Reviewerly/exHarmony huggingface dataset:
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains. This minimization can be achieved via a domain classifier to detect target-domain features that are divergent from source-domain features. However, when optimizing via such domain-classification discrepancy, ambiguous target samples that are not smoothly distributed on the low-dimensional data manifold are often missed. To solve this issue, we propose a novel Contrastively Smoothed Class Alignment (CoSCA) model, that explicitly incorporates both intra- and inter-class domain discrepancy to better align ambiguous target samples with the source domain. CoSCA estimates the underlying label hypothesis of target samples, and simultaneously adapts their feature representations by optimizing a proposed contrastive loss. In addition, Maximum Mean Discrepancy (MMD) is utilized to directly match features between source and target samples for better global alignment. Experiments on several benchmark datasets demonstrate that CoSCAoutperforms state-of-the-art approaches for unsupervised domain adaptation by producing more discriminative features.
Atomic scale displacements detected by optical image cross-correlation analysis and 3D printed marker arrays

Abstract For analyzing displacement-vector fields in mechanics, for example to characterize the properties of 3D printed mechanical metamaterials, routine high-precision position measurements are indispensable. For this purpose, nanometer-scale localization errors have been achieved by wide-field optical-image cross-correlation analysis. Here, we bring this approach to atomic-scale accuracy by combining it with well-defined 3D printed marker arrays. By using an air-lens with a numerical aperture of $$0.4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.4</mml:mn></mml:mrow></mml:math> and a free working distance of $$11.2\, \mathrm{mm}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>11.2</mml:mn><mml:mspace /><mml:mi>mm</mml:mi></mml:mrow></mml:math> , and an $$8\times 8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>8</mml:mn><mml:mo>×</mml:mo><mml:mn>8</mml:mn></mml:mrow></mml:math> array of markers with a diameter of $$2\, \upmu\mathrm{m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mspace /><mml:mi>μ</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:math> and a period of $$5\,\upmu \mathrm{ m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>5</mml:mn><mml:mspace /><mml:mi>μ</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:math> , we obtain 2D localization errors as small as $$0.9\, \AA$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.9</mml:mn><mml:mspace /><mml:mi>Å</mml:mi></mml:mrow></mml:math> in $$12.5\, \mathrm{ms}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>12.5</mml:mn><mml:mspace /><mml:mi>ms</mml:mi></mml:mrow></mml:math> measurement time ( $$80\, \mathrm{frames}/\mathrm{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>80</mml:mn><mml:mspace /><mml:mi>frames</mml:mi><mml:mo>/</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:math> ). The underlying experimental setup is simple, reliable, and inexpensive, and the marker arrays can easily be integrated onto and into complex architectures during their 3D printing process.
A Mixture Density Channel Model for Deep Learning-Based Wireless Physical Layer Design

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but it usually requires that a channel model is known. As data rates increase and wireless transceivers become more complex, the wireless channel, hardware imperfections, and their interactions become more difficult to model and compensate explicitly. New machine learning schemes for the physical layer do not require an explicit model but implicitly learn the end-to-end link including channel characteristics and non-linearities of the system directly from the training data.
A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning

We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and $$\ell ^2$$ -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to $$\ell ^2$$ -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.
Developing an information classification method

Purpose The purpose of this paper is to develop a method for information classification. The proposed method draws on established standards, such as the ISO/IEC 27002 and information classification practices. The long-term goal of the method is to decrease the subjective judgement in the implementation of information classification in organisations, which can lead to information security breaches because the information is under- or over-classified. Design/methodology/approach The results are based on a design science research approach, implemented as five iterations spanning the years 2013 to 2019. Findings The paper presents a method for information classification and the design principles underpinning the method. The empirical demonstration shows that senior and novice information security managers perceive the method as a useful tool for classifying information assets in an organisation. Research limitations/implications Existing research has, to a limited extent, provided extensive advice on how to approach information classification in organisations systematically. The method presented in this paper can act as a starting point for further research in this area, aiming at decreasing subjectivity in the information classification process. Additional research is needed to fully validate the proposed method for information classification and its potential to reduce the subjective judgement. Practical implications The research contributes to practice by offering a method for information classification. It provides a hands-on-tool for how to implement an information classification process. Besides, this research proves that it is possible to devise a method to support information classification. This is important, because, even if an organisation chooses not to adopt the proposed method, the very fact that this method has proved useful should encourage any similar endeavour. Originality/value The proposed method offers a detailed and well-elaborated tool for information classification. The method is generic and adaptable, depending on organisational needs.
Fake news detector in the medical domain by reasoning with description logics

We investigate how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g. Depression is not an illness). To automatically convert into DLs, we used the FRED converter. Reasoning in DLs is then performed with the Racer and Hermit reasoners to detect inconsistencies. Each inconsistency signals to a possible medical myth. The detected inconsistencies are verbalised to the human agent, aiming to explain why a sentence is considered a medical myth. Our fake news detector is exemplified on in the psychocognitive and epidemiological fields.
An Interaction System Using Speech and Gesture Based on CNN

Nowadays, Hand gestures playing a important role for human interactions with the computer. Deep Learning is a part of machine learning methods which makes the recognition process easier by using Convolution Neural Networks (ConvNet/CNN). Convolution Neural Networks is a multilayer process network which includes Input layer, Convolution layer, Max pooling layer, Fully connected layer, Output layer. When compared to other algorithms, CNN can give more accurate results. CNN is mainly used to analyze visual images and for the image processing, segmentation and classification with higher accuracy. Here, this model consists of two main systems. One is voice input is converted into text and hand gestures and second approach is hand gestures conversion to text. These two systems are mainly used for abnormal people. These systems are implemented in Python and OpenCV is used to capture images. Each of these two systems has different modules. Human Computer Interaction are main source for the communication between humans and computer. So, these systems are helpful in communicating some information to humans. These systems are free from lighting conditions and background noise by using CNN algorithm.
White-Box Implementation of ECDSA Based on the Cloud Plus Side Mode

White-box attack context assumes that the running environments of algorithms are visible and modifiable. Algorithms that can resist the white-box attack context are called white-box cryptography. The elliptic curve digital signature algorithm (ECDSA) is one of the most widely used digital signature algorithms which can provide integrity, authenticity, and nonrepudiation. Since the private key in the classical ECDSA is plaintext, it is easy for attackers to obtain the private key. To increase the security of the private key under the white-box attack context, this article presents an algorithm for the white-box implementation of ECDSA. It uses the lookup table technology and the “cloud plus side” mode to protect the private key. The residue number system (RNS) theory is used to reduce the size of storage. Moreover, the article analyzes the security of the proposed algorithm against an exhaustive search attack, a random number attack, a code lifting attack, and so on. The efficiency of the proposed scheme is compared with that of the classical ECDSA through experiments.
A Generic Visualization Approach for Convolutional Neural Networks

Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate attention visualization as a constrained optimization problem. We leverage the unit L2-Norm constraint as an attention filter (L2-CAF) to localize attention in both classification and retrieval networks. Unlike recent literature, our approach requires neither architectural changes nor fine-tuning. Thus, a pre-trained network's performance is never undermined. L2-CAF is quantitatively evaluated using weakly supervised object localization. State-of-the-art results are achieved on classification networks. For retrieval networks, significant improvement margins are achieved over a Grad-CAM baseline. Qualitative evaluation demonstrates how the L2-CAF visualizes attention per frame for a recurrent retrieval network. Further ablation studies highlight the computational cost of our approach and compare L2-CAF with other feasible alternatives. Code available at https://bit.ly/3iDBLFv .
TransPhrase: A new method for generating phrase embedding from word embedding in Chinese

Currently, there are two main methods of learning phrase embedding: the distribution method and the composition method. The distribution method treats a phrase as an entirety and learns phrase embedding based on the context of the phrase. Its disadvantage is that it completely ignores the semantics of the component words of the phrase and the data sparseness problem. The composition method calculates the phrase embedding from the embedding of component words. The existing composition methods fail to represent the semantics of phrases well. Because of the above problems, we take Chinese, for example, and propose a new composition method to generate phrase embedding from the component word embedding, named TransPhrase. It is a neural network that can use LSTM to learn the order information of component words, use the attention mechanism to learn the important information of component words, and use a fully connected network to learn the semantic information of component words, and finally predict phrase embedding. It can solve the data sparseness problem and properly and fully represent the semantics of phrases. Our evaluation of three Chinese phrase-level semantic tasks shows that the comprehensive performance of TransPhrase's phrase representation is better than the composition method, the distribution method, and the pre-trained language model.
Removal of manually induced artifacts in ultrasound images of thyroid nodules based on edge-connection and Criminisi image restoration algorithm

There are various artificial markers in ultrasound images of thyroid nodules, which have impact on subsequent processing and computer-aided diagnosis. The purpose of this study was to develop an approach to automatically remove artifacts and restore ultrasound images of thyroid nodules. Fifty ultrasound images with manually induced artifacts were selected from publicly available and self-collected datasets. A combined approach was developed which consisted of two steps, artifacts detection and removal of the detected artifacts. Specifically, a novel edge-connection algorithm was used for artifact detection, detection accuracy and false discovery rate were used to evaluate the performance of artifact detection approaches. Criminisi algorithm was used for image restoration with peak signal-to-noise ratio (PSNR) and mean gradient difference to evaluate its performance. In addition, computation complexity was evaluated by execution time of relevant algorithms. Results revealed that the proposed joint approach with edge-connection and Criminisi algorithm could achieve automatic artifacts removal. Mean detection accuracy and mean false discovery rate of the proposed edge-connection algorithm for the 50 ultrasound images were 0.86 and 1.50. Mean PSNR of the 50 restored images by Criminisi algorithm was 36.64 dB, and mean gradient difference of the restored images was -0.002 compared with the original images. The proposed combined approach had a good detection accuracy for different types of manually induced artifacts, and could significantly improve PSNR of the ultrasound images. The proposed combined approach may have potential use for the repair of ultrasound images with artifacts.
SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation

SegBlocks reduces the computational cost of existing neural networks, by dynamically adjusting the processing resolution of image regions based on their complexity. Our method splits an image into blocks and downsamples blocks of low complexity, reducing the number of operations and memory consumption. A lightweight policy network, selecting the complex regions, is trained using reinforcement learning. In addition, we introduce several modules implemented in CUDA to process images in blocks. Most important, our novel BlockPad module prevents the feature discontinuities at block borders of which existing methods suffer, while keeping memory consumption under control. Our experiments on Cityscapes, Camvid and Mapillary Vistas datasets for semantic segmentation show that dynamically processing images offers a better accuracy versus complexity trade-off compared to static baselines of similar complexity. For instance, our method reduces the number of floating-point operations of SwiftNet-RN18 by 60% and increases the inference speed by 50%, with only 0.3% decrease in mIoU accuracy on Cityscapes.
HYDRA: Decentralized Location-Aware Orchestration of Containerized Applications

The edge computing paradigm, spurred by the Internet-of-Things, poses new requirements and challenges for distributed application deployment. There is a need for an orchestrator design that leverages characteristics that enable this new paradigm. We present HYDRA, a decentralized and distributed orchestrator for containerized microservice applications. This orchestrator focuses on scalability and resiliency to enable the global manageability of cloud and edge environments. It can manage heterogeneous resources across geographical locations and provide robust application control. Further, HYDRA enables the location-aware deployment of microservice applications via containerization. Thus, an application's services may be deployed to separate locations according to expected needs. In this article, the experiments show the orchestrator scaling to 20000 nodes and simultaneously deploying 30000 applications. Further, empirical results show that location-aware application deployment does not hinder HYDRA's performance, and the random resource search algorithm currently being employed may be used as a baseline to find resources in this decentralized orchestrator. Therefore, we conclude that HYDRA is a viable orchestrator design for the new computing paradigm.
Big Data and learning analytics in higher education: Legal and ethical considerations

The legal and ethical use of Big Data and Learning Analytics in academic libraries has been widely debated. Analyzing large data sets has tremendous potential for libraries to implement changes that help students and prove the library’s value to the university. The librarian’s role in safeguarding patron privacy in a university setting where learning analytics of Big Data is becoming the norm is still in the formative phase. This is a literature review on the ethical use of Big Data in universities and librarians’ ethical commitment to patrons. Student profiling, legislative response to technology, and ownership of information gathered are discussed.
Fast k-Means Clustering Algorithm for Malaria Detection in Thick Blood Smear

Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast k-means clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.
WI is Almost Enough: Contingent Payment All Over Again

The problem of fair exchange consists of interchanging goods between two parties that do not trust each other. Despite known impossibility results, recent works leverage the block-chain and zero-knowledge proofs to implement zero-knowledge contingent payment (zkCP) systems that make fair exchange of digital goods possible. Implementing these systems in a secure and efficient way is a big challenge, as evidenced by several unsuccessful attempts from the literature. Campanelli et al. (ACM CCS 2017) discovered a vulnerability on an existing zkCP proposal based on SNARKs (succinct non-interactive arguments of knowledge) and suggested several repairs. Fuchsbauer (ACM CCS 2019) found a flaw in the mentioned countermeasures. In particular, he showed that witness-indistinguishability (WI) is not sufficient for the zkCP schemes proposed by Campanelli et al. to be secure. In this work, we observe that a slightly stronger notion of WI, that we coin trapdoor subversion WI (tS-WI), rules out Fuchsbauer's attack. We formally define security properties for CP systems and show that, under tS-WI, Campanelli et al.'s proposal indeed satisfies these properties. Additionally, we explore alternative approaches to implement ZK (other than SNARKs) and develop a prototype, using it to demonstrate their potential. Our new ideas result in a protocol to sell ECDSA signatures with contingent payment that can be executed in less than $150$ milliseconds over a LAN network.
Development and Validation of an Automatic Item Generation System for English Idioms

Abstract Test development is a crucial, yet difficult and time‐consuming part of any educational system, and the task often falls all on teachers. Automatic item generation systems have recently drawn attention as they can reduce this burden and make test development more convenient. Such systems have been developed to generate items for vocabulary, grammar, reading, and writing. However, the literature lacks such a system for idioms. Informed by corpus linguistics, we aimed at developing a system that can generate items on idioms using the Corpus of Contemporary American English. The result was a system capable of generating two types of multiple‐choice items, that is, fill‐in‐the‐blank and definition. To validate the system output, we asked ten experts of Teaching English as a Foreign Language to evaluate 400 generated items for content issues. Also, a sample test of 40 items was administered to 110 upper‐intermediate and advanced EFL learners, and then validated through the Rasch model. The results manifested that a large number of generated items were acceptable based on both experts’ judgments and Rasch analysis. The study is an example of the application of corpus linguistics to language testing and a step toward automatic generation of items for assessment purposes.
Residue Number System Design Automation for Neural Network Acceleration

Residue Number Systems can solve several major problems of neural network including acceleration, power consumption, area overhead and fault tolerance. However, two major issues remain in the most recent work: (1) sign detection or magnitude comparison, and (2) long right shifts. In this paper, we develop a systematic approach to design a low power, compact, fast and reliable neural network automatically.