Reviewerly/exHarmony huggingface dataset:
MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution

Convolutional neural networks have been proven to be of great benefit for single-image super-resolution (SISR). However, previous works do not make full use of multi-scale features and ignore the inter-scale correlation between different upsampling factors, resulting in sub-optimal performance. Instead of blindly increasing the depth of the network, we are committed to mining image features and learning the inter-scale correlation between different upsampling factors. To achieve this, we propose a Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time. MDCN consists of multi-scale dense cross blocks (MDCBs), hierarchical feature distillation block (HFDB), and dynamic reconstruction block (DRB). Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model. It is worth noting that all these modules can run independently. It means that these modules can be selectively plugged into any CNN model to improve model performance. Extensive experiments show that MDCN achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors. The code is provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MIVRC/MDCN-PyTorch</uri> .
A new health care system enabled by machine intelligence: Elderly people's trust or losing self control

An autonomous homecare system can ensure independence for elderly people and increase cooperation, social interaction, and adaptation. Widespread diffusion and inclusion of information and communication technology (ICT) in modern equipment has advanced the concept of machine autonomy. This study seeks to understand the effect of trust required for elderly people to accept autonomous homecare systems instead of human support. It attempts to reveal how trust and personal characteristics can increase intent to adopt an autonomous system. In this regard, different trust models and literature on human psychology to adopt ICT driven system are explored and analyzed to develop a parsimonious trust disposition model for autonomous system. The study was conducted among elderly and disabled people in retirement homes and rehabilitation centers of different major cities of Ontario, Canada through random sampling by employing both experiment and survey. In the first phase, a structured and designed experiment was conducted in three retirement homes which included 159 elderly people and in two rehabilitation centers which included 20 disabled people. Then a survey-based empirical study was conducted among the same people with the structured questionnaire to answer the questions based on their perceptions of both receiving service from human beings and viewing the video about an autonomous system governed by ambient intelligence. This study reveals that elderly people can be motivated to develop trust in this less-familiar system if they both believe they can operate it and find a sense of belongingness and feelings of social interactivity from this seemingly living machine system. The findings of this study provide clear direction for academics and practitioners. This research indicates that elderly people must have control on using any system in the absence of human support while maintaining a more solitary life in a retirement home. However, the system must be easy to learn and operate. It should also be designed in such a way that can create and impart social feelings.
Exploring global diverse attention via pairwise temporal relation for video summarization

Video summarization is an effective way to facilitate video searching and browsing. Most of existing systems employ encoder-decoder based recurrent neural networks, which fail to explicitly diversify the system-generated summary frames while requiring intensive computations. In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames. Particularly, the GDA module has two advantages: (1) it models the relations within paired frames as well as the relations among all pairs, thus capturing the global attention across all frames of one video; (2) it reflects the importance of each frame to the whole video, leading to diverse attention on these frames. Thus, SUM-GDA is beneficial for generating diverse frames to form satisfactory video summary. Extensive experiments on three data sets, i.e., SumMe, TVSum, and VTW, have demonstrated that SUM-GDA and its extension outperform other competing state-of-the-art methods with remarkable improvements. In addition, the proposed models can be run in parallel with significantly less computational costs, which helps the deployment in highly demanding applications.
Binary Whale Optimization Algorithm for Dimensionality Reduction

Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the data. In order to overcome this problem, two binary variants of the whale optimization algorithm (WOA) are proposed, called bWOA-S and bWOA-V. They are used to decrease the complexity and increase the performance of a system by selecting significant features for classification purposes. The first bWOA-S version uses the Sigmoid transfer function to convert WOA values to binary ones, whereas the second bWOA-V version uses a hyperbolic tangent transfer function. Furthermore, the two binary variants introduced here were compared with three famous and well-known optimization algorithms in this domain, such as Particle Swarm Optimizer (PSO), three variants of binary ant lion (bALO1, bALO2, and bALO3), binary Dragonfly Algorithm (bDA) as well as the original WOA, over 24 benchmark datasets from the UCI repository. Eventually, a non-parametric test called Wilcoxon’s rank-sum was carried out at 5% significance to prove the powerfulness and effectiveness of the two proposed algorithms when compared with other algorithms statistically. The qualitative and quantitative results showed that the two introduced variants in the FS domain are able to minimize the selected feature number as well as maximize the accuracy of the classification within an appropriate time.
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
PaGraph

Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data. By leveraging GPU accelerators, existing frameworks combine both mini-batch and sampling for effective and efficient model training on large graphs. However, this setup faces a scalability issue since loading rich vertices features from CPU to GPU through a limited bandwidth link usually dominates the training cycle. In this paper, we propose PaGraph, a system that supports general and efficient sampling-based GNN training on single-server with multi-GPU. PaGraph significantly reduces the data loading time by exploiting available GPU resources to keep frequently accessed graph data with a cache. It also embodies a lightweight yet effective caching policy that takes into account graph structural information and data access patterns of sampling-based GNN training simultaneously. Furthermore, to scale out on multiple GPUs, PaGraph develops a fast GNN-computation-aware partition algorithm to avoid cross-partition access during data parallel training and achieves better cache efficiency. Evaluations on two representative GNN models, GCN and GraphSAGE, show that PaGraph achieves up to 96.8% data loading time reductions and up to 4.8X performance speedup over the state-of-the-art baselines. Together with preprocessing optimization, PaGraph further delivers up to 16.0X end-to-end speedup.
Recursive Neural Network for Video Deblurring

Video deblurring is still a challenging low-level vision task since spatio-temporal characteristics across both the spatial and temporal domains are difficult to model. In this article, to model the temporal information, we develop a non-local block which estimates inter-frame similarity and inter-frame difference. Specially, for modeling the spatial characteristics and restoring sharp frame details, we propose a recursive block that iteratively refines feature maps generated at the last iteration. In addition, a novel temporal loss function is introduced to ensure the temporal consistency of generated frames. Experimental results on public datasets demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively.
Blockchain-Based Trust Management Model for Location Privacy Preserving in VANET

Vehicular ad hoc network(VANET) is a special mobile ad hoc network (MANET) which plays an important role in the intelligent traffic system(ITS). Based on the high mobility of VANETs, the security problems have not been reasonably solved when we enjoy the convenience brought by the Location Based Service(LBS). We present a blockchain-based trust management model for location privacy preserving. The scheme allows vehicles to use certificate to request LBS without revealing their privacy information. We construct anonymous cloaking region to ensure the privacy security of vehicles. We propose a trust management algorithm to constrain and standardize the behavior of vehicles, and use blockchain to implement the data security of vehicles. In the experiments, we conduct the tests with various data sets. Security analysis and experiments show that the system is resilient to sorts of trust model attacks, which can better preserve the privacy security of vehicles. Simulation results reveal that the proposed system is effective and feasible in collect.
Machine Learning Conservation Laws from Trajectories

We present AI Poincar\'e, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian systems, including the gravitational 3-body problem, and find that it discovers not only all exactly conserved quantities, but also periodic orbits, phase transitions and breakdown timescales for approximate conservation laws.
Technology-related teaching skills and attitudes: Validation of a scenario-based self-assessment instrument for teachers

Instruments that assess teachers' skills and attitudes on the basis of a broad range of specific standards and demands for teaching with digital technologies are lacking to date. Based on the K19 framework, we validated the scenario-based instrument IN.K19 that simultaneously assesses technology-related teaching skills and attitudes via self-assessment. In our study with N = 90 teachers and student teachers with teaching experience, we demonstrate that the instrument has satisfactory factorial validity in our confirmatory factor analyses. To investigate its predictive validity, we examined the instruments' relationships with teachers' frequency of technology use in class and teachers' initiation of different types of student learning activities involving technology. Results from structural equation modelling show relationships between self-assessed skills in different phases of teaching with technology and the self-reported initiation of student learning activities involving overt actions (active, constructive, and interactive learning activities), supporting the predictive validity of our instrument. Positive attitudes towards technology-related teaching also exhibit positive relationships with the initiation of learning activities involving digital technologies, but more specifically learning activities that do not include observable actions by learners (passive learning activities). Thus, teachers' self-assessed technology-related skills rather than attitudes might contribute to facilitating learning activities crucial for students’ learning.
UDapter: Language Adaptation for Truly Universal Dependency Parsing

Recent advances in multilingual dependency parsing have brought the idea of a truly universal parser closer to reality.However, crosslanguage interference and restrained model capacity remain major obstacles.To address this, we propose a novel multilingual task adaptation approach based on contextual parameter generation and adapter modules.This approach enables to learn adapters via language embeddings while sharing model parameters across languages.It also allows for an easy but effective integration of existing linguistic typology features into the parsing network.The resulting parser, UDapter, outperforms strong monolingual and multilingual baselines on the majority of both high-resource and lowresource (zero-shot) languages, showing the success of the proposed adaptation approach.Our in-depth analyses show that soft parameter sharing via typological features is key to this success.
A blockchain-based architecture for secure and trustworthy operations in the industrial Internet of Things

The industrial Internet of Things (IIoT) plays an important role in the industrial sector, where secure, scalable, and easily adopted technologies are being implemented for the smart industry. The traditional IIoT architectures are generally based on centralized architectures that are vulnerable to a single point of failure and to several cyber-attacks. Blockchain technology is frequently adopted in the modern industry because of its security and decentralization. This paper proposes a blockchain-based architecture that ensures secure and trustworthy industrial operations. A private and lightweight blockchain architecture is proposed to regulate access to valuable sensor and actuator data. To enhance the computational performance of the proposed architecture, real-time cryptographic algorithms are processed using a low-power ARM Cortex-M4 processor, and a highly scalable, fast, and energy-efficient consensus mechanism proof of authentication (PoAh) is deployed in the blockchain network. Extensive experiments and analysis proved the effectiveness of the proposed framework for smart industrial environments. Finally, we transform a conventional fruit processing plant into a secure and smart industrial platform by implementing the proposed architecture.
Data structures based on <i>k</i>-mers for querying large collections of sequencing data sets

High-throughput sequencing data sets are usually deposited in public repositories (e.g., the European Nucleotide Archive) to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow one to perform online sequence searches, yet, such a feature would be highly useful to investigators. Toward this goal, in the last few years several computational approaches have been introduced to index and query large collections of data sets. Here, we propose an accessible survey of these approaches, which are generally based on representing data sets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.
A Novel Data Placement Strategy for Data-Sharing Scientific Workflows in Heterogeneous Edge-Cloud Computing Environments

The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.
Postdigital Ecopedagogies: Genealogies, Contradictions, and Possible Futures

This paper charts some genealogies, challenges, and directions for experimenting with the utopic postdigital ecopedagogies demanded by our present (post)pandemic reality. These are messianic—rather than prophetic—utopias that exist not as proclamations or programmes for a distant future but as potentialities immanent in the irreducible excess of the present. While their roots most clearly emanate from the Freirean-inspired ecopedagogy movement, we conceptualize ecopedagogies instead as educational forms that emerge from, negotiate, debate, produce, resist, and/or overcome the shifting and expansive postdigital ecosystems from and to which we write and think. These are expansive ecosystems of humans, postdigital machines, nonhuman animals, minerals, objects, and more; ecosystems that are overdetermined by new forms of ontological hierarchies and capitalism, imperialism, and settler-colonialism. By charting some of the potential lineages, directions, contradictions, and challenges—and by proposing potential lines of educational praxis—we lay a basis for reinvigorated fields of inquiry that moves beyond the existing postdigital literature on the current pandemic.
TSIT: A Simple and Versatile Framework for Image-to-Image Translation

We introduce a simple and versatile framework for image-to-image translation. We unearth the importance of normalization layers, and provide a carefully designed two-stream generative model with newly proposed feature transformations in a coarse-to-fine fashion. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network, permitting our method to scale to various tasks in both unsupervised and supervised settings. No additional constraints (e.g., cycle consistency) are needed, contributing to a very clean and simple method. Multi-modal image synthesis with arbitrary style control is made possible. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations. GitHub: https://github.com/EndlessSora/TSIT .
Surfing blockchain wave, or drowning? Shaping the future of distributed ledgers and decentralized technologies

Blockchain is a promising technology whose four TRs (TRaceability, TRacking, TRansparency, TRrust) features are bound to revolutionize material, information, financial flows and transactions inside and outside organisations. Many studies have been published showing the potential of this disruptive technology in many fields and this number is growing exponentially in recent years. This enormous amount of papers calls for a more systematic approach to analyse the overall trend in this research field. A bibliometric approach based on performance analysis and network analysis techniques is used to examine the evolution of blockchain technology research. Firstly, this paper contributes to the body of literature by discussing the most influential countries, authors, subject areas and journals of the current blockchain research. Secondly, this paper identifies six main clusters of blockchain-related research contributions and, based on the analysis on centrality and density measures, it classifies research themes in motor themes, basic themes, emerging or disappearing themes, and specialised themes. Despite the majority of contributions belong to the computer science subject area, many papers belonging to the technology management subject area provide pivotal insights for practitioners and policy makers. Specifically, they may exploit the results of this research to rethink many traditional processes in the light of blockchain technology implementation, exploit the benefits of the four TRs to manage processes, automate common tasks, generate actionable results, and improve daily operations.
Genetic algorithm based adaptive histogram equalization (GAAHE) technique for medical image enhancement

In Magnetic Resonance Imaging (MRI), the poor quality images may not provide the sufficient information for the visual interpretation of the affected locations of human body. So, to improve the image visions and to provide computational support, a novel adaptive image enhancement technique has been proposed in this paper, named as genetic algorithm based adaptive histogram equalization (GAAHE) technique. The proposed framework includes genetic algorithm, histogram sub-division and modified probability density function (PDF). A novel approach of subdivision is applied to the histogram using the exposure threshold and optimal threshold for preserving the brightness and reducing the information loss. To make the proposed technique more adaptive, the threshold parameters are optimized by utilizing the concept of genetic algorithm, guided by the proposed multi-objective fitness function. Then, the PDF of each sub-histogram is modified to enhance the image quality. The experimental results show that, the proposed GAAHE technique performs superior over other existing enhancement techniques.