Reviewerly/exHarmony huggingface dataset:
A Conceptual Framework for HPC Operational Data Analytics

This paper provides a broad framework for understanding trends in Operational Data Analytics (ODA) for High-Performance Computing (HPC) facilities. The goal of ODA is to allow for the continuous monitoring, archiving, and analysis of near real-time performance data, providing immediately actionable information for multiple operational uses. In this work, we combine two models to provide a comprehensive HPC ODA framework: one is an evolutionary model of analytics capabilities that consists of four types, which are descriptive, diagnostic, predictive and prescriptive, while the other is a four-pillar model for energy-efficient HPC operations that covers facility, system hardware, system software, and applications. This new framework is then overlaid with a description of current development and production deployments of ODA within leading-edge HPC facilities. Finally, we perform a comprehensive survey of ODA works and classify them according to our framework, in order to demonstrate its effectiveness.
Climate change impact on the technical-economic potential for solar photovoltaic energy in the residential sector: a case study for Brazil

Climate change could affect solar photovoltaic power by altering climate variables, such as global horizontal irradiance, surface wind speed and surface air temperature. This study evaluates the potential for distributed solar photovoltaic power in the Brazilian residential sector under current climate and climate change scenarios. The applied methodology uses estimations for the number of residences country-wide and their available roof area, meteorological data, electricity tariffs, investment costs, financing conditions and climate projections to assess the technical and economic potential for solar photovoltaic power generation under current climate and 1.5°C, 2.0°C and 4.0°C Specific Warming Levels (SWL) scenarios. In the most aggressive climate change scenario, national technical and economic potentials would fall -0.84% and -0.008% compared to current climate, respectively. The South region would be the most affected, with reductions of -3.62% and -0.05% in this same scenario. Some municipalities in the Brazilian North and Northeast could even experience increases in solar PV power output under climate change scenarios, albeit at the cost of large impacts in other sectors not considered here (e.g. increased energy demand, impact on other renewable energy sources and price volatility induced by extreme weather events). Therefore, distributed solar photovoltaic potential in the Brazil is robust to climate change and can thus be regarded as an important source to conciliate climate change mitigation and adaptation.
An Image Encryption Algorithm Using Logistic Map with Plaintext-Related Parameter Values

This paper deals with a plaintext-related image encryption algorithm that modifies the parameter values used by the logistic map according to plain image pixel intensities. The parameter values are altered in a row-wise manner, which enables the usage of the same procedure also during the decryption. Furthermore, the parameter modification technique takes into account knowledge about the logistic map, its fixed points and possible periodic cycles. Since the resulting interval of parameter values achieves high positive values of Lyapunov exponents, the chaotic behavior of the logistic map should be most pronounced. These assumptions are verified by a set of experiments and the obtained numerical values are compared with those reported in relevant papers. It is found that the proposed design that uses a simpler, but well-studied, chaotic map with mitigated issues obtains results comparable with algorithms that use more complex chaotic systems. Moreover, the proposed solution is much faster than other approaches with a similar purpose.
MBRep: Motif-based representation learning in heterogeneous networks

• Considering networks’ heterogeneity in embedding methods is effective. • Triangle motifs preserve network heterogeneity and link directionality. • Atomic-level motif embedding overcomes manual intervention. • Higher-order heterogeneous connectivity patterns cope cold-start well. In recent years, there has been a surge of interest in applying machine learning to graphs and networks that already exist in the world around us. The approach has been successfully used for domains as diverse as traffic management, e-commerce recommendation and public opinion monitoring. A critical aspect of representation learning for applied machine learning is feature engineering. Deep learning-based research in representation learning has developed methods for automatically learning a large number of potentially correlated features from original networks. However, most of these methods cannot be applied to heterogeneous networks, which are true expressions of the real-world. This is because they do not adequately capture the structure and semantics of different types of nodes in heterogeneous networks and the links between them. They also struggle to represent higher-order heterogeneous patterns of connection. This paper proposes a generalized motif-based higher-order representation learning method, MBRep, that learns triangle motif embedding in a network, on the basis of which it can obtain the embedding and representation of nodes in a heterogeneous network. Statistically, significant motif structures are extracted from the original heterogeneous network and combined with the heterogeneity of the nodes. A weight-biased random walk is then applied to the motif level higher-order network, using a SkipGram model to embed the motifs. After this, the embedding of the original network nodes is calculated using weighted averages and feature alignment. This can then be used for link prediction. We confirmed the effectiveness of MBRep by comparing its AUC and MRR performance with other state-of-the-art methods on three real-world datasets. Its adaptability was also validated by conducting a cold-start test.
Multi-task BERT for Aspect-based Sentiment Analysis

Social media data are increasingly used for smart computing applications, e.g., social event detection and sentiment analysis. Sentiment analysis, an important natural language processing task, has been applied in many real-world applications such as recommender systems and intelligence business systems. To process such social media data, natural language processing techniques such as BERT can be applied to extract essential language representations and produce state-of-the-art results. In this paper, we utilize the pre-trained BERT model as the backbone network and propose the BERT-SAN model to perform aspect-based sentiment analysis. The result demonstrates that our proposed model has a significant improvement against other baselines.
Cloud-Based Blockchains for Secure and Reliable Big Data Storage Service in Healthcare Systems

Due to regulations and policies enacted to protect patients' privacy, electronic health records (EHRs) must be kept as sensitive information in a secure and reliable manner. Maintaining EHRs by hospitals and safely sharing them with others have proved to be challenging tasks. In this paper, we introduce a cloud-based blockchain (CBC) approach to achieving data accessibility, redundancy, and security for storing and sharing EHRs. A lightweight CBC, called lite blockchain (LBC), is designed to store EHRs' metadata and text-based information locally. Our approach allows for big data to be safely stored in CBCs, and for information to be efficiently retrieved via LBCs. To restrict and grant access of the data to authorized participants only, we present our data security mechanism along with role-based access control policies. The experimental results show that our cloud-based blockchain approach is feasible and efficient for accessing and sharing EHRs stored both locally and in clouds.
Iterative Feature Matching for Self-Supervised Indoor Depth Estimation

In this paper, we propose an iterative feature matching framework for self-supervised depth estimation in indoor scenes. Conventional methods usually leverage the structure-from-motion supervision to help the photometric optimization escape from the local minima, which have complex ego-motion and large regions with non-texture or repeated-texture. However, the supervision is limited as the reconstruction is usually sparse. To address this, we propose an iterative feature matching framework called IFMNet to jointly learn depths and search for correspondences. With the predicted depths from the previous iteration, we present an online optimized grid searching algorithm to find more accurate correspondences. Given these new correspondences, we compute the triangulated depths and improve the depth network with adaptive bin-wise online hard example mining. Experimental results on the NYU Depth V2 and SceneNet datasets verify the effectiveness of our approach.
Multi-Robot Task Allocation Games in Dynamically Changing Environments

We propose a game-theoretic multi-robot task allocation framework that enables a large team of robots to optimally allocate tasks in dynamically changing environments. As our main contribution, we design a decision-making algorithm that defines how the robots select tasks to perform and how they repeatedly revise their task selections in response to changes in the environment. Our convergence analysis establishes that the algorithm enables the robots to learn and asymptotically achieve the optimal stationary task allocation. Through experiments with a multi-robot trash collection application, we assess the algorithm’s responsiveness to changing environments and resilience to failure of individual robots.
Unsupervised Image Deraining: Optimization Model Driven Deep CNN

The deep convolutional neural network has achieved significant progress for single image rain streak removal. However, most of the data-driven learning methods are full-supervised or semi-supervised, unexpectedly suffering from significant performance drop when dealing with the real rain. These data-driven learning methods are representative yet generalize poor for real rain. The opposite holds true for the model-driven unsupervised optimization methods. To overcome these problems, we propose a unified unsupervised learning framework which inherits the generalization and representation merits for real rain removal. Specifically, we first discover a simple yet important domain knowledge that directional rain streak is anisotropic while the natural clean image is isotropic, and formulate the structural discrepancy into the energy function of the optimization model. Consequently, we design an optimization model driven deep CNN in which the unsupervised loss function of the optimization model is enforced on the proposed network for better generalization. In addition, the architecture of the network mimics the main role of the optimization models with better feature representation. On one hand, we take advantage of the deep network to improve the representation. On the other hand, we utilize the unsupervised loss of the optimization model for better generalization. Overall, the unsupervised learning framework achieves good generalization and representation: unsupervised training (loss) with only a few real rainy images (input) and physical meaning network (architecture). Extensive experiments on synthetic and real-world rain datasets show the superiority of the proposed method.
Design of an efficient hybridized CS-PSO algorithm and its applications for solving constrained and bound constrained structural engineering design problems

In the recent years, studies of structural engineering design problems are of keen interest among the researchers including engineers and algorithm developers. This article attempts to design an efficient hybridized algorithm for solving structural engineering design optimization problems. In this algorithm, after imposing some modifications on cuckoo search algorithm, the whole swarm or population is partitioned into two groups and then these are upgraded by the embedded cuckoo search (ECS) and a variant of PSO (AGQPSO) algorithms. After combining these two upgraded swarms, a deletion strategy of CS is applied with certain effective modifications. The performance of this hybridized algorithm is tested by considering six structural engineering design problems reported by several researchers and the errors made by some of them are pointed out. Finally, a comparison of the best found results and statistical analyses of this crossbred algorithm are performed with the same of existing algorithms available in the literature and it is shown that the presented algorithm performs better in almost all aspects. Also, to establish the efficiency of the proposed algorithm, convergence graphs and statistical tests are provided.
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input

From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating different tissue regions manually is a laborious, time-consuming and costly task that requires expert knowledge. On the other hand, the state-of-the-art automatic deep learning models for semantic segmentation require lots of annotated training data and there are only a limited number of tissue region annotated images publicly available. To obviate this issue in computational pathology projects and collect large-scale region annotations efficiently, we propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. The user is only required to draw a simple squiggle inside each region of interest so it will be used as the guiding signal for the model. To deal with the complex appearance and amorph geometry of different tissue regions we introduce several automatic and minimalistic guiding signal generation techniques that help the model to become robust against the variation in the user input. By experimenting on a dataset of breast cancer images, we show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.
Towards Robust One-shot Task Execution using Knowledge Graph Embeddings

Requiring multiple demonstrations of a task plan presents a burden to end-users of robots. However, robustly executing tasks plans from a single end-user demonstration is an ongoing challenge in robotics. We address the problem of one-shot task execution, in which a robot must generalize a single demonstration or prototypical example of a task plan to a new execution environment. Our approach integrates task plans with domain knowledge to infer task plan constituents for new execution environments. Our experimental evaluations show that our knowledge representation makes more relevant generalizations that result in significantly higher success rates over tested baselines. We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
TwinVisor

Confidential VM, which offers an isolated execution environment for cloud tenants with limited trust in the cloud provider, has recently been deployed in major clouds such as AWS and Azure. However, while ARM has become increasingly popular in cloud data centers, existing confidential VM designs mainly leverage specialized x86 hardware extensions (e.g., AMD SEV and Intel TDX) to isolate VMs upon a shared hypervisor.
Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation

Modern navigation algorithms based on deep reinforcement learning (RL) show promising efficiency and robustness. However, most deep RL algorithms operate in a risk-neutral manner, making no special attempt to shield users from relatively rare but serious outcomes, even if such shielding might cause little loss of performance. Furthermore, such algorithms typically make no provisions to ensure safety in the presence of inaccuracies in the models on which they were trained, beyond adding a cost-of-collision and some domain randomization while training, in spite of the formidable complexity of the environments in which they operate. In this paper, we present a novel distributional RL algorithm that not only learns an uncertainty-aware policy, but can also change its risk measure without expensive fine-tuning or retraining. Our method shows superior performance and safety over baselines in partially- observed navigation tasks. We also demonstrate that agents trained using our method can adapt their policies to a wide range of risk measures at run-time.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data

Error-bounded lossy compression is becoming an indispensable technique for the success of today’s scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100% $\sim$ 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.
Intelligent assistive technology devices for persons with dementia: A scoping review

Assistive technology (AT) with context-aware computing and artificial intelligence capabilities can be applied to address cognitive and communication impairments experienced by persons with dementia (PwD). This paper aims to provide an overview of current literature regarding some characteristics of intelligent assistive technology devices (IATDs) for cognitive and communicative impairments of PwD. It also aims to identify the areas of impairment addressed by these IATDs.A multi-faceted systematic search strategy yielded records. Predefined criteria were applied for inclusion and data extraction. Thereafter data was thematically analysed and synthesised. This review demonstrates that almost all of the research involving IATDs has focused on cognitive impairments of PwD and has not yet evolved past the conceptual or prototype stages of development. Summaries of commercially available IATDs for PwD and relevant prototypes are provided at the end of this review.This research concluded that IATDs for PwD targeting cognition and communication problems primarily focus on social robots, and that they address cognitive impairments of attention, affect, and social-pragmatic communicative impairments. Future research endeavours concerning AT for PwD should explore collaboration between computer engineering and health practitioners to address the identified gaps. This may contribute to the available information for evidence-based decision making for PwD.
Sensor Dataglove for Real-time Static and Dynamic Hand Gesture Recognition

Hand gesture recognition has been a widely explored field of Human Activity Recognition (HAR). In this work, we have presented a sensor-based hand gesture recognition framework to classify both static and dynamic hand gestures in real-time using a dataglove that contains a 3-axis accelerometer (ACC), a 3-axis gyroscope, and 5 flex sensors. We have collected data from 35 volunteers performing 14 static and 3 dynamic gestures wearing the dataglove. We have preprocessed the raw flex sensor data using digital filtering techniques and performed mathematical operations on the accelerometer and gyroscope data for determining accurate orientation and motion profile. Four classical machine learning algorithms were used and compared on both datasets. We have achieved maximum accuracy of 99.53% for static gestures and 98.64% for dynamic gestures using the K-Nearest Neighbors (KNN) classifier. Our proposed framework provides real-time wireless hand gesture detection for Human-Computer Interaction (HCI) and Sign Language Recognition (SLR).
Blockchain-Based Solution for the Administration of Controlled Medication

Controlled drugs are open to abuse, misuse, and diversion. Therefore, they are regulated and tracked across the healthcare sector to protect the health of the general public which is a highly prioritized rule in the health professional’s code of ethics. Healthcare centers that provide controlled medication to patients are still using manual papers to record controlled drugs production, delivery, prescription, administration, and disposal which causes delays in the system. Moreover, instances of controlled drugs misuse, abuse, and diversion still exist, which shows how the currently used system is inefficient in detecting such activities. Therefore, to ensure that the public health is safe and secure, an end-to-end system that tracks the whole healthcare supply chain is necessary. In this paper, we introduce a private Ethereum blockchain-based solution for the management of controlled medication.We ensure transparency, accountability, security, and data provenance by developing smart contracts that record all actions on an immutable ledger. We utilize off-chain storage, which is represented in the IPFS to store content that is large in size such as images. We present algorithms of the different phases in the proposed solution to illustrate how each phase will be carried out. We showcase the functionality of the proposed solution by performing tests and validating the smart contracts. We assess the performance of the proposed solution by conducting privacy, security, and confidentiality analysis. Performance evaluation shows that our solution is secure against common attacks and vulnerabilities and preserves the privacy and confidentiality of the patients. The smart contracts code is made publicly available along with the testing scripts.