Reviewerly/exHarmony huggingface dataset:
Coevolution of cyberinfrastructure development and scientific progress

Many nations have been investing significant funds in cyberinfrastructure for sciences (commonly known as “CI” or “e-science”) in the last few decades. The purpose of these investments has been to advance scientific progress in the supported scientific disciplines such as biodiversity and ecological research. However, there is scare research on how these CI investments impact scientific progress. We conducted a longitudinal case study of CI development and scientific progress based on the Long Term Ecological Research (LTER) program in the United States. We contribute to filling in the research gap by finding CI investments result in impacts both internal and external to the particular discipline of biodiversity. We also find a feedback effect from the discipline to CI development, such that the science and its supporting CI coevolve. The coevolution of CI development and the scientific discipline hinges on the “linking-pin” of data infrastructure. The large-scale data sharing within the discipline and across disciplines, in turn, is contingent on CI governance and how data are structured. The identification of the synergistic coevolution of CI and science enriches coevolutionary theories as well as CI development research. Accordingly, further research and policy changes relating to why and how to fund CI development for sciences, are necessary.
Evaluation of Optimized CNNs on Heterogeneous Accelerators using a Novel Benchmarking Approach

Numerous algorithmic optimization techniques have been proposed to alleviate the computational complexity of convolutional neural networks. Given the broad selection of AI accelerators, it is not obvious which approach benefits from which optimization most. The design space includes a large number of deployment settings (batch sizes, power modes, etc.) and unclear measurement methods. This research provides clarity into this design space, leveraging a novel benchmarking approach. We provide a theoretical evaluation of different CNNs and hardware platforms, focusing on understanding the impact of pruning and quantization as primary optimization techniques. We benchmark across a spectrum of FPGA, GPU, TPU, and VLIW processors for systematically pruned and quantized neural networks (ResNet50, GoogLeNetv1, MobileNetv1, a VGG derivative, a multilayer perceptron) over many deployment options, considering power, latency, and throughput at a specific accuracy. Our findings show that channel pruning is most effective and works across most hardware platforms, with speedups directly correlated to the reduction in compute load, while FPGAs benefit the most from quantization. Pruning and quantization are orthogonal, and yield optimal design points when combined. Further in-depth results can be found at our web portal, where we share all experimental data, provide data analytics, and invite the community to contribute.
Text Content Based Layout Analysis

State-of-the-art Document Layout Analysis methods rely on graphical appearance features in order to detect and classify the different layout regions present in a scanned text image. In many cases, however, performing this task using only graphical information is problematic or impossible. Only by actually reading some text in the boundaries of the problematic regions it becomes possible to reliably detect and separate these regions. In these situations, textual, content-based features would be required, but since transcription is usually performed after layout analysis, a vicious circle arises. In this work, we circumvent this deadlock by making use of the recently introduced concept of Probabilistic Index Map. We use the word relevance probabilities provided by this map to calculate relevant text content based features at the pixel level. We assess the impact of these new features on a historical document complex paragraph classification task. The experiments are performed using both a classical Hidden Markov Model approach and Deep Neural Networks. The obtained results are encouraging and showcase the positive impact text content based features will have on the Document Layout Analysis research field.
Feature Selection Embedded Robust K-Means

Clustering is one of the most important unsupervised learning problems in machine learning. As one of the most widely used clustering algorithms, K-means has been studied extensively. A number of more complicated and advanced clustering algorithms have been developed based on K-means. Moreover, K-means is often used as a final clustering step in many algorithms, such as subspace clustering, nonnegative matrix factorization, etc. However, for high-dimensional data, these algorithms generally use all features of the data, which often degrades the clustering performance due to the use of redundant and noisy information. Existing researches have demonstrated the importance of learning patterns with meaningful features, which inspires us to simultaneously discover useful features in the K-means framework. Thus, in this article, we incorporate feature selection into K-means framework. Moreover, to further enhance the clustering ability, we minimize the fitting residual with sparse norm and exploit the representation on manifold, which enhances the robustness to outliers, missing values, and noise and improves the ability to recover nonlinear structures of the data. We conducted extensive experiments to testify the effectiveness of the proposed method on gene expression and face image data sets. In particular, we compare the clustering performance with several state-of-the art algorithms on both original data and noisy data. We also analyze the convergence, parameter sensitivity, learned features, and computational time of the proposed method. From extensive experimental results, we observe superior clustering performance to the baseline methods, which implies the effectiveness of the proposed method.
Crowd Counting for Static Images: A Survey of Methodology

Crowd counting for static images is one of the typical application fields of image processing. it is essential to direct crowd analysis for public security purposes under the background of a rapidly growing social environment. Crowd counting for static images involves crucial challenges such as occlusions, scale variations, scene perspective distortions and diverse crowd distributions. It has now become an independent research hotspot. This paper investigates the literature of crowd counting tasks, summarizes the evolution of counting approaches, compares and analyzes traditional methods and current trends of CNN estimation-based counting, and then evaluates common datasets for crowd counting tasks. Conclusions concerning summarizations and prospects are made.
Accuracy of satellite-derived solar direct irradiance in Southern Spain and Switzerland

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Can digital personal assistants persuade people to exercise?

Digital personal assistants (DPAs) have recently grown in popularity because they are both a commercially available new technology and reasonably affordable to the average household. This opens opportunities for new ways to assist people in everyday activities in their homes through voice-interaction. Physical activity has significant health benefits, and yet globally, 1 in 4 adults are not active enough. To address this, we investigate the persuasive potential of DPAs in increasing people’s physical activity at home. We conducted a study with 48 participants to understand the effect of applying three of Fogg’s persuasive principles to the design of a DPA exercise programme: Suggestion, Virtual Reward, and Praise. Our findings show that DPAs have the potential, within their current technical and reactive capabilities, to persuade people to increase their physical activity at home, using Suggestion to encourage physical effort, Virtual Reward to encourage endurance, and Praise to create reassurance for beginners. Based on this, we offer three alternate perspectives for developing persuasive DPAs. We also discuss limitations of the study and suggest future research directions around using persuasion with DPAs.
Scene Target 3D Point Cloud Reconstruction Technology Combining Monocular Focus Stack and Deep Learning

In order to obtain the depth information of the target in the scene and realize three-dimensional (3D) reconstruction, in this paper, a target reconstruction method combining monocular focus stack image and deep neural network is proposed. This method makes full use of the advantages of light field imaging technology and can generate the all focus image. The method first collects multiple frames of continuous images at different focal lengths of the scene, using a divide and conquer algorithm strategy, uplink uses YOLO neural network to identify the target in 3D space and track the position information; the downlink reconstructs the four-dimensional (4D) light field data based on the focus stack image frequency domain back projection, and then uses light field imaging technology to invert the scene parallax; subsequently, achieve scene depth estimation and reconstruction of all focus image; finally, the uplink and downlink are merged to realize the reconstruction of the 3D point cloud of the space target. Experimental results on real scenes show the effectiveness of the proposed algorithm.
An Empirical Study of Neural Network Hyperparameters

The learning algorithms related to deep learning involves many attributes called hyperparameters, these variables help in determining the network structure. The performance of algorithms depends upon these hyper-parameter variables that are needed to be set prior to the actual implementation of the algorithm. This study involves an overview of some of the commonly used hyperparameters in the context of learning algorithms used for training neural networks along with the analysis of adaptive learning algorithms used for tuning learning rates.
A novel approach using modified filtering algorithm (MFA) for effective completion of cloud tasks

In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities.
Multilabel Text Classification With Incomplete Labels: A Safe Generative Model With Label Manifold Regularization and Confidence Constraint

In multilabel text classification, the label information of training instances may be incomplete due to various factors, such as expensively manual labeling, errors, etc. Hence, imposing big challenges to training classifiers. To tackle the problem of the incomplete labels, we proposed to exploit label correlations, and further jointly leverage manifold regularization and label confidence constraint. On one hand, the label manifold regularization preserves the local label structure to handle the high-level incomplete labels. On the other hand, the label confidence constraint, following the likelihood theory, avoids overestimating the weights of negative labels, leading to a safer inference. Upon the ideas, we propose a novel generative model, namely <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> ayesian <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> odel with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> abel manifold <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> egularization and label <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> onfidence constraint (BM-LRC), solved by generalized expectation maximization. Empirical results on popular benchmark data sets show that BM-LRC achieves competitive performance, especially when the available labels are scarce.
Early failure detection of paper manufacturing machinery using nearest neighbor‐based feature extraction

Abstract In a paper manufacturing system, it is substantially important to detect machine failure before it occurs and take necessary maintenance actions to prevent an unexpected breakdown of the system. Multiple sensor data collected from a machine provides useful information on the system's health condition. However, it is hard to predict the system condition ahead of time due to the lack of clear ominous signs for future failures, a rare occurrence of failure events, and a wide range of sensor signals which might be correlated with each other. We present two versions of feature extraction techniques based on the nearest neighbor combined with machine learning algorithms to detect a failure of the paper manufacturing machinery earlier than its occurrence from the multistream system monitoring data. First, for each sensor stream, the time series data is transformed into the binary form by extracting the class label of the nearest neighbor. We feed these transformed features into the decision tree classifier for the failure classification. Second, expanding the idea, the relative distance to the local nearest neighbor has been measured, results in the real‐valued feature, and the support vector machine is used as a classifier. Our proposed algorithms are applied to the dataset provided by Institute of Industrial and Systems Engineers 2019 data competition, and the results show better performance than other state‐of‐the‐art machine learning techniques.
Collaborative Learning Using LSTM-RNN for Personalized Recommendation

Today, the ability to track users' sequence of online activities, makes identifying their evolving preferences for recommendation practicable. However, despite the myriad of available online activity information, most existing time-based recommender systems either focus on predicting some user rating, or rely on information from similar users. These systems, therefore, disregard the temporal and contextual aspects of users preferences, revealed in the rich and useful historical sequential information, which can potentially increase recommendation accuracy. In this work, we consider such rich, user online activity sequence, as a complex dependency of each user's consumption sequence, and combine the concept of collaborative filtering with long short-term memory recurrent neural network (LSTM-RNN), to make personalized recommendations. Specifically, we use encoder-decoder LSTM-RNN, to make sequence-to-sequence recommendations. Our proposed model builds on the strength of collaborative filtering while preserving individual user preferences for personalized recommendation. We conduct experiments using Movielens ( https://grouplens.org/datasets/movielens ) dataset to evaluate our proposed model and empirically demonstrate that it improves recommendation accuracy when compared to state-of-the-art recommender systems.
Video Analysis System Using Deep Learning Algorithms

Detection of video duplicates is an active field of research, motivated by the protection of intellectual property, the fight against piracy or the tracing of the origin of reused video segments. In this work, a method for the detection of duplicate videos is proposed and implemented, making use of deep learning methods and techniques typical of the field of information recovery. This method has been evaluated with a data set usually used in the field, with which high average accuracies, above 85%, have been obtained. The effect of the different layers of the convolutional neural network used by the algorithm, the aggregation mechanisms that can be used on them, and the influence of the recovery model have been studied, finding a set of parameters that optimize the overall accuracy of the system.
Auto-scaling Using TOSCA Infrastructure as Code

Autoscaling cloud infrastructures still remains a challenging endeavour during orchestration, given the many possible risks, options, and connected costs. In this paper we discuss the options for defining and enacting autoscaling using TOSCA standard templates and its own policy definition specifications. The goal is to define infrastructure blueprints to be self-contained, executable by an orchestrator that can take over autonomously all scaling tasks while maintaining acceptable structural and non-functional quality levels.
Fact Check-Worthiness Detection with Contrastive Ranking

Check-worthiness detection aims at predicting which sentences should be prioritized for fact-checking. A typical use is to rank sentences in political debates and speeches according to their degree of check-worthiness. We present the first direct optimization of sentence ranking for check-worthiness; in contrast, all previous work has solely used standard classification based loss functions. We present a recurrent neural network model that learns a sentence encoding, from which a check-worthiness score is predicted. The model is trained by jointly optimizing a binary cross entropy loss, as well as a ranking based pairwise hinge loss. We obtain sentence pairs for training through contrastive sampling, where for each sentence we find the top most semantically similar sentences with opposite label. Through a comparison to existing state-of-the-art check-worthiness methods, we find that our approach improves the MAP score by 11%.
Associative memory on qutrits by means of quantum annealing

When associative memory is implemented on the well-studied Hopfield network, patterns are recorded in the interaction constants between binary neurons. These constants are chosen so that each pattern should have its own minimum energy of the system described by the Ising model. In the quantum version of the Hopfield network, it was proposed to recall such states by the adiabatic change of the Hamiltonian in time. Qubits, quantum elements with two states, for example, spins with S = 1/2 were considered as neurons. In this paper, for the first time, we study the function of associative memory using three-level quantum elements—qutrits, represented by spins with S = 1. We record patterns with the help of projection operators. This choice is due to the need to operate with a state with a zero spin projection, whose interaction with the magnetic field vanishes. We recall the state corresponding to one of the patterns recorded in the memory, or superposition of such states by means of quantum annealing. To equalize the probabilities of finding the system in different states of superposition, an auxiliary Hamiltonian is proposed, which is turned off at the end of evolution. Simulations were performed on two and three qutrits and an increase in the memory capacity after replacing qubits with qutrits was shown.
Ensemble approach for sensor-based human activity recognition

This paper discusses in detail our (Team:AISA) ensemble based approach to detect Human Activity for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. The SHL recognition challenge is an open competition wherein the participants are tasked with recognizing 8 different types of activities based on smartphone data collected from multiple positions - Hand, Hips, Torso, Bag. On the magnitude of sensor data, time and frequency domain features were calculated to achieve position independence. To make the model robust, we trained it with a random shuffle of the training and validation data provided. To find the optimal hyper-parameters, we parallely executed randomized search to choose the best performing model from about 200 models. We set aside 30% of this combined dataset for internal testing and the model predicted human activities with an F1-Score of 86% on this test dataset.
Analyzing the performance of data processing in private blockchain based distributed ledger

A blockchain is a new open-source technology who attract whole the business world and technology experts to research, to explore and to understand the potential of this technology in different areas. Blockchain initially released in 2008 by Satoshi Nakamoto as a fundamental technology for the first-ever global decentralized cryptography-based digital currency known as BITCOIN. A blockchain is a transparent and immutable globally distributed ledger, distributed databases, who have global agreement by all its users. It means that when anything written in distributed ledger is cannot be cheated and modified, it will be trusted if the writer of content is trusted. In this research studies, author analyzes the performance of two widely used blockchain platform one is Ethereum and second is Hyperledger Fabric.
Security design and application of Internet of things based on asymmetric encryption algorithm and neural network for COVID-19

During the period of COVID-19 protection, Internet of Things (IoT) has been widely used to fight the outbreak of pandemic. However, the security is a major issue of IoT. In this research, a new algorithm knn-bp is proposed by combining BP neural network and KNN. Knn-bp algorithm first predicts the collected sensor data. After the forecast is completed, the results are filtered. Compared with the data screened by traditional BP neural network, k-nearest-neighbor algorithm has good data stability in adjusting and supplementing outliers, and improves the accuracy of prediction model. This method has the advantages of high efficiency and small mean square error. The application of this method has certain reference value. Knn-bp algorithm greatly improves the accuracy and efficiency of the Internet of things. Internet of things network security is guaranteed. It plays an indelible role in the protection of COVID-19.
Nurse care activity recognition

Nurse care activity recognition is a new challenging research field in human activity recognition (HAR) because unlike other activity recognition, it has severe class imbalance problem and intra-class variability depending on both the subject and the receiver. In this paper, we applied the Random Forest-based resampling method to solve the class imbalance problem in the Heiseikai data, nurse care activity dataset. This method consists of resampling, feature selection based on Gini impurity, and model training and validation with Stratified KFold cross-validation. By implementing the Random Forest classifier, we achieved 65.9% average cross-validation accuracy in classifying 12 activities conducted by nurses in both lab and real-life settings. Our team, "Britter Baire" developed this algorithmic pipeline for "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data".