed for a unified and comprehensive
assessment of TSG methods. It comprises three modules: (1) a curated collection
of publicly available, real-world datasets tailored for TSG, together with a
standardized preprocessing pipeline; (2) a comprehensive evaluation measures
suite including vanilla measures, new distance-based assessments, and
visualization tools; (3) a pioneering generalization test rooted in Domain
Adaptation (DA), compatible with all methods. We have conducted comprehensive
experiments using \textsf{TSGBench} across a spectrum of ten real-world
datasets from diverse domains, utilizing ten advanced TSG methods and twelve
evaluation measures. The results highlight the reliability and efficacy of
\textsf{TSGBench} in evaluating TSG methods. Crucially, \textsf{TSGBench}
delivers a statistical analysis of the performance rankings of these methods,
illuminating their varying performance across different datasets and measures
and offering nuanced insights into the effectiveness of each method.Comment: Accepted and to appear in VLDB 202
AI Hallucinations: A Misnomer Worth Clarifying
As large language models continue to advance in Artificial Intelligence (AI),
text generation systems have been shown to suffer from a problematic phenomenon
termed often as "hallucination." However, with AI's increasing presence across
various domains including medicine, concerns have arisen regarding the use of
the term itself. In this study, we conducted a systematic review to identify
papers defining "AI hallucination" across fourteen databases. We present and
analyze definitions obtained across all databases, categorize them based on
their applications, and extract key points within each category. Our results
highlight a lack of consistency in how the term is used, but also help identify
several alternative terms in the literature. We discuss implications of these
and call for a more unified effort to bring consistency to an important
contemporary AI issue that can affect multiple domains significantly
Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency -- A Review
The rise of advanced technology in project management (PM) highlights a
crucial need for inclusiveness. This work examines the enhancement of both
inclusivity and efficiency in PM through technological integration, focusing on
defining and measuring inclusiveness. This approach illuminates how
inclusivity-centered technology can significantly elevate project outcomes. The
research navigates through the challenges of achieving inclusivity, mainly
biases in learning databases and the design process of these technologies,
assessment of transformative potential of these technologies, particularly in
automating tasks like data collection and analysis, thus enabling managers to
prioritize human-centric aspects of projects. However, the integration of such
technology transcends efficiency, indicating a paradigm shift in understanding
their societal roles. This shift necessitates a new approach in the development
of these systems to prevent perpetuating social inequalities. We proposed a
methodology involving criteria development for evaluating the inclusiveness and
effectiveness of these technologies. This methodical approach is vital to
comprehensively address the challenges and limitations inherent in these
systems. Emphasizing the importance of inclusivity, the study advocates for a
balance between technological advancement and ethical considerations, calling
for a holistic understanding and regulation. In conclusion, the paper
underscores that while these technologies can significantly improve outcomes,
their mindful integration, ensuring inclusivity, is paramount. This exploration
into the ethical and practical aspects of technology in PM contributes to a
more informed and balanced approach within the field
Recommended from our members
RUN-TIME ANALYSIS AND SECURITY OF MULTI-LANGUAGE SYSTEMS
The contemporary software development landscape has witnessed a widespread integration of diverse programming languages, leveraging the specific advantages of each, such as the efficiency of C and the programmability of Python. This trend finds notable applications in prominent domains, including the Android operating system and advanced machine learning frameworks like PyTorch. However, adopting this multi-language approach has ushered in aseries of great challenges for developers, necessitating the identification of robust solutions to tackle potential security vulnerabilities.Traditional techniques such as program analysis and fuzzing, initially designed for single-language software, face limitations in effectively uncovering vulnerabilities in multi-language systems. Program analysis grapples with challenges in comprehending the intricate control and data flows across diverse languages, often resulting in incomplete vulnerability detection. Conversely, greybox fuzzing encounters difficulties adapting to the nuances of various languages, leading to incomplete code coverage and complications in reproducing identified vulnerabilities. The intricacies within runtime systems supporting multilingual software exacerbate the security clearance predicament, as these systems are often constructed using multiple languages. This complexity adds an additional layer of difficulty for conventional security techniques, emphasizing the need for more adaptive and comprehensive approachestailored to the unique challenges posed by the multifaceted nature of multi-language systems.Within the scope of my dissertation, I endeavored to tackle the intricate challenges posed by vulnerabilities in multi-language software through a comprehensive and multifaceted approach. This approach entailed conducting extensive empirical investigations into vulnerability susceptibility, facilitating the development of dynamic cross-language information flow analysis. Recognizing the pivotal significance of comprehensive test input coverage, I devisedan integrated greybox fuzzing methodology. This innovative approach integrates sensitivity analysis and comprehensive whole-system coverage measurements, significantly enhancing the efficiency of the fuzzing process and vulnerability identification. Furthermore, I focused on fortifying runtime security by proposing a novel two-level collaborative fuzzing framework tailored explicitly for Python language runtime. This contribution was pivotal in reinforcing the software system’s foundational safeguards, ensuring a robust defense mechanism against potential security threats
- …