title: Feature extractor stacking for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. We propose feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

text: Feature extractor stacking for cross-domain few-shot learning Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. We propose feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

year: 2023-11-30

title: Secure FPGA as a Service - Towards Secure Data Processing by Physicalizing the Cloud

authors: Will, Mark Antony

subjects: Science & Technology

abstract: Securely processing data in the cloud is still a difficult problem, even with homomorphic encryption and other privacy preserving schemes. Hardware solutions provide additional layers of security and greater performance over their software alternatives. However by definition the cloud should be flexible and adaptive, often viewed as abstracting services from products. By creating services reliant on custom hardware, the core essence of the cloud is lost. FPGAs bridge this gap between software and hardware with programmable logic, allowing the cloud to remain abstract. FPGA as a Service (FaaS) has been proposed for a greener cloud, but not for secure data processing. This paper explores the possibility of Secure FaaS in the cloud for privacy preserving data processing, describes the technologies required, identifies use cases, and highlights potential challenges.

text: Secure FPGA as a Service - Towards Secure Data Processing by Physicalizing the Cloud Securely processing data in the cloud is still a difficult problem, even with homomorphic encryption and other privacy preserving schemes. Hardware solutions provide additional layers of security and greater performance over their software alternatives. However by definition the cloud should be flexible and adaptive, often viewed as abstracting services from products. By creating services reliant on custom hardware, the core essence of the cloud is lost. FPGAs bridge this gap between software and hardware with programmable logic, allowing the cloud to remain abstract. FPGA as a Service (FaaS) has been proposed for a greener cloud, but not for secure data processing. This paper explores the possibility of Secure FaaS in the cloud for privacy preserving data processing, describes the technologies required, identifies use cases, and highlights potential challenges.

year: 2017

title: Te Kuranui

authors: Maxwell, Te Kahautu

abstract: Ko te tūāpapa o tēnei kōrero Te Kuranui he titiro ki te tūāpapa o Ōpōtiki Mai Tawhiti kapa haka. He aha te take i whakatūngia ai tēnei Kuranui e karangatia ana ko Ōpōtiki Mai Tawhiti? Hai tāhūhū mō tēnei tuhinga raurūtia tēnei kupu whakaari a Te Kōoti Arikirangi; “He Kura hoki ahau”, me tēnei wehenga o te mōteatea nāna; Te Pono ki runga rā, te ara mai o te tika. Tukua mai ki raro nā te maungarongo; te whana i tū ai nā te kotahitanga; te aroha i mau ai. Ana ko te Kupu Whakaari nei; He Kura hoki ahau he wewete i tēnei Kupu Whakaari kia kite i te hōhonu o te tikanga ōna hai whakatamārama i tō Te Kōoti wawata hai kura i tana iwi Ringatū me tā te kaituhi whakaraupapa i ēnei kura ki roto i te whakairotanga i te Kuranui nei.
Ka rua kua tātaritia tēnei wehenga o te mōteatea nei kia rima ōna wāhanga hai taki i tēnei tuhinga epā ana ki te Kuranui nei a Ōpōtiki Mai Tawhiti. Tuatahi Te Pono ki runga rā, ka titiro ki te kaituhi nei me tōna whakatipuranga i ōna kaumātua me ō rātou kuranga i a ia kia tohenihorau ki tōna ao Māori. He kuranga ēnei hai maungāngākau, maungāhinengaro ki te whakaturuki i te Kuranui a Ōpōtiki Mai Tawhiti. Te ara mai o te tika, ko te whakatūtanga o te kapa haka nei a Ōpōtiki Mai Tawhiti te Kuranui,arā te whakapaparanga mai i te kapa i tōna tīmatatanga ki te wā tonu nei. Tukua mai ki raro nā te maungarongo; ko te whakapono tēnei wāhanga, ko te ū o Ōpōtiki Mai Tawhiti ki tēnei kura a Te Kooti Arikirangi. Te whana i tū ai nā te kotahitanga;ko te titiro ki te pouārahi me te whakapau kaha ki te tuitui i ngā whakaaro o te katoa, ko te ranga i te iwi, ko te pupuri i te iwi, i te kapa haka ki tōna, rautaki, whāinga ko te whakakotahitanga i te iwi i raro i te kaupapa o Ōpōtiki Mai Tawhiti. Te aroha i mau ai, ko te aroha ki te iwi, ko te aroha ki te kaupapa, ko te whakaū, whakapūmau i te moemoeā kia whakahokia mai te toheariki ki te iwi kia tū toherauariki a Te Whakatōhea i raro i te korowai o Ōpōtiki Mai Tawhiti.

text: Te Kuranui Ko te tūāpapa o tēnei kōrero Te Kuranui he titiro ki te tūāpapa o Ōpōtiki Mai Tawhiti kapa haka. He aha te take i whakatūngia ai tēnei Kuranui e karangatia ana ko Ōpōtiki Mai Tawhiti? Hai tāhūhū mō tēnei tuhinga raurūtia tēnei kupu whakaari a Te Kōoti Arikirangi; “He Kura hoki ahau”, me tēnei wehenga o te mōteatea nāna; Te Pono ki runga rā, te ara mai o te tika. Tukua mai ki raro nā te maungarongo; te whana i tū ai nā te kotahitanga; te aroha i mau ai. Ana ko te Kupu Whakaari nei; He Kura hoki ahau he wewete i tēnei Kupu Whakaari kia kite i te hōhonu o te tikanga ōna hai whakatamārama i tō Te Kōoti wawata hai kura i tana iwi Ringatū me tā te kaituhi whakaraupapa i ēnei kura ki roto i te whakairotanga i te Kuranui nei.
Ka rua kua tātaritia tēnei wehenga o te mōteatea nei kia rima ōna wāhanga hai taki i tēnei tuhinga epā ana ki te Kuranui nei a Ōpōtiki Mai Tawhiti. Tuatahi Te Pono ki runga rā, ka titiro ki te kaituhi nei me tōna whakatipuranga i ōna kaumātua me ō rātou kuranga i a ia kia tohenihorau ki tōna ao Māori. He kuranga ēnei hai maungāngākau, maungāhinengaro ki te whakaturuki i te Kuranui a Ōpōtiki Mai Tawhiti. Te ara mai o te tika, ko te whakatūtanga o te kapa haka nei a Ōpōtiki Mai Tawhiti te Kuranui,arā te whakapaparanga mai i te kapa i tōna tīmatatanga ki te wā tonu nei. Tukua mai ki raro nā te maungarongo; ko te whakapono tēnei wāhanga, ko te ū o Ōpōtiki Mai Tawhiti ki tēnei kura a Te Kooti Arikirangi. Te whana i tū ai nā te kotahitanga;ko te titiro ki te pouārahi me te whakapau kaha ki te tuitui i ngā whakaaro o te katoa, ko te ranga i te iwi, ko te pupuri i te iwi, i te kapa haka ki tōna, rautaki, whāinga ko te whakakotahitanga i te iwi i raro i te kaupapa o Ōpōtiki Mai Tawhiti. Te aroha i mau ai, ko te aroha ki te iwi, ko te aroha ki te kaupapa, ko te whakaū, whakapūmau i te moemoeā kia whakahokia mai te toheariki ki te iwi kia tū toherauariki a Te Whakatōhea i raro i te korowai o Ōpōtiki Mai Tawhiti.

year: 2014-09-27