nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Cross lingual Representation Learning for Speech Recognition
Venue: Interspeech 2021
Year: 2021

This paper presents XLSR which learns cross lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on a concurrently introduced self supervised model which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine tuned on labeled data and experiments show that cross lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to the strongest comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages.
"I don't understand it either, but it is cool" visitor interactions with a multi touch table in a museum
Venue: 2008 3rd IEEE International Workshop on Horizontal Interactive Human Computer Systems
Year: 2008

Most tabletop research presents findings from lab based user studies, focusing on specific interaction techniques. This means we still know little about how these new interfaces perform in real life settings and how users appropriate them. This paper presents findings from a field study of an existing interactive table in a museum of natural history. Visitors were found to employ a wide variety of gestures for interacting; different interface elements invited different types of gesture. The analysis highlights challenges and design conflicts in the design of tabletop interfaces for public settings, such as latency times and side effects of dasiaframe lesspsila content, which had some users struggling to learn how to interact. While the majority of visitors engaged at least briefly with the table, which enabled browsing question answer text about animal species, talk amongst visitors dealt mainly with how to interact and evoked few comments, indicating shallow engagement with content.
Personal protective equipment preparedness in intensive care units during the coronavirus disease 2019 pandemic: An Asia Pacific follow up survey.
Venue: Australian critical care official journal of the Confederation of Australian Critical Care Nurses
Year: 2021

Personal protective equipment (PPE) preparedness, defined as adherence to guidelines, healthcare worker (HCW) training, procuring PPE stocks and responding appropriately to suspected cases, is crucial to prevent HCW infections.  To perform a follow up survey to assess changes in PPE preparedness across six Asia Pacific countries during the COVID 19 pandemic.  A prospective follow up cross sectional, web based survey was conducted between 10/08/2020 to 01/09/ 2020, five months after the initial Phase 1 survey. The survey was sent to the same 231 intensivists across the six Asia Pacific countries (Australia, Hong Kong, India, New Zealand, Philippines, and Singapore) that participated in Phase 1. The main outcome measure was to identify any changes in PPE preparedness between Phases 1 and 2.  Phase 2 had responses from 132 ICUs (57% Compared to Phase 1 respondents reported increased use of PPE based practices such as powered air purifying respirator (40.2% vs. 6.1% N95 masks at all times (86.4% vs. 53.7% and double gloving (87.9% vs. 42.9% The reported awareness of PPE stocks (85.6% vs. 51.9% mandatory showering policies following PPE breach (31.1% vs. 6.9% and safety perception amongst HCWs (60.6% vs. 28.4% improved significantly during Phase 2. Despite reported statistically similar adoption rate of the buddy system in both phases (42.4% vs. 37.2% there was a reported reduction in donning/doffing training in Phase 2 (44.3% vs. 60.2% There were no reported differences HCW training in other areas, such as tracheal intubation, intra hospital transport and safe waste disposal, between the 2 phases.  Overall reported PPE preparedness improved between the two survey periods, particularly in PPE use, PPE inventory and HCW perceptions of safety. However, the uptake of HCW training and implementation of low cost safety measures continued to be low and the awareness of PPE breach management policies were suboptimal. Therefore, the key areas for improvement should focus on regular HCW training, implementing low cost buddy system and increasing awareness of PPE breach management protocols.
All nanocellulose nonvolatile resistive memory
Year: 2016

Single use disposable nonvolatile memory devices hold promise for novel applications in internet of everything (IoE) technology by storing the health status of individual humans in daily life. However, conventional memory devices are not disposable because they are mostly composed of non renewable, non biodegradable and sometimes toxic materials, causing serious damage to ecological systems when they are released to the environment. Here, we demonstrate an environment friendly, disposable nonvolatile memory device composed of 99.3 vol. nanocellulose. Our memory device consists of a nanocellulose based resistive switching layer and a nanopaper substrate. The device exhibited nonvolatile resistive switching with the capability of multilevel storage and potential scalability down to the single nanofiber level (ca. 15 nm) The biodegradability of our memory device was confirmed by burying it in natural soil for 26 days. Environmentally friendly memory cells that use 'nanopaper' substrates instead of silicon have been produced by an international team. The rising use of networked devices in consumer packaging and biomedical implants has created a need for recyclable electronic materials. By manipulating nanoscale cellulose fibres into extremely flat, paper like substrates, Takeshi Yanagida from Kyushu University in Japan and co workers have produced a nonvolatile memory device that can be composted. The team's technique first turned wood chips into nanopaper through a spraying and casting routine. Then, coating opposite sides of the nanopaper with silver and indium tin oxide electrodes produced a see through, resistive switching device capable of multilevel persistent memory storage. Following water based treatments to remove the electrodes, the nanopaper substrates biodegraded completely after being buried in soil for 26 days. Conventional memory devices are not disposable as they are mostly composed of non renewable, non biodegradable and some toxic materials, causing a serious damage for ecological system when they are emitted to the environment. Here we demonstrate an environment friendly, disposable nonvolatile memory device composed of 99.3 vol. nanocellulose. Our device consists of a nanocellulose based resistive switching layer and nanopaper substrate. The device exhibited the nonvolatile resistive switching with the capability of multilevel storage and the potential scalability down to single nanofiber level. Furthermore, the biodegradability of our memory device was confirmed by burying it into the natural soil for 26 days.
Efficient ConvNet based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing
Venue: ICDSC
Year: 2018

Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on board camera. Recent advances in deep learning have made it possible to use single shot Convolutional Neural Network (CNN) detection algorithms that process the input image to detect various objects of interest. To keep the computational demands low these neural networks typically operate on small image sizes which, however, makes it difficult to detect small objects. This is further emphasized when considering UAVs equipped with cameras where due to the viewing range, objects tend to appear relatively small. This paper therefore, explores the trade offs involved when maintaining the resolution of the objects of interest by extracting smaller patches (tiles) from the larger input image and processing them using a neural network. Specifically, we introduce an attention mechanism to focus on detecting objects only in some of the tiles and a memory mechanism to keep track of information for tiles that are not processed. Through the analysis of different methods and experiments we show that by carefully selecting which tiles to process we can considerably improve the detection accuracy while maintaining comparable performance to CNNs that resize and process a single image which makes the proposed approach suitable for UAV applications.
Clinical and angiographic follow up of spontaneous isolated superior mesenteric artery dissection.
Venue: European journal of vascular and endovascular surgery the official journal of the European Society for Vascular Surgery
Year: 2009

To observe the clinical features and angiographic findings in patients with a spontaneous isolated superior mesenteric artery dissection (SISMAD) and to identify any correlation between them.  From a single institution, 32 patients (22 symptomatic patients at presentation; mean age 54years; men 97% with SISMAD were retrospectively reviewed. All patients were available for clinical follow up after treatment (conservative, n=28, 88% open or endovascular superior mesenteric artery (SMA) reconstruction, n=4, 12% and follow up CT scans were available in 28 patients (mean 22months, range 1 80months)  We found a positive correlation between pain severity and dissection length (p=0.03, rho=0.50, Spearman's partial correlation analysis) After conservative treatment, only one patient (3% required bowel resection, and there was no difference in outcome between patients who were treated with anticoagulation or anti platelet therapy and those who were not (p=1.00, Fisher's exact test) No patients had progression of their lesion on the follow up CT angiography.  In SISMAD patients, dissection length is positively associated with more severe clinical symptoms. After conservative treatment, we observed a benign clinical course and no CT progression of the dissection, even without anticoagulation or anti platelet therapy. Based on our observation, patients with SISMAD can be treated conservatively without anticoagulation therapy.
Selection of a sustainable third party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA
Venue: Omega
Year: 2019

Pressure from legislation and customers has motivated companies to consider reverse logistics (RL) in their operations. Since it is a complex procedure that requires an adequate system, the recent trend consists in outsourcing RL to third party reverse logistics providers (3PRLPs) This paper provides the background of sustainable triple bottom line theory with focus on economic, environmental, and social aspects under 3PRL concerns. The relevant sustainability criteria are used in a case study conducted in cooperation with an Indian automotive remanufacturing company. To select the most preferred service provider, we use a hybrid method combining a variant of ELECTRE I accounting for the effect of reinforced preference, the revised Simos procedure, and Stochastic Multi criteria Acceptability Analysis. The incorporated approach exploits all parameters of an outranking model compatible with the incomplete preference information of the Decision Maker. In particular, it derives the newly defined kernel acceptability and membership indices that can be interpreted as a support given to the selection of either a particular subset of alternatives or a single option. The proposed ELECTRE based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection. As indicated by the extensive review presented in the paper, this application field was so far dominated by Analytic Hierarchy Process and TOPSIS, whose weaknesses can be overcome by applying the outranking methods.
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
Year: 2018

Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al. 2016; Luong et al. 2016) However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domains for which a reliable parser is not available. In this paper, we introduce an unsupervised tree to sequence (tree2seq) model for neural machine translation; this model is able to induce an unsupervised hierarchical structure on the source sentence based on the downstream task of neural machine translation. We adapt the Gumbel tree LSTM of Choi et al. (2018) to NMT in order to create the encoder. We evaluate our model against sequential and supervised parsing baselines on three low and medium resource language pairs. For low resource cases, the unsupervised tree2seq encoder significantly outperforms the baselines; no improvements are seen for medium resource translation.
Easier Said than Done: Writing an Autoethnography
Year: 2008

Autoethnography is an intriguing and promising qualitative method that offers a way of giving voice to personal experience for the purpose of extending sociological understanding. The author's experience of writing an autoethnography about international adoption has shown her, however, that autoethnography can be a very difficult undertaking. In writing her autoethnography, she confronted anxiety producing questions pertaining to representation, balance, and ethics. As well, she dealt with the acceptability of her autoethnography by informal and formal reviewers. In this article she discusses the challenges she faced in her autoethnographic project to inform future autoethnographers and to inspire them to share their experiences and reflections. For the author questions linger, but she hopes that sharing issues that arise in autoethnographic work will strengthen our understandings of this challenging yet highly promising form of inquiry.
Efficient Privacy Preserving Face Recognition Full Version
Year: 2009

Automatic recognition of human faces is becoming increasingly popular in civilian and law enforcement applications that require reliable recognition of humans. However, the rapid improvement and widespread deployment of this technology raises strong concerns regarding the violation of individuals' privacy. A typical application scenario for privacy preserving face recognition concerns a client who privately searches for a specific face image in the face image database of a server. In this paper we present a privacy preserving face recognition scheme that substantially improves over previous work in terms of communicationand computation efficiency: the most recent proposal of Erkin et al. (PETS'09) requires O(log M) rounds and computationally expensive operations on homomorphically encrypted data to recognize a face in a database of M faces. Our improved scheme requires only O(1) rounds and has a substantially smaller online communication complexity (by a factor of 15 for each database entry) and less computation complexity. Our solution is based on known cryptographic building blocks combining homomorphic encryption with garbled circuits. Our implementation results show the practicality of our scheme also for large databases (e.g. for M 1000 we need less than 13 seconds and less than 4 MByte online communication on two 2.4GHz PCs connected via Gigabit Ethernet)
Multi objective optimization of an automotive body component with fiber reinforced composites
Year: 2018

In this study, the design optimization process of a vehicle front body structure made of short fiber composites is proposed. The aim of the optimization process is to reduce the weight in consideration of quality and production characteristics. Injection molding of short fiber reinforced composites leads to anisotropic material properties which depend on the dimensions of the structure, a finding which is taken into account in this research. First, a basic design is derived through topology optimization that assumes linear isotropic material properties. Second, anisotropic properties are derived by an injection molding analysis for each design point and are applied to a structural analysis to consider the anisotropic properties according to the dimensions of the structure. Third, based on the analysis results, a surrogate model is created and multi objective optimization is conducted. The Pareto region, inversely correlated with the lightweight effect, the quality and the production characteristics, is identified. The optimal solution based on the design goal is derived using the goal programing method.
Producing permanence: employment, domesticity and the flexible future on a South African border farm
Year: 2013

What does it mean to be 'permanent' in an increasingly flexible world of work? On the Zimbabwean South African border, white farmers guard against risk by investing in portfolios of estates and emphasizing their mobility. But the farms rely on core black workforces of resident 'general workers' known as mapermanent. The lives of mapermanent embody temporal contradictions in South African agriculture. Work regimes depend on arrangements established through long term residence in labour compounds, a stability threatened by employers' pragmatism in a volatile sector. Here, short term 'permanence' coexists with longer term insecurity. Moreover, what I call provisional permanence is built on others' transience: mapermanent draw on the domestic labour of temporary contract workers and the order enforcement of rotating border garrisons. Tensions between temporalities characterize workers' assertions of 'permanence' and their limitations, in an economy of flexibility and shifting investments.
Big Data in Natural Disaster Management: A Review
Year: 2018

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.
Sticker: A 0.41 62.1 TOPS/W 8Bit Neural Network Processor with Multi Sparsity Compatible Convolution Arrays and Online Tuning Acceleration for Fully Connected Layers
Venue: 2018 IEEE Symposium on VLSI Circuits
Year: 2018

Neural Networks (NNs) have emerged as a fundamental technology for machine learning. The sparsity of weight and activation in NNs varies widely from 5% 90% and can potentially lower computation requirements. However, existing designs lack a universal solution to efficiently handle different sparsity in various layers and neural networks. This work, named STICKER, first systematically explores NN sparsity for inference and online tuning operations. Its major contributions are: 1) autonomous NN sparsity detector that switches the processor modes; 2) Multi sparsity compatible Convolution (CONV) PE arrays that contain a multi mode memory supporting different sparsity, and the set associative PEs supporting both dense and sparse operations and reducing 92% memory area compared with previous hash memory banks; 3) Online tuning PE for sparse FCs that achieves 32.5x speedup compared with conventional CPU, using quantization center based weight updating and Compressed Sparse Column (CSC) based back propagations. Peak energy efficiency of the 65nm STICKER chip is up to 62.1 TOPS/W at 8bit data length.
Case Studies, Trade War Impacts Between USA And China The Decline Of Cotton Carded Yarn 32/1 Selling Volume To China On Dan Liris Incorporation
Year: 2020

This study aims to describe the prospect of the trend of export sales volume of 32/1 cotton carded yarn in PT Dan Liris. In 2018, the volume of exports of 32/1 cotton carded yarn to China dropped dramatically to zero orders. In addition, research on the decline in the sales volume of exports of 32/1 cotton carded yarn to China aims to find out deeply the flow of the company's external factors, namely trade war which caused a decline in the volume of export sales to China at PT Dan Liris. The method used in this study is a mixed method by providing an overview of the problems that occur in the PT Dan Liris company. The analytical tool in this study is the trend analysis technique by giving an overview of the trend in volume and the prospect of possible future export volumes of yarn products. The results of the analysis of the data obtained are the volume of export sales of PT Dan Liris yarn to China that are upside down or unstable. This study also succeeded in revealing that the existence of external factors in the trade war that occurred between the United States and China was very detrimental to the company in terms of income and continuity of production. Based on the results of the analysis of the data, the authors then conducted a SWOT analysis matrix as the company's alternative. The author suggests 12 new strategies that can be done by PT Dan Liris such as PT Dan Liris can take action by utilizing promotional services either online or offline, conducting research and analyzing the development of buyer countries related to economic issues on a regular basis, as well as the likely impact on the company. A survey of customer satisfaction and attention to employee welfare is also very important to maintain the integrity of the company.  Sales Volume, Trade War, Company Integrity.
A 0.5 nJ/Pixel 4 K H.265/HEVC Codec LSI for Multi Format Smartphone Applications
Venue: IEEE Journal of Solid State Circuits
Year: 2016

A 4 K x 2 K H.265/HEVC video codec chip supporting 14 video standard formats is implemented on a 1.49 x 1.45 mm 2 die in a 28 nm CMOS process. Several HEVC fast algorithms reduce the coding modes by analyzing the content features leading to over 68.5% and 83% of complexity reduction in intra and inter coding, respectively. A fully parallel processing element (PE) array is adopted in SAO and IP/ME, which reduce number of accesses to SRAM by 48.7% and 78.4% respectively. A shared memory management unit (MMU) including line store SRAM pool (LSSP) and data bus translation (DBT) techniques efficiently reuses and packs the neighboring pixels which contribute 71.6% of external bandwidth reduction. This chip achieves 4096 x 2160@30 fps HEVC encoding/decoding and consumes 126.73 mW, 0.5 nJ/pixel of energy efficiency, under 494 MHz and 350 MHz of clock frequency, enabling 4 K video services for smart phone applications.