Reviewerly/exHarmony huggingface dataset:
GDPR Oriented Vendors Contracts in Relation to Data Transfer: Analysis of Standard Clauses 2010 and 2021

Nowadays, data protection and its laws have been in an unstoppable de-bating. Every country have established their own data protection law in respect to European Union Global Data Protection Regulation (GDPR). Each and every organizations of any sector have to comply with some global standards whilst dealing with some other organizations which have been termed as vendors and to ensure them that they have took appropriate measures for protecting the information of consumers or employees. Standard contractual clause has been established by European Union (EU) in which two major terms have been discussed under which liabilities have been defined. These two major terms, i.e. Data Processors and Data Controllers discussed in whole contract. In 2021, new Standard Contractual Clauses (SCC) has been published which has defined the similar liabilities of Data Processors and Data Controllers unlike of 2020 SCC, the liabilities are different. In this paper the author have analysis both 2020 and 2021 Standard Contractual clauses and their implications.
Experimental Operationalizations of Anthropomorphism in HCI Contexts: A Scoping Review

Anthropomorphism of computerized agents, avatars, and technologies has been the focus of a large body of research in human-computer interaction (HCI). Yet, operational definitions of anthropomorphism vary greatly, creating the potential for error when broad theoretical conclusions are drawn from operationalizations lacking in content validity. This scoping review aimed to identify and categorize the range of operationalizations of anthropomorphism in experimental studies of computerized agents, avatars, and technologies, adding needed clarity to a diverse area of inquiry. Using five selection criteria, this review categorized the operationalization(s) of anthropomorphism in 31 experiment-based articles published in academic research journals. Results showed a heavy dominance of manipulations of physical appearance as operationalizations of anthropomorphism, which thretens content validity and raises questions about the understanding of anthropomorphism in HCI.
STRETCHING PROJECTION - PROJECTION-BASED VISUAL CONTROL AND SUPPORT FOR SPORTS EXERCISES

Various fitness trackers make it possible to check vital signs, count steps or measure the duration of an exercise. However, a fitness tracker does not provide information about the correct execution of a sport exercise. This is the motivation for our contribution. We introduce the concept of an interactive training environment using the example of stretching exercises in the form of a computer-based visual feedback system, which is intended to increase the efficiency and safety of a training session. We answer the question: How can such a system be designed?
Hilbert transformation deep learning network for single-shot moiré profilometry

Phase demodulation from a single moiré fringe pattern is an ill-posed inverse problem that limits the applications of moiré profilometry in dynamic three-dimensional (3D) measurement. In this paper, a deep-learning-based high-precision technique is used to solve this problem arising from highly under sampled inputs. Our novel approach termed two-dimensional (2D) Hilbert transformation network uses two Res U-Net networks paired with a dichotomous network to generate the desired quadrature fringe pattern by referring to the input. This process can be viewed as 2D Hilbert transformation of a fringe pattern. By using the proposed network, the wrapped phase can be extracted easily if the sampled fringe pattern is filtered and normalized in advance. Experimental results obtained using the proposed Hilbert transformation network trained on simulated data indicate that it is a simple, albeit robust solution for phase extraction from a single fringe pattern with a phase error of less than 0.02 rad. Thus, the proposed network represents a novel approach to reliable and practical learning-based single-shot Moiré profilometry.
Pathological Image Contrastive Self-supervised Learning

Self-supervised learning methods have been receiving wide attentions in recent years, where contrastive learning starts to show encouraging performance in many tasks in the field of computer vision. Contrastive learning methods build pre-training weight parameters by crafting positive/negative samples and optimizing their distance in the feature space. It is easy to construct positive/negative samples on natural images, but the methods cannot directly apply to histopathological images because of the unique characteristics of the images such as staining invariance and vertical flip invariance. This paper proposes a general method for constructing clinical-equivalent positive sample pairs on histopathological images for applying contrastive learning on histopathological images. Results on the PatchCamelyon benchmark show that our method can improve model accuracy up to 6% while reducing the training costs, as well as reducing reliance on labeled data.
Morphology-Aware Interactive Keypoint Estimation

Diagnosis based on medical images, such as X-ray images, often involves manual annotation of anatomical keypoints. However, this process involves significant human efforts and can thus be a bottleneck in the diagnostic process. To fully automate this procedure, deep-learning-based methods have been widely proposed and have achieved high performance in detecting keypoints in medical images. However, these methods still have clinical limitations: accuracy cannot be guaranteed for all cases, and it is necessary for doctors to double-check all predictions of models. In response, we propose a novel deep neural network that, given an X-ray image, automatically detects and refines the anatomical keypoints through a user-interactive system in which doctors can fix mispredicted keypoints with fewer clicks than needed during manual revision. Using our own collected data and the publicly available AASCE dataset, we demonstrate the effectiveness of the proposed method in reducing the annotation costs via extensive quantitative and qualitative results.
Task Variance Regularized Multi-Task Learning

Multi-task Learning (MTL), which involves the simultaneous learning of multiple tasks, can achieve better performance than learning each task independently. It has achieved great success in various applications, ranging from Computer Vision (CV) to Natural Language Processing (NLP). In MTL, the losses of the including tasks are jointly optimized. However, it is common for these tasks to be competing. When the tasks are competing, minimizing the losses of some tasks increases the losses of others, which accordingly increases the task variance (variance between the task-specific loss); furthermore, it induces under-fitting in some tasks and over-fitting in others, which degenerates the generalization performance of an MTL model. To address this issue, it is necessary to control the task variance; thus, task variance regularization is a natural choice. While intuitive, task variance regularization remains unexplored in MTL. Accordingly, to fill this gap, we study the generalization error bound of MTL through the lens of task variance and propose the task variance matters the generalization performance of MTL. Furthermore, this paper investigates how the task variance might be effectively regularized, and consequently proposes a multi-task learning method based on adversarial multi-armed bandit. The proposed method, dubbed BanditMTL, regularizes the task variance by means of a mirror gradient ascent-descent algorithm. Adopting BanditMTL both in CV and NLP applications is found to achieve state-of-the-art performance. The results of extensive experiments back up our theoretical analysis and validate the superiority of our proposals.
Automatic Detection of Steatosis in Ultrasound Images with Comparative Visual Labeling

A common difficulty in computer-assisted diagnosis is acquiring accurate and representative labeled data, required to train, test and monitor models. Concerning liver steatosis detection in ultrasound (US) images, labeling images with human annotators can be error-prone because of subjectivity and decision boundary biases. To overcome these limits, we propose comparative visual labeling (CVL), where an annotator labels the relative degree of a pathology in image pairs, that is combined with a RankNet to give per-image diagnostic scores. In a multi-annotator evaluation on a public steatosis dataset, CVL+RankNet significantly improves label quality compared to conventional single-image visual labeling (SVL) (0.97 versus 0.87 F1-score respectively, 95% CI significance). This is the first application of CVL for diagnostic medical image labeling, and it may stimulate more research for other diagnostic labeling tasks. We also show that Deep Learning (DL) models trained with CVL+RankNet or histopathology labels attain similar performance.
Differentially Oblivious Two-Party Pattern Matching With Sublinear Round Complexity

Privacy-preserving pattern matching enables a user to find all occurrences of a pattern in a text without revealing any sensitive information. However, many previous works designed on homomorphic encryption suffer from expensive computational overhead and a simple way to use it can lead to potential input leakage via access pattern during the matching process. In this article, we propose a differentially oblivious pattern matching algorithm, called <monospace>DOPM</monospace> . It is deployed on two servers by taking a series of lightweight secret-sharing-based protocols as building blocks. In <monospace>DOPM</monospace> , we utilize a witness array and the single instruction multiple data (SIMD) technique to parallelize the algorithm, which achieves sublinear round complexity in performing two-party computation. Additionally, we formally define a new access pattern privacy in the context of differential privacy, named <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -differentially oblivious privacy ( <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -DOP), and present a pair of differentially oblivious algorithms to read and write elements in an array without using oblivious shuffle. Detailed security analysis demonstrates that the proposed <monospace>DOPM</monospace> achieves the goal of protecting confidentiality and access pattern during the matching process. Finally, we benchmark our scheme on a real-world human genome dataset, and experimental results show that <monospace>DOPM</monospace> is <inline-formula><tex-math notation="LaTeX">$10.9\times$</tex-math></inline-formula> faster than the brute-force matching, <inline-formula><tex-math notation="LaTeX">$3.4-7.1\times$</tex-math></inline-formula> faster than two state-of-the-art approaches.
Proxyless Neural Architecture Adaptation at Once

Recently, Neural Architecture Search (NAS) methods are introduced and show impressive performance on many benchmarks. Among those NAS studies, Neural Architecture Transformer (NAT) aims to adapt the given neural architecture to improve performance while maintaining computational costs. In the architecture adaptation task, we can utilize the known high-performance architectures, and the architecture adaptation results of NAT showed performance improvements on various architectures in their experiments. However, we verified that NAT lacks reproducibility through multiple trials of experiments. Moreover, it requires an additional architecture adaptation process before network weight training. In this paper, we propose proxyless neural architecture adaptation that is reproducible and efficient. The proposed method doesn’t need a proxy task for architecture adaptation. It directly improves the architecture during the conventional training process, and we can directly use the trained neural network. Moreover, the proposed method can be applied to both supervised learning and self-supervised learning. The proposed method shows stable performance improvements on various architectures and various datasets. Extensive experiments on two benchmark datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., CIFAR-10 and Tiny Imagenet, present that the proposed method definitely outperforms NAT and be applicable to various models and datasets.
Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation

We propose a novel framework that applies atlas-based whole-brain segmentation methods to tumor-bearing MR images. Given a patient brain MR image where the tumor is initially segmented, we use a point-cloud deep learning method to predict a displacement field, which is meant to be the deformation (inverse) caused by the growth (mass-effect and cell-infiltration) of the tumor. It's then used to warp and modify the brain atlas to represent the change so that existing atlas-based healthy-brain segmentation methods could be applied to these pathological images. To show the practicality of our method, we implement a pipeline with nnU-Net MRI tumor initial segmentation and SAMSEG, an atlas-based whole-brain segmentation method. To train and validate the deformation network, we synthesize pathological ground truth by simulating artificial tumors in healthy images with TumorSim. This method is evaluated with both real and synthesized data. These experiments show that segmentation accuracy can be improved by learning tumor-induced deformation before applying standard full brain segmentation. Our code is available at https://github.com/jiameng1010/Brain_MRI_Tumor .
The impact of virtual meetings in learning on eye fatigue during the Covid-19 pandemic

The Covid-19 pandemic has impacted all aspects of human life, including education. Implementation of learning through virtual meetings is an alternative solution when face-to-face learning cannot be done. However, the implementation of learning through monotonous virtual meetings has the potential to cause eye fatigue for students. This study aims to determine the relationship between virtual meeting learning and eye fatigue experienced by students in participating in learning. This research is quantitative research with an analytical survey with a cross-sectional study design. The population in this study were all students in Gorontalo Province. The sample size was 105 students who were selected using a purposive sampling technique with several considerations from the researcher. The data was collected using a questionnaire made in the form of a google form. Data analysis using Chi-Square test. The results of the analysis show that virtual meetings are related to the fatigue experienced by students in participating in learning (p-value=0.031). Fatigue can be avoided by replacing learning through virtual meetings with e-learning approaches, or other learning methods.
The Legal Innovation Sandbox

"The Legal Innovation Sandbox" examines a novel regulatory approach, called the innovation sandbox, in the context of the legal profession. The paper makes the claim that the “sandbox” regulatory model is in fact better suited to fostering innovation in the legal services arena than it is in the financial technology, or fintech, arena in which the sandbox concept developed. However, any effort to transplant a technique from one context to another needs to be carefully considered. This article is comparative across disciplines – financial regulation and legal services regulation – and across jurisdictions – covering the United Kingdom, the United States, and Canada.The paper analyzes the key normative assumptions underlying the sandbox concept in fintech: that innovation is beneficial almost by definition, that consumer choice and market preferences can be counted on to winnow out “bad” ideas, and that a private sector-driven strategy based on lifting “regulatory burdens” is an effective way of advancing the public interest. These assumptions, which are fairly mainstream in financial regulation, are unfamiliar if not alarming when transposed to legal services regulation. After discussing normative and contextual differences between these regulatory environments, the paper argues that although these ideas may seem problematic at first glance, the sandbox approach may in fact be particularly promising. It may actually be possible to foster legal innovation, advance the public interest, and take meaningful steps to address the access to justice crisis using an innovation sandbox. However, success will come down to how well the sandbox is implemented. The paper’s second half provides a roadmap, informed by rule of law and justice concerns and based on experience from the fintech sector, for how to create a high-functioning, accountable, equity-conscious innovation sandbox for legal services.
OPTICS: Automatic MT Evaluation Based on Optimal Transport by Integration of Contextual Representations and Static Word Embeddings

Automatic MT metrics using word embeddings are extremely effective. Semantic word similarities are obtained using word embeddings. However, similarities using only static word embeddings are insufficient for lack of contextual information. Automatic metrics using fine-tuned models can adapt to a specific domain using contextual representations obtained by learning, but that adaptation requires large amounts of data to learn suitable models. As described herein, we propose an automatic MT metric based on optimal transport using both contextual representations and static word embeddings. The contextual representations are obtained by learning the neural models. In that case, our proposed metric requires no other data except source sentences and references, which correspond to the evaluation target hypotheses, to learn the models that are used to extract the contextual representations. Therefore, our proposed metric can adapt to the domain appropriately without requiring large amounts of learning data. Experiment results obtained using the WMT 20 metric shared task data indicated that correlations with human judgment using our proposed metric are higher than those using a metric based only on static word embeddings. Moreover, our proposed metric achieved state-of-the-art performance with system-level correlation and to-English segment-level correlation.
Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks

Localization of tube-shaped objects is an important topic in medical imaging. Previously it was mainly addressed via dense segmentation that may produce inconsistent results for long and narrow objects. In our work, we propose a point-based approach for explicit centerline segmentation that can be learned by fully-convolutional networks. We propose a new bi-directional encoding scheme that does not require any autoregressive blocks and is robust to various shapes and orientations of lines, being adaptive to the number of points in their centerlines. We present extensive evaluation of our approach on synthetic and real data (chest x-ray and coronary angiography) and show its advantage over the state-of-the-art segmentation models.
Text-to-Text Transfer Transformer Phrasing Model Using Enriched Text Input

Appropriate prosodic phrasing of the input text is crucial for natural speech synthesis outputs. The presented paper focuses on using a Text-to-Text Transfer Transformer for predicting phrase boundaries in text and inspects the possibility of enriching the input text with more detailed information to improve the success rate of the phrasing model trained on plain text. This idea came from our previous research on phrasing that showed that more detailed syntactic/semantic information might lead to more accurate predicting of phrase boundaries.
Transfer Learning of Transformers for Spoken Language Understanding

Pre-trained models used in the transfer-learning scenario are recently becoming very popular. Such models benefit from the availability of large sets of unlabeled data. Two kinds of such models include the Wav2Vec 2.0 speech recognizer and T5 text-to-text transformer. In this paper, we describe a novel application of such models for dialog systems, where both the speech recognizer and the spoken language understanding modules are represented as Transformer models. Such composition outperforms the baseline based on the DNN-HMM speech recognizer and CNN understanding.
A Hybrid Propagation Network for Interactive Volumetric Image Segmentation

Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional user hints, e.g., scribbles and clicks. Currently, interactive segmentation methods for 2D medical images are well studied, while seldom works are conducted on 3D medical volumetric data. Given a 3D volumetric image, the user interaction can only be performed on a few slices, thus the key issue is how to propagate the information over the entire volume for spatial-consistent segmentation. In this paper, we propose a novel hybrid propagation network for interactive segmentation of 3D medical images. Our proposed method consists of two key designs, including a slice propagation network (denoted as SPN) for transferring user hints to adjacent slices to guide the segmentation slice-by-slice and a volume propagation network (denoted as VPN) for propagating user hints over the entire volume in a global manner. Specifically, as for SPN, we adopt a memory-augmented network, which utilizes the information of segmented slices (memory slices) to propagate interaction information. To use interaction information propagated by VPN, a feature-enhanced memory module is designed, in which the volume segmentation information from the latent space of VPN is introduced into the memory module of SPN. In such a way, the interactive segmentation can leverage both advantages of volume and slice propagation, thus improving the volume segmentation results. We perform experiments on two commonly-used 3D medical datasets, with the experimental results indicating that our method outperforms the state-of-the-art methods. Our code is available at https://github.com/luyueshi/Hybrid-Propagation .
Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment. These learning tasks are often solved with deep multi-instance learning (MIL) models that do not explicitly capture intratumoral heterogeneity. We develop a novel variance pooling architecture that enables a MIL model to incorporate intratumoral heterogeneity into its predictions. Two interpretability tools based on "representative patches" are illustrated to probe the biological signals captured by these models. An empirical study with 4,479 gigapixel WSIs from the Cancer Genome Atlas shows that adding variance pooling onto MIL frameworks improves survival prediction performance for five cancer types.