392 research outputs found

    A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

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    Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology

    Incremental Offline/Online PIR (extended version)

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    Recent private information retrieval (PIR) schemes preprocess the database with a query-independent offline phase in order to achieve sublinear computation during a query-specific online phase. These offline/online protocols expand the set of applications that can profitably use PIR, but they make a critical assumption: that the database is immutable. In the presence of changes such as additions, deletions, or updates, existing schemes must preprocess the database from scratch, wasting prior effort. To address this, we introduce incremental preprocessing for offline/online PIR schemes, allowing the original preprocessing to continue to be used after database changes, while incurring an update cost proportional to the number of changes rather than the size of the database. We adapt two offline/online PIR schemes to use incremental preprocessing and show how it significantly improves the throughput and reduces the latency of applications where the database changes over time

    Autoencoder-based Image Recommendation for Lung Cancer Characterization

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    Neste projeto, temos como objetivo desenvolver um sistema de IA que recomende um conjunto de casos relativos (passados) para orientar a tomada de decisão do médico. Objetivo: A ambição é desenvolver um modelo de aprendizado baseado em IA para caracterização de câncer de pulmão, a fim de auxiliar na rotina clínica. Considerando a complexidade dos fenômenos biológicos que ocorrem durante o desenvolvimento do câncer, as relações entre eles e as manifestações visuais capturadas pela tomografia computadorizada (CT) têm sido exploradas nos últimos anos. No entanto, devido à falta de robustez dos métodos atuais de aprendizado profundo, essas correlações são frequentemente consideradas espúrias e se perdem quando confrontadas com dados coletados a partir de distribuições alteradas: diferentes instituições, características demográficas ou até mesmo estágios de desenvolvimento do câncer.In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician. Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development

    Unbalanced Circuit-PSI from Oblivious Key-Value Retrieval

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    Circuit-based Private Set Intersection (circuit-PSI) enables two parties, a client and a server, with their input sets XX and YY respectively, to securely compute a function ff on the intersection XYX \cap Y, while keeping XYX \cap Y secret from both parties. Although several computationally efficient circuit-PSI protocols have been proposed recently, they most focus on the balanced scenario where X|X| is similar to Y|Y|. However, in many realistic scenarios, a circuit-PSI protocol may be performed in the unbalanced case where X|X| is remarkably smaller than Y|Y| (e.g., the client is a constrained device holding a small set, while the server is a service provider holding a large set). Directly applying existing protocols to this scenario will lead to significant efficiency issues because the communication complexity of the protocols scales at least linearly with the size of the larger set, i.e., max(X,Y)\max(|X|, |Y|). In this work, we put forth efficient constructions for unbalanced circuit-PSI with sublinear communication complexity in the size of the larger set. The main insight is that we formalize unbalanced circuit-PSI as obliviously retrieving values corresponding to keys from a set of key-value pairs. To this end, we present a new functionality called Oblivious Key-Value Retrieval (OKVR) and design the OKVR protocol from a new notion called sparse Oblivious Key-Value Stores (sparse OKVS). We conduct extensive experiments and the results show that our constructions remarkably outperform the state-of-the-art circuit-PSI schemes (EUROCRYPT\u2719, PETs\u2722, CCS\u2722), i.e., 1.8448.86×1.84 \sim 48.86 \times communication improvement and 1.5039.81×1.50 \sim39.81 \times faster computation. Very recently, Son and Jeong (AsiaCCS\u2723) also present unbalanced circuit-PSI protocols, and our constructions outperform them by 1.1815.99×1.18 \sim 15.99 \times and 1.2210.44×1.22 \sim 10.44 \times in communication and computation overhead, respectively, depending on set sizes and network environments

    Graphic Design in the Age of Artificial Intelligence : A Speculative Co-design Investigation into the Possibilities and Challenges of Artificial Intelligence on the Field of Graphic Design in Saudi Arabia

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    The potential impact of artificial intelligence on graphic design has, in recent years, stimulated a range of questions and concerns from design practitioners and academics about the future of AI-driven designs. This impact has prompted researchers, academics and practitioners alike to rethink the new implication of AI on the role of the graphic designer in this progression. It has also led to consideration of a plethora of issues and challenges around academia and practice, addressing questions associated with the definition of creativity, cultural acceptance, and ethical issues, besides possibilities that AI can imply in having autonomous AI-driven designs. In this research, I investigate the impact of AI from the graphic designers' perspective measuring the impact on their roles as designers in the design process, including an assessment of how to use AI as a self-governed system to generate visual designs autonomously rather than having AI as an application tool. This investigation will propose a new literature of theory and practice into the process of designing, particularly exploring and speculating upon new opportunities associated with combining data and algorithms with graphic design, in practice as well as in education. Co-design activities and semi-structured interviews were used in this research to initiate speculative provocative discussions and debates. In addition, this thesis presents the ADI card toolkit, a speculative design toolkit designed to help initiate collaboration in brainstorming and generate solutions to these challenges by using the gameplay approach. The ADI card toolkit was tested as part of conducting the research in Saudi Arabia, a country undergoing a transformation in terms of employing the 4th industrial revolution of technology and innovation towards building their infrastructure, economy, and quality of life under the government’s Vision 2030. The findings suggest many actions to consider when using AI in education and practice one of which is equipping graphic designers with knowledge, skills and qualifications to be furtherly open and aware of the broad spectrum of AI potentials which allows for proactive collaboration as a self-driven system with graphic designers. The research also suggests using gameplay as an approach when introducing AI tools in academia which can aid in exploring opportunities to alternate the human–machine entanglements and enable designer and academics alike to explore self-generated designs and alternative futures in this field

    A regional solar forecasting approach using generative adversarial networks with solar irradiance maps

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    The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region

    Lower Bounds for (Batch) PIR with Private Preprocessing

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    In this paper, we study (batch) private information retrieval with private preprocessing. Private information retrieval (PIR) is the problem where one or more servers hold a database of nn bits and a client wishes to retrieve the ii-th bit in the database from the server(s). In PIR with private preprocessing (also known as offline-online PIR), the client is able to compute a private rr-bit hint in an offline stage that may be leveraged to perform retrievals accessing at most tt entries. For privacy, the client wishes to hide index ii from an adversary that has compromised some of the servers. In the batch PIR setting, the client performs queries to retrieve the contents of multiple entries simultaneously. We present a tight characterization for the trade-offs between hint size rr and number of accessed entries tt during queries. For any PIR scheme that enables clients to perform batch retrievals of kk entries, we prove a lower bound of tr=Ω(nk)tr = \Omega(nk) when rkr \ge k. When r<kr < k, we prove that t=Ω(n)t = \Omega(n). Our lower bounds hold when the scheme errs with probability at most 1/151/15 and against PPT adversaries that only compromise one out of \ell servers for any =O(1)\ell = O(1). Our work also closes the multiplicative logarithmic gap for the single query setting (k=1)(k = 1) as our lower bound matches known constructions. Our lower bounds hold in the model where each database entry is stored without modification but each entry may be replicated arbitrarily. Finally, we show connections between PIR and the online matrix-vector (OMV) conjecture from fine-grained complexity. We present barriers for proving lower bounds for two-server PIR schemes in general computational models as they would immediately imply the OMV conjecture

    Assessment of Smart Mechatronics Applications in Agriculture: A Review

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    Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues
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