Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study of HBase Storage Architecture—A Systematic Literature Review

According to research, generally, 2.5 quintillion bytes of data are produced every day. About 90% of the world’s data has been produced in the last two years alone. The amount of data is increasing immensely. There is a fight to use and store this tremendous information effectively. HBase is the top option for storing huge data. HBase has been selected for several purposes, including its scalability, efficiency, strong consistency support, and the capacity to support a broad range of data models. This paper seeks to define, taxonomically classify, and systematically compare existing research on a broad range of storage technologies, methods, and data models based on HBase storage architecture’s symmetry. We perform a systematic literature review on a number of published works proposed for HBase storage architecture. This research synthesis results in a knowledge base that helps understand which big data storage method is an effective one.
E-GCN: graph convolution with estimated labels

G raph C onvolutional N etwork (GCN) has been commonly applied for semi-supervised learning tasks. However, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi-supervised learning tasks. In this paper, we propose a novel G raph C onvolutional N etwork with E stimated labels (E-GCN) for semi-supervised learning. The core design of E-GCN is to learn a suitable network topology for semi-supervised learning by linking both estimated labels and given labels in a centralized network framework. The major enhancement is that both given labels and estimated labels are utilized for the topology optimization in E-GCN, which assists the graph convolution implementation for unknown labels evaluation. Experimental results demonstrate that E-GCN is significantly better than s tate-o f-t he-a rt (SOTA) baselines without estimated labels.
Distributed Double Machine Learning with a Serverless Architecture

This paper explores serverless cloud computing for double machine learning. Being based on repeated cross-fitting, double machine learning is particularly well suited to exploit the high level of parallelism achievable with serverless computing. It allows to get fast on-demand estimations without additional cloud maintenance effort. We provide a prototype Python implementation DoubleML-Serverless for the estimation of double machine learning models with the serverless computing platform AWS Lambda and demonstrate its utility with a case study analyzing estimation times and costs.
A Hybrid Approach to Industrial Augmented Reality Using Deep Learning-Based Facility Segmentation and Depth Prediction

Typical AR methods have generic problems such as visual mismatching, incorrect occlusions, and limited augmentation due to the inability to estimate depth from AR images and attaching the AR markers onto physical objects, which prevents the industrial worker from conducting manufacturing tasks effectively. This paper proposes a hybrid approach to industrial AR for complementing existing AR methods using deep learning-based facility segmentation and depth prediction without AR markers and a depth camera. First, the outlines of physical objects are extracted by applying a deep learning-based instance segmentation method to the RGB image acquired from the AR camera. Simultaneously, a depth prediction method is applied to the AR image to estimate the depth map as a 3D point cloud for the detected object. Based on the segmented 3D point cloud data, 3D spatial relationships among the physical objects are calculated, which can assist in solving the visual mismatch and occlusion problems properly. In addition, it can deal with a dynamically operating or a moving facility, such as a robot—the conventional AR cannot do so. For these reasons, the proposed approach can be utilized as a hybrid or complementing function to existing AR methods, since it can be activated whenever the industrial worker requires handing of visual mismatches or occlusions. Quantitative and qualitative analyses verify the advantage of the proposed approach compared with existing AR methods. Some case studies also prove that the proposed method can be applied not only to manufacturing but also to other fields. These studies confirm the scalability, effectiveness, and originality of this proposed approach.
Politics and Open Science: How the European Open Science Cloud Became Reality (the Untold Story)

This article will document how the European Open Science Cloud (EOSC) emerged as one of the key policy intentions to foster Open Science (OS) in Europe. It will describe some of the typical, non-rational roadblocks on the way to implement EOSC. The article will also argue that the only way Europe can take care of its research data in a way that fits the European specificities fully, is by supporting EOSC.
Personal Data Access Control Through Distributed Authorization

This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts.These smart contracts, running on the Ethereum blockchain, implement access control lists and grant immutability, traceability and verifiability of the references to personal data, which is stored itself in a (possibly distributed) file system.A distributed authorization mechanism is devised, where trust from multiple network nodes is necessary to grant the access to the data.To this aim, two possible alternatives are described: a Secret Sharing scheme and Threshold Proxy Re-Encryption scheme.The performance of these alternatives is experimentally compared in terms of execution time.Threshold Proxy Re-Encryption appears to be faster in different scenarios, in particular when increasing message size, number of nodes and the threshold value, i.e. number of nodes needed to grant the data disclosure.
Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows

This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot’s ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.
Fuzzy Aggregated Topology Evolution for Cognitive Multi-tasks

Evolutionary optimization aims to tune the hyper-parameters during learning in a computationally fast manner. For optimization of multi-task problems, evolution is done by creating a unified search space with a dimensionality that can include all the tasks. Multi-task evolution is achieved via selective imitation where two individuals with the same type of skill are encouraged to crossover. Due to the relatedness of the tasks, the resulting offspring may have a skill for a different task. In this way, we can simultaneously evolve a population where different individuals excel in different tasks. In this paper, we consider a type of evolution called Genetic Programming (GP) where the population of genes have a tree-like structure and can be of different lengths and hence can naturally represent multiple tasks. We apply the model to multi-task neuroevolution that aims to determine the optimal hyper-parameters of a neural network such as number of nodes, learning rate, and number of training epochs using evolution. Here each gene is encoded with the hyper parameters for a single neural network. Previously, optimization was done by enabling or disabling individual connections between neurons during evolution. This method is extremely slow and does not generalize well to new neural architectures such as Seq2Seq. To overcome this limitation, we follow a modular approach where each sub-tree in a GP can be a sub-neural architecture that is preserved during crossover across multiple tasks. Lastly, in order to leverage on the inter-task covariance for faster evolutionary search, we project the features from both tasks to common space using fuzzy membership functions. The proposed model is used to determine the optimal topology of a feed-forward neural network for classification of emotions in physiological heart signals and also a Seq2seq chatbot that can converse with kindergarten children. We can outperform baselines by over 10% in accuracy.
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition

Powered by innovations in novel architecture design, noise tolerance techniques and increasing model capacity, Automatic Speech Recognition (ASR) made giant strides in improving prediction accuracies over the past decade. ASR models are often trained with tens of thousand hours of high quality speech data to produce the state-of-the-art results. Industry-scale ASR model-training thus remains as a computationally heavy and time-consuming procedure, and consequently attracted little attention thus far in adopting automatic techniques in exploring neural architecture variations. Neural Architecture Search (NAS), on the other hand, gained a lot of interest in the past few years for its ability in discovering state-of-the-art architectures mainly in computer vision tasks. However, NAS approaches also suffer from the requirement of a large-scale computing infrastructure to support training of a massive number of neural networks and are often difficult to reproduce. Lately, a number of attempts have been made to ameliorate the computational problem and improve the search turnaround time of NAS algorithms by introducing benchmark datasets like NAS-Bench-101, NAS-Bench-201, and NAS-NLP. These datasets, however, focus predominantly on computer vision and NLP tasks and thus suffer from the problem of limited coverage of application domains. In this work we apply NAS for finding cell architecture for ASR models and release a comprehensive NAS-Bench dataset for reproducible NAS research. The dataset consists of 8,242 unique ASR models trained on the TIMIT audio dataset, each starting from 3 seed initializations. Novelty of our dataset includes consideration for on-device deployability and inclusion of runtime measures of all the models on diverse hardware platforms and settings. We further evaluate performances of a number of NAS algorithms on our dataset. Finally, we show that cells in our search space for TIMIT transfer well to a much larger LibriSpeech dataset.
Analysis on the e-Learning Method in Malaysia with AHP-VIKOR Model

A lot of educational institutions are facing the problem in conducting the physical classes due to COVID-19 pandemic recently. Therefore, most of the medium of teaching has been changed from a face-to-face method to online teaching. These changes may create some challenges to the students as well as the teachers or lecturers. In fact, different learning methods have their own strengths and weaknesses. Therefore, this study aims to analyze the preference of E-learning method among the undergraduate students based on multiple criteria. This study determines the priority of decision criteria in the selection of learning methods among the undergraduate students using Analytic Hierarchy Process-VIseKriterijumska Optimizacija I Kompromisno Resenje (AHP-VIKOR) model. The decision criteria that considered in this study are quality management system, information quality, flexibility, learning and teaching as well as attractiveness. Besides, blended learning, distance learning, and e-learning are identified as the alternatives of the learning methods as well as the most preferred learning method is determined in this study. The results of this study show that the most preferred learning method among undergraduate students is E-learning, followed by distance learning, and finally blended learning. Flexibility, information quality, and quality management system are ranked as the top three influential decision criteria. The significance of this study is to determine the most preferred learning method as well as the most influential decision criteria in the selection of learning methods among undergraduate students with AHP-VIKOR model.
Complexity of Quantum State Verification in the Quantum Linear Systems Problem

We analyze the complexity of quantum state verification in the context of solving systems of linear equations of the form Ax→=b→. We show that any quantum operation that verifies whether a given quantum state is within a constant distance from the solution of the quantum linear systems problem requires q=Ω(κ) uses of a unitary that prepares a quantum state |b⟩, proportional to b→, and its inverse in the worst case. Here, κ is the condition number of matrix A. For typical instances, we show that q=Ω(κ) with high probability. These lower bounds are almost achieved if quantum state verification is performed using known quantum algorithms for the quantum linear systems problem. We also analyze the number of copies of |b⟩ required by verification procedures of the prepare-and-measure type. In this case, the lower bounds are quadratically worse, being Ω(κ2) in the worst case and Ω(κ) in typical instances with high probability. We discuss the implications of our results to known variational and related approaches to this problem, where state preparation, gate, and measurement errors will need to decrease rapidly with κ for worst-case and typical instances if error correction is not used, and present some open problems.Received 14 August 2020Accepted 16 December 2020DOI:https://doi.org/10.1103/PRXQuantum.2.010315Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum Information
Social-Ecologic Oriented Curricula in Engineering Education: “Leonardo’s Oath” as an Answer to Janus-Headedness in Engineering Work

Any engineering activity is characterized by contradictions: social – between engineering problems and technical solutions and technological – between laws of nature and artificial engineering objects. This leads to the ethical dilemma of engineering work: when creating something new, social and environmental problems usually arise. This explains the “Janus-headedness” of the engineering profession: although the original intention is to do good, however, there are negative consequences that must be mitigated through new technical solutions that have the effect of creating improvements. “Leonardo’s oath” not only highlights this need, but also serves as a reference point for developing the engineering training programs which should be prepared specifically for this task. As a starting approach, the article suggests using the “sustainability triangle” – a model of social transformational effects in relation to the definition of the variables “social”, “economy” and “ecology” and their corresponding interdependence. It is shown that digitalization changes the “economy” setting in the sustainability triangle, which inevitably leads to consequences for the currently dependent variables “social “ and “ecology”. Digitalization requires, first, an economic approach, not environmental. It does not solve the fundamental problem: participation in the formation of a society to achieve socio-ecological balance is faced with the duality between the ethical necessity of forming and lack of real balance.
A Scalable Voting System: Validation of Holographic Consensus in DAOstack

Blockchain technology allows for new forms of online governance relying on decentralized infrastructure.In particular, it has enabled the so called Decentralized Autonomous Organizations (DAOs): pieces of software deployed on a blockchain which mediate the interaction of groups of people.Allegedly, their main aim is to facilitate large-scale decentralized cooperation in online communities.In order to do that, they provide governance mechanisms for democratic decision-making in communities.One of them is "Holographic Consensus", a voting mechanism intended to filter relevant proposals for large communities.To which extent is Holographic Consensus working as intended, facilitating effective scalable cooperation?In order to validate this method, we analyze the 22 DAO communities and 6000 users from the DAOstack platform, which uses Holographic Consensus in all its DAOs.Our results show that this mechanisms seems to follow the expected behavior, facilitating scalable decision-making in large DAOs.
DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents

Creating presentation materials requires complex multimodal reasoning skills to summarize key concepts and arrange them in a logical and visually pleasing manner. Can machines learn to emulate this laborious process? We present a novel task and approach for document-to-slide generation. Solving this involves document summarization, image and text retrieval, slide structure and layout prediction to arrange key elements in a form suitable for presentation. We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner. Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides. To help accelerate research in this domain, we release a dataset about 6K paired documents and slide decks used in our experiments. We show that our approach outperforms strong baselines and produces slides with rich content and aligned imagery.
Path Planning Algorithm of Dijkstra-Based Intelligent Aircraft under Multiple Constraints

Aiming at the rapid planning of the optimal flight path of the intelligent aircraft, considering the error constraints and correction probability constraints, a model for intelligent aircraft path planning under multiple constraints is constructed, and a global search algorithm based on Dijkstra algorithm is proposed to solve the model. By calculating the residual error and restricts flight distance, the basic Dijkstra algorithm is improved to make it more adaptable to solve the path planning under multiple constraints. At the same time, simulation experiment is conducted with the optimal goal of the shortest track length and satisfying the error constraints. The experimental results show that the aircraft passed a total of 18 correction points when it reached the destination. The total track length was 144 287.932 m, the vertical position error was 17.254 units, and the horizontal position error was 6.420 units. The results meet the error requirements. The results show that the intelligent aircraft path planning model and Dijkstra-based global search algorithm with multiple constraints are reasonable in solving such problems.
Graph Drawing via Gradient Descent, $$(GD)^2$$

Readability criteria, such as distance or neighborhood preservation, are often used to optimize node-link representations of graphs to enable the comprehension of the underlying data. With few exceptions, graph drawing algorithms typically optimize one such criterion, usually at the expense of others. We propose a layout approach, Graph Drawing via Gradient Descent, $$(GD)^2$$ , that can handle multiple readability criteria. $$(GD)^2$$ can optimize any criterion that can be described by a smooth function. If the criterion cannot be captured by a smooth function, a non-smooth function for the criterion is combined with another smooth function, or auto-differentiation tools are used for the optimization. Our approach is flexible and can be used to optimize several criteria that have already been considered earlier (e.g., obtaining ideal edge lengths, stress, neighborhood preservation) as well as other criteria which have not yet been explicitly optimized in such fashion (e.g., vertex resolution, angular resolution, aspect ratio). We provide quantitative and qualitative evidence of the effectiveness of $$(GD)^2$$ with experimental data and a functional prototype: http://hdc.cs.arizona.edu/~mwli/graph-drawing/ .
Fringe Order Correction for Fringe Projection Profilometry Based on Robust Principal Component Analysis

Temporal phase unwrapping is among the most robust and efficient phase unwrapping methods in fringe projection profilometry.They can recover the fringe orders even in the presence of surface discontinuities.However, fringe order errors may occur due to phase noise and poor measurement conditions.Such errors often exhibit an impulsive nature and introduce errors to the absolute phase map.Most existing fringe order error correction methods detect and correct the errors in a pixel-by-pixel manner, which may under-utilize the correlation of the fringe orders on different pixels.In this paper, we propose a new method to cope with the fringe order errors associated with temporal phase unwrapping.By exploiting the low-rankness of the fringe order map and sparse nature of the impulsive fringe order errors, we develop a robust principal component analysis (RPCA)-based approach to remove the impulsive fringe order errors.Experiments demonstrate that the proposed method is valid in eliminating the fringe order errors.
Neural Machine Translation With Explicit Phrase Alignment

While neural machine translation has achieved state-of-the-art translation performance, it is unable to capture the alignment between the input and output during the translation process. The lack of alignment in neural machine translation models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints. These problems not only increase the difficulty of designing new architectures for neural machine translation, but also limit its applications in practice. To alleviate these problems, we propose to introduce explicit phrase alignment into the translation process of arbitrary neural machine translation models. The key idea is to build a search space similar to that of phrase-based statistical machine translation for neural machine translation where phrase alignment is readily available. We design a new decoding algorithm that can easily impose lexical and structural constraints. Experiments show that our approach makes the translation process of neural machine translation more interpretable without sacrificing translation quality. In addition, our approach achieves significant improvements in lexically and structurally constrained translation tasks.