Reviewerly/exHarmony huggingface dataset:
Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems

Abstract We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles, the proposed methods are optimal in terms of the number of communication steps. However, for all classes of the objective, the optimality in terms of the number of oracle calls per node takes place only up to a logarithmic factor and the notion of smoothness. By using mini-batching technique, we show that the proposed methods with stochastic oracle can be additionally parallelized at each node. The considered algorithms can be applied to many data science problems and inverse problems.
Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer

Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.
The effect of using machine translation on linguistic features in L2 writing across proficiency levels and text genres

Many studies that have investigated the educational value of online machine translation (MT) in second language (L2) writing generally report significant improvements after MT use, but no study as of yet has comprehensively analyzed the effectiveness of MT use in terms of various measures in syntactic complexity, accuracy, lexical complexity, and fluency (CALF). The present study examined how learners’ use of MT affects CALF measures in L2 writing using evaluations by automated computational tools as well as human raters. In addition, the study investigated whether proficiency level and text genre affect the learners’ use of MT. A total of 91 Korean learners of English participated in the main task of the study in which they wrote on an assigned topic in English first without the help of any resource, and then on a different topic using only Google Translate a week later. Text analysis of students’ writing revealed major improvements in accuracy but unclear benefits in syntactic and lexical complexity. It was also found that MT use provided different advantages and disadvantages depending on the proficiency level (high vs. low) and text genre (narrative vs. argumentative). Survey responses strongly indicated that students are highly satisfied with MT and plan to use it again in the future despite being aware of its limitations. Overall, this study found that MT can be useful for improving accuracy but must be used with much discretion for it to benefit other aspects of L2 writing.
Privacy-preserving and communication-efficient federated learning in Internet of Things

Aimed at the privacy leakage caused by collecting data from numerous Internet of Things (IoT) devices for centralized training, a novel distributed learning framework, namely federated learning, came into being, where devices train models collaboratively while leaving their private datasets locally. Although many schemes have been proposed about federated learning, they are still short in communications and privacy due to the limited network bandwidth and advanced privacy attacks. To address these challenges, we develop PCFL, a privacy-preserving and communication-efficient scheme for federated learning in IoT. PCFL is composed of three key components: (1) gradient spatial sparsification where irrelevant local updates that deviate from the collaborative convergence tendency are prevented from being uploaded; (2) bidirectional compression where computation-less compression operators are used to quantize the gradients both at the device-side and server-side; and (3) privacy-preserving protocol which integrates secret sharing with lightweight homomorphic encryption to protect the data privacy and resist against various collusion scenarios. We analyze the correctness and privacy of our scheme, and carry out theoretical and experimental comparison on two real-world datasets. Results show that PCFL outperforms the state-of-the-art methods by more than 2× in terms of communication efficiency, along with high model accuracy and marginal decreases in convergence rate.
Transferable common feature space mining for fault diagnosis with imbalanced data

Many deep transfer learning methods for fault diagnosis have been proposed in this decade. Some of the existing methods focus on addressing the problem of fault data scarcity and fault knowledge transfer across different domains with different number of samples. There is still much room to improve considering the best performance so far on imbalanced and transfer fault diagnosis. The existing researches apply synthetic data generation, weighted sample or cost and transfer learning techniques to solve the problem. However, the synthetic samples might not follow the true fault data distribution or exploit excessively over the available small data which could lead to model bias or overfitting. In addition, the value of the abundant normal condition data has not been well explored which may carry essential information for fault discrimination. To address these problems, a novel two stage transferable common feature space mining method for fault diagnosis is developed which is termed as Common Feature and Compare Net (CFCNet). The fault diagnosis task has been divided into two stages, common feature learning and fault category diagnosis. In the first stage, CFCNet trains a weakly supervised domain adaptive convolutional Autoencoder to learn the common features underlying multi-domain data, which makes efficient use of all the available data and is termed as Common Feature Net. In the second stage, the trained Common Feature Net and a Unique Feature Net is combined to construct a dual-channel feature extraction and comparison architecture. CFCNet could mine both the transferable common features and unique features of different faults. Based on a feature concatenation and similarity computation structure, CFCNet enables an efficient similarity estimation mechanism for fault diagnosis. Training strategy of few shot learning is adopted to train CFCNet which can balance the training progress instead of the imbalanced data. Extensive experiments have verified the superior performance of the proposed method.
Banquet: Short and Fast Signatures from AES

This work introduces Banquet, a digital signature scheme with post-quantum security, constructed using only symmetric-key primitives. The design is based on the MPC-in-head paradigm also used by Picnic (CCS 2017) and BBQ (SAC 2019). Like BBQ, Banquet uses only standardized primitives, namely AES and SHA-3, but signatures are more than 50% shorter, making them competitive with Picnic (which uses a non-standard block cipher to improve performance). The MPC protocol in Banquet uses a new technique to verify correctness of the AES S-box computations, which is efficient because the cost is amortized with a batch verification strategy. Our implementation and benchmarks also show that both signing and verification can be done in under 10ms on a current x64 CPU. We also explore the parameter space to show the range of trade-offs that are possible with the Banquet design, and show that Banquet can nearly match the signature sizes possible with Picnic (albeit with slower, but still practical run times) or have speed within a factor of two of Picnic (at the cost of larger signatures).
Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.
When permissioned blockchains deliver more decentralization than permissionless

Considerations for the governance of distributed systems.
Channel and Space Attention Neural Network for Image Denoising

Recently, convolutional neural networks (CNN) have been widely used in image denoising. But with most CNN denoising methods, all the channels are treated equally and the relationship between spatial locations are neglected. In the letter, we propose a novel channel and space attention neural network (CSANN) for image denoising. In CSANN, we concatenate the noise level with the average and maximum values of each channel as the input and propose a convolutional network to learn the relationship between channels. Meanwhile, we combine the noise level map with the average and maximum values of each spatial locations as the input and use a convolutional network to learn the relationship between spatial locations. Moreover, we combine them as an attention network and introduce it into the main CNN and symmetric skip connections, which makes channels related to attention network play different roles in the subsequent convolution and offsets the performance degradation caused by using a single convolution kernel in spatial locations. In addition, the use of symmetric skip connections and resnet blocks avoid the vanishing gradient problem and the loss of shallow features. Experimental results show that, compared with some state-of-the-art denoising algorithms, the experimental results of CSANN have better visual effects and higher peak signal-to-noise ratio (PSNR) values.
Belonging There

The world is entering a new normal of hybrid organisations, in which it will be common that some members are co-located and others are remote. Hybridity is rife with asymmetries that affect our sense of belonging in an organisational space. This paper reports a study of an XR Telepresence technology probe to explore how remote workers might present themselves and be perceived as an equal and unique embodied being in a workplace. VROOM (Virtual Robot Overlay for Online Meetings) augments a standard Mobile Robotic Telepresence experience by (1) adding a virtual avatar overlay of the remote person to the local space, viewable through a HoloLens worn by the local user, through which the remote user can gesture and express themselves, and (2) giving the remote user an immersive 360° view of the local space, captured by a 360° camera on the robot, which they can view through a VR headset. We ran a study to understand how pairs of participants (one local and one remote) collaborate using VROOM in a search and word-guessing game. Our findings illustrate that there is much potential for a system like VROOM to support dynamic collaborative activities in which embodiment, gesturing, mobility, spatial awareness, and non-verbal expressions are important. However, there are also challenges to be addressed, specifically around proprioception, the mixing of a physical robot body with a virtual human avatar, uncertainties of others' views and capabilities, fidelity of expressions, and the appearance of the avatar. We conclude with further design suggestions and recommendations for future work.
Using Students’ Experience to Derive Effectiveness of COVID-19-Lockdown-Induced Emergency Online Learning at Undergraduate Level: Evidence from Assam, India

The COVID-19 episode has left the institutions worldwide, which were earlier reluctant to change their traditional pedagogical approaches, with no choice than an emergency shift to virtual learning, creating several arguments associated with digital infrastructure, digital divide, digital literacy, technical and pedagogic skills of teachers, digital contents and so on. With this background, this study intended to assess the implementation mechanism of COVID-19-lockdown-induced emergency online learning at undergraduate level in Assam, its effectiveness, and finally the problems faced therein, from the perspective of students’ experience and satisfaction. Primary data were collected from 132 undergraduate students drawn at random from different provincialized colleges in Assam through using a questionnaire in Google Form structured on the basis of available literature. The result revealed that 46.21% students perceive emergency online learning partially effective, 34.09% as effective, and 19.70% as ineffective. Poor Internet connectivity, irregular electricity, high cost in data plans, lack of compatible devices, lack of conducive environment at home, lack of technical and pedagogic skills of teachers, insufficient students’ engagement and so on, turned out to be the main challenges of online learning. Thus, the findings sufficiently yield the need of collaborative effort to improve digital infrastructure, reduce cost involvement and well-structured teachers’ training on pedagogical and technical skills. Creating, adopting and evaluating innovative techniques and tools, student-friendly contents, and above all a positive attitude are next to none measures to be accelerated for inclusive and effective online delivery of higher education in India in coming days.
Analog Lagrange Coded Computing

A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is dispersed among the workers. Lagrange coded computing (LCC), proposed by Yu et al., leverages the well-known Lagrange polynomial to perform polynomial evaluation of the dataset in such a scenario in an efficient parallel fashion while keeping the privacy of data amidst possible collusion of workers. This solution relies on quantizing the data into a finite field, so that Shamir's secret sharing, as one of its main building blocks, can be employed. Such a solution, however, is not properly scalable with the size of dataset, mainly due to computation overflows. To address such a critical issue, we propose a novel extension of LCC to the analog domain, referred to as analog LCC (ALCC). All the operations in the proposed ALCC protocol are done over the infinite fields of \mathbb R/ \mathbb C but for practical implementations floating-point numbers are used. We characterize the privacy of data in ALCC, against any subset of colluding workers up to a certain size, in terms of the distinguishing security (DS) and the mutual information security (MIS) metrics. Also, the accuracy of outcome is characterized in a practical setting assuming operations are performed using floating-point numbers. Consequently, a fundamental trade-off between the accuracy of the outcome of ALCC and its privacy level is observed and is numerically evaluated. Moreover, we implement the proposed scheme to perform matrix-matrix multiplication over a batch of matrices. It is observed that ALCC is superior compared to the state-of-the-art LCC, implemented using fixed-point numbers, assuming both schemes use an equal number of bits to represent data symbols.
Co-Embedding Network Nodes and Hierarchical Labels with Taxonomy Based Generative Adversarial Networks

Network embedding aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. To this end, we propose TaxoGAN to co-embed network nodes and hierarchical labels, through a hierarchical network generation process. Particularly, TaxoGAN models the child labels and network nodes of each parent label in an individual embedding space while learning to transfer network proximity among the spaces of hierarchical labels through stacked network generators and embedding encoders. To enable robust and efficient model inference, we further develop a hierarchical adversarial training process. Comprehensive experiments and case studies on four real-world datasets of networks with hierarchical labels demonstrate the utility of TaxoGAN in improving network embedding on traditional tasks of node classification and link prediction, as well as novel tasks like conditional proximity search and fine-grained taxonomy layout.
Breast Cancer Histopathology Image Super-Resolution Using Wide-Attention GAN With Improved Wasserstein Gradient Penalty and Perceptual Loss

In the realm of image processing, enhancing the quality of the images is known as a superresolution problem (SR). Among SR methods, a super-resolution generative adversarial network, or SRGAN, has been introduced to generate SR images from low-resolution images. As it is of the utmost importance to keep the size and the shape of the images, while enlarging the medical images, we propose a novel super-resolution model with a generative adversarial network to generate SR images with finer details and higher quality to encourage less blurring. By widening residual blocks and using a self-attention layer, our model becomes robust and generalizable as it is able to extract the most important part of the images before up-sampling. We named our proposed model as wide-attention SRGAN (WA-SRGAN). Moreover, we have applied improved Wasserstein with a Gradient penalty to stabilize the model while training. To train our model, we have applied images from Camylon 16 database and enlarged them by 2×, 4×, 8×, and 16× upscale factors with the ground truth of the size of 256 × 256 × 3. Furthermore, two normalization methods, including batch normalization, and weight normalization have been applied and we observed that weight normalization is an enabling factor to improve metric performance in terms of SSIM. Moreover, several evaluation metrics, such as PSNR, MSE, SSIM, MS-SSIM, and QILV have been applied for having a comprehensive objective comparison with other methods, including SRGAN, A-SRGAN, and bicubial. Also, we performed the job of classification by using a deep learning model called ResNeXt-101 (32 × 8d) for super-resolution, high-resolution, and low-resolution images and compared the outcomes in terms of accuracy score. Finally, the results on breast cancer histopathology images show the superiority of our model by using weight normalization and a batch size of one in terms of restoration of the color and the texture details.
Free-form tumor synthesis in computed tomography images via richer generative adversarial network

The insufficiency of annotated medical imaging scans for cancer makes it challenging to train and validate data-hungry deep learning models in precision oncology. We propose a new richer generative adversarial network for free-form 3D tumor/lesion synthesis in computed tomography (CT) images. The network is composed of a new richer convolutional feature enhanced dilated–gated generator (RicherDG) and a hybrid loss function. The RicherDG has dilated–gated convolution layers to enable tumor-painting and to enlarge perceptive fields; and it has a novel richer convolutional feature association branch to recover multi-scale convolutional features especially from uncertain boundaries between tumor and surrounding healthy tissues. The hybrid loss function, which consists of a diverse range of losses, is designed to aggregate complementary information to improve optimization. We perform a comprehensive evaluation of the synthesis results on a wide range of public CT image datasets covering the liver, kidney tumors, and lung nodules. The qualitative and quantitative evaluations and ablation study demonstrated improved synthesizing results over advanced tumor synthesis methods.
Adaptive Pairwise Prediction-Error Expansion and Multiple Histograms Modification for Reversible Data Hiding

In recent years, high-dimensional histogram modification based reversible data hiding for images has drawn much attention among researchers. Pairwise prediction-error expansion (pairwise PEE) achieves great performance by exploiting the correlations among prediction-errors, in which a two-dimensional (2D) prediction-error histogram (PEH) is generated, and then modified based on a specific 2D modification mapping for reversible embedding. However, the used 2D mapping is fixed and heuristically designed without considering the image content, so the embedding performance of pairwise PEE can be further improved. To this end, the adaptive pairwise PEE (APPEE) is proposed in this paper to adaptively design the 2D mapping according to the distribution of 2D PEH, such that a better embedding performance is derived. To further improve the embedding performance, the proposed APPEE is extended from one single 2D PEH to multiple 2D PEHs generated based on different local complexities, in which multiple content-based 2D mappings are designed adaptively and individually for each 2D PEH. Experimental results show that the APPEE for either one single 2D PEH or multiple 2D PEHs significantly outperforms the conventional pairwise PEE. Simultaneously, the superiority of the proposed method is also experimentally verified compared with some other state-of-the-art works.