Reviewerly/exHarmony huggingface dataset:
Function delivery network: Extending serverless computing for heterogeneous platforms

Serverless computing has rapidly grown following the launch of Amazon's Lambda platform. Function-as-a-Service (FaaS) a key enabler of serverless computing allows an application to be decomposed into simple, standalone functions that are executed on a FaaS platform. The FaaS platform is responsible for deploying and facilitating resources to the functions. Several of today's cloud applications spread over heterogeneous connected computing resources and are highly dynamic in their structure and resource requirements. However, FaaS platforms are limited to homogeneous clusters and homogeneous functions and do not account for the data access behavior of functions before scheduling. We introduce an extension of FaaS to heterogeneous clusters and to support heterogeneous functions through a network of distributed heterogeneous target platforms called Function Delivery Network (FDN). A target platform is a combination of a cluster of homogeneous nodes and a FaaS platform on top of it. FDN provides Function-Delivery-as-a-Service (FDaaS), delivering the function to the right target platform. We showcase the opportunities such as varied target platform's characteristics, possibility of collaborative execution between multiple target platforms, and localization of data that the FDN offers in fulfilling two objectives: Service Level Objective (SLO) requirements and energy efficiency when scheduling functions by evaluating over five distributed target platforms using the FDNInspector, a tool developed by us for benchmarking distributed target platforms. Scheduling functions on an edge target platform in our evaluation reduced the overall energy consumption by 17x without violating the SLO requirements in comparison to scheduling on a high-end target platform.
Blockchain in human resource management of organizations: an empirical assessment to gauge HR and non-HR perspective

Purpose The purpose of this study is to understand the views of employees about the application of distributed ledger database technology blockchain, in area of human resource management (HRM) of organizations. The current study aims to understand the views of both HR and non-HR employees of how they assess the current scenario of HRM in their organizations, their awareness about the blockchain technology and their opinion about the scope of application of blockchain in HRM. Design/methodology/approach A sample of 158 employees was collected consisting of employees working in both HR and non-HR profiles across various organizations. Chi-square test of homogeneity, log-linear analysis and basic frequencies were used to analyze the data. Findings The results revealed that there was no difference in viewpoints of HR and non-HR employees across all contexts related to blockchain in HRM. The study also analyzed the opinion of employees regarding advantages, organizational barriers and probable usages of blockchain in HRM. Research limitations/implications The study will provide an insight to the organization decision-makers who are willing to roll out Industry 4.0 technology blockchain in HRM and beliefs of employees regarding acceptance of such change in organization. Originality/value This study will be a novel attempt to understand the scope of application of blockchain technology in HRM of organizations in Indian context.
Deep neural network-based fusion model for emotion recognition using visual data

Abstract In this study, we present a fusion model for emotion recognition based on visual data. The proposed model uses video information as its input and generates emotion labels for each video sample. Based on the video data, we first choose the most significant face regions with the use of a face detection and selection step. Subsequently, we employ three CNN-based architectures to extract the high-level features of the face image sequence. Furthermore, we adjusted one additional module for each CNN-based architecture to capture the sequential information of the entire video dataset. The combination of the three CNN-based models in a late-fusion-based approach yields a competitive result when compared to the baseline approach while using two public datasets: AFEW 2016 and SAVEE.
Digital Citizenship and Digital Literacy in the Conditions of Social Crisis

In the present day, Internet technology and social media totally dominate as a means of communication. The media and social interaction have a two-sided nature, as the important role is not only those of the media messages to the users, but of the users to the media, too. This article aims to present the dominant importance of digital media, digital literacy transformation into a precondition for social inclusion, and an indicator of professional competence and social skills. Digital citizenship is a term that reflects the level of training and competencies, with a view to active participation in social, professional, and civic life. The article is based on two methods: Focus groups that were conducted in late 2019, which includes: Students, young mothers, pensioners, and unemployed. The second method used is the documents analysis—publications, materials, and quantitative results of research on social reactions to digital media as a source of information in the context of the COVID-19 pandemic crisis. The combination of materials and data that have been analyzed are related to the period of the lockdown between March 2020 and December 2020. In the time of global social crises and confrontations, digital media literacy has turned out to be of critical importance for the normal course of social events and their interpretations. In this regard, digital citizenship contributes to social understanding and control, as well as the individual practices in the global pandemic trajectory.
Integrating Frequency Translational Invariance in TDNNs and Frequency Positional Information in 2D ResNets to Enhance Speaker Verification

This paper describes the IDLab submission for the text-independent task of the Short-duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker verification competition focuses on short duration test recordings and cross-lingual trials, along with the constraint of limited availability of in-domain DeepMine Farsi training data. Currently, both Time Delay Neural Networks (TDNNs) and ResNets achieve state-of-the-art results in speaker verification. These architectures are structurally very different and the construction of hybrid networks looks a promising way forward. We introduce a 2D convolutional stem in a strong ECAPA-TDNN baseline to transfer some of the strong characteristics of a ResNet based model to this hybrid CNN-TDNN architecture. Similarly, we incorporate absolute frequency positional encodings in an SE-ResNet34 architecture. These learnable feature map biases along the frequency axis offer this architecture a straightforward way to exploit frequency positional information. We also propose a frequency-wise variant of Squeeze-Excitation (SE) which better preserves frequency-specific information when rescaling the feature maps. Both modified architectures significantly outperform their corresponding baseline on the SdSVC-21 evaluation data and the original VoxCeleb1 test set. A four system fusion containing the two improved architectures achieved a third place in the final SdSVC-21 Task 2 ranking.
Do Syntax Trees Help Pre-trained Transformers Extract Information?

Much recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models.However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) remains unclear, especially given recent studies highlighting how these models implicitly encode syntax.In this work, we systematically study the utility of incorporating dependency trees into pretrained transformers on three representative information extraction tasks: semantic role labeling (SRL), named entity recognition, and relation extraction.We propose and investigate two distinct strategies for incorporating dependency structure: a late fusion approach, which applies a graph neural network on the output of a transformer, and a joint fusion approach, which infuses syntax structure into the transformer attention layers.These strategies are representative of prior work, but we introduce additional model design elements that are necessary for obtaining improved performance.Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks.However, our analysis also reveals a critical shortcoming of these models: we find that their performance gains are highly contingent on the availability of human-annotated dependency parses, which raises important questions regarding the viability of syntax-augmented transformers in real-world applications. 1
LaSS

Serverless computing has emerged as a new paradigm for running short-lived computations in the cloud. Due to its ability to handle IoT workloads, there has been considerable interest in running serverless functions at the edge. However, the constrained nature of the edge and the latency sensitive nature of workloads result in many challenges for serverless platforms. In this paper, we present LaSS, a platform that uses model-driven approaches for running latency-sensitive serverless computations on edge resources. LaSS uses principled queuing-based methods to determine an appropriate allocation for each hosted function and auto-scales the allocated resources in response to workload dynamics. LaSS uses a fair-share allocation approach to guarantee a minimum of allocated resources to each function in the presence of overload. In addition, it utilizes resource reclamation methods based on container deflation and termination to reassign resources from over-provisioned functions to under-provisioned ones. We implement a prototype of our approach on an OpenWhisk serverless edge cluster and conduct a detailed experimental evaluation. Our results show that LaSS can accurately predict the resources needed for serverless functions in the presence of highly dynamic workloads, and reprovision container capacity within hundreds of milliseconds while maintaining fair share allocation guarantees.
Path-encoded high-dimensional quantum communication over a 2-km multicore fiber

Abstract Quantum key distribution (QKD) protocols based on high-dimensional quantum states have shown the route to increase the key rate generation while benefiting of enhanced error tolerance, thus overcoming the limitations of two-dimensional QKD protocols. Nonetheless, the reliable transmission through fiber links of high-dimensional quantum states remains an open challenge that must be addressed to boost their application. Here, we demonstrate the reliable transmission over a 2-km-long multicore fiber of path-encoded high-dimensional quantum states. Leveraging on a phase-locked loop system, a stable interferometric detection is guaranteed, allowing for low error rates and the generation of 6.3 Mbit/s of a secret key rate.
Fast Federated Learning by Balancing Communication Trade-Offs

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient transmissions decelerate FL. To mitigate the communication overheads, two main techniques have been studied: (i) local update of weights characterizing the trade-off between communication and computation and (ii) gradient compression characterizing the trade-off between communication and precision. To the best of our knowledge, studying and balancing those two trade-offs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jointly and dynamically</i> while considering their impacts on convergence has remained unresolved even though it promises significantly faster FL. In this paper, we first formulate our problem to minimize learning error with respect to two variables: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local update coefficients</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparsity budgets</i> of gradient compression who characterize trade-offs between communication and computation/precision, respectively. We then derive an upper bound of the learning error in a given wall-clock time considering the interdependency between the two variables. Based on this theoretical analysis, we propose an enhanced FL scheme, namely Fast FL (FFL), that jointly and dynamically adjusts the two variables to minimize the learning error. We demonstrate that FFL consistently achieves higher accuracies faster than similar schemes existing in the literature.
Experimental quantum kernel trick with nuclear spins in a solid

We employ so-called quantum kernel estimation to exploit complex quantum dynamics of solid-state nuclear magnetic resonance for machine learning. We propose to map an input to a feature space by input-dependent Hamiltonian evolution, and the kernel is estimated by the interference of the evolution. Simple machine learning tasks, namely one-dimensional regression tasks and two-dimensional classification tasks, are performed using proton spins which exhibit correlation over 10 spins. We also performed numerical simulations to evaluate the performance without the noise inevitable in the actual experiments. The performance of the trained model tends to increase with the longer evolution time, or equivalently, with a larger number of spins involved in the dynamics for certain tasks. This work presents a quantum machine learning experiment using one of the largest quantum systems to date.
Context-Aware Holographic Communication Based on Semantic Knowledge Extraction

Abstract Augmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.
Adversarial Feature Translation for Multi-domain Recommendation

Real-world super platforms such as Google and WeChat usually have different recommendation scenarios to provide heterogeneous items for users' diverse demands. Multi-domain recommendation (MDR) is proposed to improve all recommendation domains simultaneously, where the key point is to capture informative domain-specific features from all domains. To address this problem, we propose a novel Adversarial feature translation (AFT) model for MDR, which learns the feature translations between different domains under a generative adversarial network framework. Precisely, in the multi-domain generator, we propose a domain-specific masked encoder to highlight inter-domain feature interactions, and then aggregate these features via a transformer and a domain-specific attention. In the multi-domain discriminator, we explicitly model the relationships between item, domain and users' general/domain-specific representations with a two-step feature translation inspired by the knowledge representation learning. In experiments, we evaluate AFT on a public and an industrial MDR datasets and achieve significant improvements. We also conduct an online evaluation on a real-world MDR system. We further give detailed ablation tests and model analyses to verify the effectiveness of different components. Currently, we have deployed AFT on WeChat Top Stories. The source code is in https://github.com/xiaobocser/AFT.
ImGAGN

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have shown promising performance on many network analysis tasks. However, most existing GNNs have almost exclusively focused on the balanced networks, and would get unappealing performance on the imbalanced networks. To bridge this gap, in this paper, we present a generative adversarial graph network model, called ImGAGN to address the imbalanced classification problem on graphs. It introduces a novel generator for graph structure data, named GraphGenerator, which can simulate both the minority class nodes' attribute distribution and network topological structure distribution by generating a set of synthetic minority nodes such that the number of nodes in different classes can be balanced. Then a graph convolutional network (GCN) discriminator is trained to discriminate between real nodes and fake (i.e., generated) nodes, and also between minority nodes and majority nodes on the synthetic balanced network. To validate the effectiveness of the proposed method, extensive experiments are conducted on four real-world imbalanced network datasets. Experimental results demonstrate that the proposed method ImGAGN outperforms state-of-the-art algorithms for semi-supervised imbalanced node classification task.
Normalization of HE-stained histological images using cycle consistent generative adversarial networks

Abstract Background Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques. Methods In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network G B that learns to map an image X from a source domain A to a target domain B , i.e. G B : X A → X B . In addition, a discriminator network D B is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair ( G A , D A ), for the inverse mapping G A : X B → X A . Cycle consistency ensures that a generated image is close to its original when being mapped backwards ( G A ( G B ( X A ))≈ X A and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set. Results Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%. Conclusions CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch . The data set supporting the solutions is available at 10.11588/data/8LKEZF .
Image smog restoration using oblique gradient profile prior and energy minimization

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.Supplementary material is available in the online version of this article at 10.1007/s11704-020-9305-8.
Quantum repeater architecture with hierarchically optimized memory buffer times

We propose a quantum repeater protocol and architecture that mitigates decoherence of the entangled states by optimizing the quantum memory buffer time. The protocol maximizes the rate of distillable entanglement in the average accessed state at all nesting levels. The achievable rate is higher by orders of magnitude in comparison to a canonical protocol that does not optimize the buffer time. The advantage of the proposed design is observed for all nesting levels of the repeater for technologically feasible memory quality, entanglement generation and swapping success probabilities.