Reviewerly/exHarmony huggingface dataset:
: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service

The striking progress of deep learning paves the way towards intelligent and quality medical diagnostic services. Enterprises deploy such services via the neural network (NN) inference, yet confronted with rising privacy concerns of the medical data being diagnosed and the pre-trained NN models. We propose Open image in new window , a system framework that enables enterprises to offer secure medical diagnostic service to their customers via an execution of NN inference in the ciphertext domain. Open image in new window ensures the privacy of both parties with cryptographic guarantees. At the heart, we present an efficient and communication-optimized secure inference protocol that purely relies on the lightweight secret sharing techniques and can well cope with the commonly-used linear and non-linear NN layers. Compared to the garbled circuits based solutions, the latency and communication of Open image in new window are 24\(\times \) lower and 868\(\times \) less for the secure ReLU, and 20\(\times \) lower and 314\(\times \) less for the secure Max-pool. We evaluate Open image in new window on two benchmark and four real-world medical datasets, and comprehensively compare it with prior arts. The results demonstrate the promising performance of Open image in new window , which is much more bandwidth-efficient compared to prior works.
EAR-Net: Efficient Atrous Residual Network for Semantic Segmentation of Street Scenes Based on Deep Learning

Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation costs. First, we performed feature extraction and restoration, utilizing depthwise separable convolution (DSConv) and interpolation. Compared with conventional methods, DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. Second, we utilized residual learning and atrous spatial pyramid pooling (ASPP) to achieve high accuracy. Residual learning increases the ability to extract context information by preventing the problem of feature and gradient losses. In addition, ASPP extracts additional context information while maintaining the resolution of the feature map. Finally, to alleviate the class imbalance between the image background and objects and to improve learning efficiency, we utilized focal loss. We evaluated EAR-Net on the Cityscapes dataset, which is commonly used for street scene segmentation studies. Experimental results showed that the EAR-Net had better segmentation results and similar computation costs as the conventional methods. We also conducted an ablation study to analyze the contributions of the ASPP and DSConv in the EAR-Net.
Reciprocal Learning for Semi-supervised Segmentation

Semi-supervised learning has been recently employed to solve problems from medical image segmentation due to challenges in acquiring sufficient manual annotations, which is an important prerequisite for building high-performance deep learning methods. Since unlabeled data is generally abundant, most existing semi-supervised approaches focus on how to make full use of both limited labeled data and abundant unlabeled data. In this paper, we propose a novel semi-supervised strategy called reciprocal learning for medical image segmentation, which can be easily integrated into any CNN architecture. Concretely, the reciprocal learning works by having a pair of networks, one as a student and one as a teacher. The student model learns from pseudo label generated by the teacher. Furthermore, the teacher updates its parameters autonomously according to the reciprocal feedback signal of how well student performs on the labeled set. Extensive experiments on two public datasets show that our method outperforms current state-of-the-art semi-supervised segmentation methods, demonstrating the potential of our strategy for the challenging semi-supervised problems. The code is publicly available at https://github.com/XYZach/RLSSS.
Linguistic Studies on Social Media: A Bibliometric Analysis

This study aimed to present the status quo of linguistic studies on social media in the past decade. In particular, it conducted a bibliometric analysis of articles from the field of linguistics of the database of Web of Science Core Collection with the aid of the tool CiteSpace to identify the general characteristics, major strands of linguistics, main research methods, and important research themes in the area of linguistic studies on social media. The main findings are summarized as follows. First, the study reported the publication trend, main publication venues, researched social media platforms, and languages used in researched social media. Second, sociolinguistics and pragmatics were found to be major strands of linguistics used in relevant studies. Third, the study identified seven main research methods: discourse analysis, critical discourse analysis, conversation analysis, multimodal analysis, narrative analysis, ethnographic analysis, and corpus analysis. Fourth, important research themes were extracted and classified based on four dimensions of the genre framework of social media studies. They were the participation nature and technology affordances of social media in the dimension of compositional level, the researched topics of education, (language) policy and politics in the dimension of thematic orientations, the researched discursive practices of (im)politeness, humor, indexicality and multilingualism in the dimension of stylistic traits, and the researched communicative functions of constructing identity, communicating (language) ideology, and expressing attitude in the pragmatic dimension. Moreover, linguistic studies on social media tended to be characterized by cross-disciplinary and mixed-method approaches.
Drivers of Blockchain Adoption in Financial and Supply Chain Enterprises

The arrival of blockchain technology has disrupted many business sectors globally, encouraging enterprises to employ it and digitize their operations to work more efficiently. This study has gathered practical insights from many organizational blockchain experts to explore the drivers of blockchain technology adoption at the organizational level and differences in discovered drivers across financial and supply chain industries. This qualitative study is conducted based on grounded theory, using Technology-Organization-Environment (TOE) as a framework for blockchain adoption drivers in Thailand, as identified from in-depth interviews with blockchain experts of 12 Thai financial and supply chain organizations. The findings have shown that operational efficiency, suitable application, supportive governmental policies and regulations, and stakeholders’ cooperation are newly emerged TOE factors, and that each of the focussed sectors weighs environmental factors differently due to naturally different goals and the business model of each sector, which ultimately guides the future adopters in blockchain adoption.
On Recognizing Occluded Faces in the Wild

Facial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the problem. In this paper, we present the Real World Occluded Faces (ROF) dataset, that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks. We propose two evaluation protocols for this dataset. Benchmark experiments on the dataset have shown that no matter how powerful the deep face representation models are, their performance degrades significantly when they are tested on real-world occluded faces. It is observed that the performance drop is far less when the models are tested on synthetically generated occluded faces. The ROF dataset and the associated evaluation protocols are publicly available at the following link https://github.com/ekremerakin/RealWorldOccludedFaces.
Detection of Breast Cancer using Machine Learning Classifier

Breast Cancer is a cancer that starts from the cells of breasts of women typically in the age of 25–50. Manual diagnosis of breast cancer takes long time. So, for early detection, automatic diagnosis system should be developed. This can be done using Machine learning techniques effectively. So, here we have developed a machine learning model which will predict whether the person has breast cancer or not. We will be using machine learning classifiers for training the model on Wisconsin Diagnostic Breast Cancer (WDBC) dataset from UCI repository and will compare the accuracies. We would consider different features of the person's cell and train our model accordingly to achieve maximum accuracy. For the ease of use, we would convert the model into a web application where user is supposed to enter the information about the cells of the person. This method would surely decrease the efforts of doctors in detecting breast cancer easily.
Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learning

The objective of this work is to segment any arbitrary structures of interest (SOI) in 3D volumes by only annotating a single slice, (i.e. semi-automatic 3D segmentation). We show that high accuracy can be achieved by simply propagating the 2D slice segmentation with an affinity matrix between consecutive slices, which can be learnt in a self-supervised manner, namely slice reconstruction. Specifically, we compare our proposed framework, termed as Sli2Vol, with supervised approaches and two other unsupervised/self-supervised slice registration approaches, on 8 public datasets (both CT and MRI scans), spanning 9 different SOIs. Without any parameter-tuning, the same model achieves superior performance with Dice scores (0–100 scale) of over 80 for most of the benchmarks, including the ones that are unseen during training. Our results show generalizability of the proposed approach across data from different machines and with different SOIs: a major use case of semi-automatic segmentation methods where fully supervised approaches would normally struggle.
Towards Protecting Face Embeddings in Mobile Face Verification Scenarios

This paper proposes PolyProtect, a method for protecting the sensitive face embeddings that are used to represent people's faces in neural-network-based face verification systems. PolyProtect transforms a face embedding to a more secure template, using a mapping based on multivariate polynomials parameterised by user-specific coefficients and exponents. In this work, PolyProtect is evaluated on two open-source face recognition systems in a cooperative-user mobile face verification context, under the toughest threat model that assumes a fully-informed attacker with complete knowledge of the system and all its parameters. Results indicate that PolyProtect can be tuned to achieve a satisfactory trade-off between the recognition accuracy of the PolyProtected face verification system and the irreversibility of the PolyProtected templates. Furthermore, PolyProtected templates are shown to be effectively unlinkable, especially if the user-specific parameters employed in the PolyProtect mapping are selected in a non-naive manner. The evaluation is conducted using practical methodologies with tangible results, to present realistic insight into the method's robustness as a face embedding protection scheme in practice. This work is fully reproducible using the publicly available code at: https://gitlab.idiap.ch/bob/bob.paper.polyprotect_2021.
Privacy-Preserving Multi-Granular Federated Neural Architecture Search A General Framework

Jointly learning from multiple datasets can help building versatile intelligent systems yet may give rise to serious concerns of data privacy and model selection. Specifically, on the one hand, these datasets can be distributed at various local clients, who may not be willing or do not ought to share data with each other. On the other hand, it is unrealistic to choose a model architecture that can well suit the disparate patterns and distributions carried by the various datasets in a priori. Whereas many works in federated learning [1] and neural architecture search [2] have been proposed to address one of the two concerns, very few have attempted the both. To close the gap, in this paper we deliver a framework, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Granular Federated Neural Architecture Search</i> (MGFNAS), to enable the automation of model architecture search in a federated and thus privacy-preserved setting. We argue that our MGFNAS framework is general in the sense that it does not impose any restriction on the search space or strategy, such that most existing neural architecture search techniques can be readily implemented in. The main idea of our framework is to search the optimal neural network architecture in two levels of granularity, enabling the neural-operator-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro-level</i> search and the cell-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">macro-level</i> search. The main challenge of implementing our framework lies in the fact that, due to the decentralized nature, the local architectures searched by multiple clients can differ drastically in order to fit their own datasets, while a general method to form the global model by aggregating the local architectures in both micro and macro levels is missing. To solve the issue, we propose a novel aggregation function, named Network Architecture Probabilistic Aggregation (NAPA). The key idea of our NAPA function is to treat the network architectures as graphs, of which the sub-graph structures being frequently appeared across multiple clients are modeled by probabilistic distributions. At each round, a global model is formed by sampling from those distributions in an exploration-exploitation fashion. Extensive experiments are carried out, and the results substantiate the viability and effectiveness of our proposed framework.
A Truthful Procurement Auction for Incentivizing Heterogeneous Clients in Federated Learning

Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the participation of heterogeneous clients. This work targets social cost minimization, and studies the incentive mechanism design in FL through a procurement auction. Different from existing literature, we consider a practical scenario of FL where clients are selected and scheduled at different global iterations to guarantee the completion of the FL job, and capture the distinct feature of FL that the number of global iterations is determined by the local accuracy of all participants to balance between computation and communication. Our auction framework <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{FL}$</tex> first decomposes the social cost minimization problem into a series of winner determination problems (WDPs) based on the number of global iterations. Then to solve each WDP, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{FL}$</tex> invokes a greedy algorithm to determine the winners, and a payment algorithm for computing remuneration to winners. Finally, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{FL}$</tex> returns the best solution among all WDPs. Theoretical analysis proves that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{FL}$</tex> is truthful, individual rational, computationally efficient, and achieves a near-optimal social cost. We further conduct large-scale simulation studies based on the real-world data. Simulation results show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A_{FL}$</tex> can reduce the social cost by up to 75% compared with state-of-the-art algorithms.
Online-trained Upsampler for Deep Low Complexity Video Compression

Deep learning for image and video compression has demonstrated promising results both as a standalone technology and a hybrid combination with existing codecs. However, these systems still come with high computational costs. Deep learning models are typically applied directly in pixel space, making them expensive when resolutions become large.In this work, we propose an online-trained upsampler to augment an existing codec. The upsampler is a small neural network trained on an isolated group of frames. Its parameters are signalled to the decoder. This hybrid solution has a small scope of only 10s or 100s of frames and allows for a low complexity both on the encoding and the decoding side.Our algorithm works in offline and in zero-latency settings. Our evaluation employs the popular x265 codec on several high-resolution datasets ranging from Full HD to 8K. We demonstrate rate savings between 8.6% and 27.5% and provide ablation studies to show the impact of our design decisions. In comparison to similar works, our approach performs favourably.
A Reliable Technique for Sentiment Analysis on Tweets via Machine Learning and BERT

In recent times, when we fiddle with innovation particularly with social media, it leads to an increasing change in human conduct. The day-by-day utilization of web-based media by individuals has expanded a lot that it is gradually infusing an expression into our conduct. Particularly, Twitter is generally utilized authoritatively by numerous individuals and is one of only a handful few social media platforms where individuals search for reliable information. The intent of this work is to perform sentiment analysis of the tweets using different Machine Learning algorithms and the BERT model to prevent offensive use of words and give an insight into the emotion of the words in the tweet. The dataset utilized is obtained from Kaggle. The Machine Learning models employed are Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), Logistic Regression, Decision-Tree., Random Forest Method, and XGBoost along with a deep learning architecture BERT for the analysis. A comparative analysis of all the models was carried out and it can be concluded that logistic Regression obtained the highest training and validation accuracy of 82.65% and 81.74%, respectively, and the F1 score of this model was 79.14%. BERT model has an exceptional performance in Natural Language Processing, so BERT was also implemented for the sentimental analysis of the Twitter tweets and it achieved the highest training accuracy, validation accuracy, and F1 score of 94%., 93.63%, and 92.34% respectively.
When tales of money fail: the importance of price, trust, and sociality for cryptocurrency users

This paper is based on research among blockchain communities in the Netherlands and online terrains. Through an empirical example, it explores tales produced by projects based on the blockchain protocol and on the premise that cryptocurrencies are money and that money has generative potential for social and economic change. By unpacking the pragmatics of a particular project – Bitnation and Pangea – I argue that despite tales of decentralisation through the moneyness of cryptocurrencies, and the distributed and automated character of the blockchain protocol, these currencies, and projects, are deeply entangled with fiat and mainstream economies and markets. This is visible by looking at the ups and downs of cryptocurrency pricing and on the effects this volatility has on (certain) projects. The lack of a sustainable community of trust in cryptocurrencies as money – particularly visible in initiatives following more libertarian and utopian tales, detached from everyday life realities – and the way these retain attention mainly due to speculation, have very real effects for blockchain based projects. No matter how radical their tales for decentralisation and socioeconomic revolution are, utterances for these tales to become real have to be there, and seem absent.
United equilibrium optimizer for solving multimodal image registration

This study presents an optimization algorithm, called united equilibrium optimizer (UEO), which is modified from the equilibrium optimizer (EO). We improved the search structure of the EO and adjusted it using dynamic parameters. These improvements increase the potential of UEO in both exploration and exploitation, which makes UEO perform better in local minima avoidance and fast convergence. In this study, the UEO and other 11 algorithms were benchmarked with 30 unimodal, multimodal, and composition functions, as well as in medical image registration problems. In medical image registration problems, UEO is compared with the other three algorithms that have been successfully applied to medical image registration. The UEO has been tested for benchmark datasets, including three types of different modality images, from up to 16 patients, resulting in 41 multimodal registration scenarios. All the results demonstrated that UEO outperforms other algorithms in most cases, either in optimization tests or in practical problems. The source code of the UEO is publicly available at https://github.com/PengGui-N/United-Equilibrium-Optimizer.
EAR: An Enhanced Adversarial Regularization Approach against Membership Inference Attacks

Membership inference attacks on a machine learning model aim to determine whether a given data record is a member of the training set. They pose severe privacy risks to individuals, e.g., identifying an individual's participation in a hospital's health analytic training set reveals that this individual was once a patient in that hospital. Adversarial regularization (AR) is one of the state-of-the-art defense methods that mitigate such attacks while preserving a model's prediction accuracy. AR adds membership inference attacks as a new regularization term to the target model during the training process. It is an adversarial training algorithm that is trained on a defended model which is essentially the same as training the generator of generative adversarial networks (GANs). We observe that many GAN variants are able to generate higher quality samples and offer more stability during the training phase than GANs. However, whether these GAN variants are available to improve the effectiveness of AR has not been investigated. In this paper, we propose an enhanced adversarial regularization (EAR) method based on Least Square GANs (LSGANs). The new EAR surpasses the existing AR in offering more powerful defensive ability while preserving the same prediction accuracy of the protected classifiers. We systematically evaluate EAR on five datasets with different target classifiers under four different attack methods and compare it with four other defense methods. We experimentally show that our new method performs the best among other defense methods.