Reviewerly/exHarmony huggingface dataset:
Time of Use and Patterns of Internet Consumption in University Students: A Comparative Study between Spanish-Speaking Countries

Today, the processes of virtualization in higher education are decisive. The digital paradigm has an enormous influence on the university world, and will have even more in the future. Knowing how students access the Internet, how they consume it and how long they use it would be of great value for university policy, to facilitate the proper integration of information and communication technologies (ICT). The main objective of this study is to determine the time of use of the Internet by university students in different Spanish-speaking countries in the last eight years (2012–2019). It also aims to determine whether belonging to a common cultural space has an influence on this fact. The broad sample was composed of 2463 subjects from Chile, Ecuador, Spain, Guatemala, Mexico, Peru, and Venezuela. The results show that there is no homogeneity in the time of use and consumption patterns among the countries analyzed. The particular elements and social habits of each specific country, and multiple variables within each of them, condition their use. In conclusion, it can be argued that the integration of ICTs in university contexts is a very complex phenomenon in which multiple factors are present. In this sense, the sustainable educational policies of each country—and even of each university—must focus on their characteristics and idiosyncrasies. Importing practices from other countries, or attempting to apply common patterns of integration, may not be effective because of differences in the very different variables present in each.
Use of Scrum in the rehabilitation of a commercial building in Peru

Purpose This study seeks to break the current paradigm that Scrum, as an agile framework, is only applicable for information technologies (IT) projects and to confirm the feasibility of its application in the construction sector. Design/methodology/approach The rehabilitation of a shopping mall in Piura (Peru) is used as a case study to implement Scrum in a construction project. In addition, a thorough literature review is performed to establish the state-of-the-art and practice in this field and set the foundation of the elements applied during the case study. Findings The results of this study show the great versatility of Scrum in the construction sector. The key findings include a reduction of the construction duration that provided value to the owner, flexibility for the inclusion of changes (induced by the client or by the complexity of the context in which the project is developed), risk control in high uncertainty scenarios and general satisfaction for all stakeholders. Originality/value The waterfall or traditional management methodologies are not always effective in construction projects, and their application depends on the features and complexity of the environment in which they are developed. This study provides a case study showing the application of Scrum in the construction industry. The positive results obtained from the application can be used by researchers and practitioners looking to incorporate Scrum (or other agile tools) to enhance the management of construction projects.
Gifting in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning has generally been studied under an assumption inherited from classical reinforcement learning: that the reward function is the exclusive property of the environment, and is only altered by external factors. In this work, we break free of this assumption and introduce peer rewarding, in which agents can deliberately influence each others' reward function. We formalize this more general setting and discuss its properties in depth. We also empirically study gifting, a peer rewarding mechanism which allows agents to reward other agents as part of their action space. We demonstrate that this approach can greatly improve learning progression in a resource appropriation setting and provide a preliminary analysis of the complex effects of gifting on the learning dynamics.
Image-Level Harmonization of Multi-site Data Using Image-and-Spatial Transformer Networks

We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site.
An Assessment of Hitmen and Contracted Violence Providers Operating Online

Past research has considered the ways in which vendors and consumers of illicit goods adapt to various formal and informal threats and manage risk in online environments. However, this topic is virtually unexplored in the context of contract-based violence. Using a sample of 24 advertisements posted on the Open and Dark web, this study utilizes a qualitative case study design to analyze the ways in which vendors attempt to signal legitimacy through the language and images used in their posts. Further, this work outlines the advertised payment structures and prices based on the skill level of contract hitmen, the weapons used, the method of violence, and the status of potential victims. The analysis reveals several ways in which vendors emphasize the privacy and anonymity of their services and highlight their ties to well-recognized organizations (e.g., the military, the mafia) in an attempt to mitigate risk and uncertainty for consumers. In addition, results reveal that online list prices for basic services are higher than previous estimates for similar services offered off-line, suggesting a premium associated with legitimacy and anonymity. These findings contribute to the literature surrounding the market for contract violence, as well as online illicit market processes generally.
Auto-Sklearn 2.0: The Next Generation

Automated Machine Learning, which supports practitioners and researchers with the tedious task of manually designing machine learning pipelines, has recently achieved substantial success. In this paper we introduce new Automated Machine Learning (AutoML) techniques motivated by our winning submission to the second ChaLearn AutoML challenge, PoSH Auto-sklearn. For this, we extend Auto-sklearn with a new, simpler meta-learning technique, improve its way of handling iterative algorithms and enhance it with a successful bandit strategy for budget allocation. Furthermore, we go one step further and study the design space of AutoML itself and propose a solution towards truly hand-free AutoML. Together, these changes give rise to the next generation of our AutoML system, Auto-sklearn (2.0). We verify the improvement by these additions in a large experimental study on 39 AutoML benchmark datasets and conclude the paper by comparing to Auto-sklearn (1.0), reducing the regret by up to a factor of five.
Confocal chromatic sensor with an actively tilted lens for 3D measurement

Confocal chromatic displacement sensors are versatile and precise sensors for measuring the distance to a single point. In order to obtain a 3D measurement device, this paper presents an integrated scanning sensor design that employs a tilting lens mechanism for manipulating the light path of the sensor. The optical implications of the design are analytically modeled and simulated. An experimental setup is constructed to evaluate the system design and to test its performance on a variety of samples. Results show good agreement with the simulations and modeling; with maximal tip/tilt angles of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>±<!-- ± --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mn>2.5</mml:mn> <mml:mo>∘<!-- ∘ --></mml:mo> </mml:msup> </mml:mrow> </mml:math> , the setup is capable of measuring a volume of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>1.7</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mn>1.7</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mn>1</mml:mn> <mml:mspace width="thickmathspace" /> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:mrow> <mml:mn>3</mml:mn> </mml:msup> </mml:math> with a lateral resolution of 24.8 µm and an axial resolution of 3 µm.
An Automated Vertebrate Animals Classification Using Deep Convolution Neural Networks

On over years, the accuracy level of any algorithm for animal detection using a computer vision system is still practically unusable under uncontrolled environment. A lot of interesting has been shown to object detection, recognition, and classification, etc. Visual monitoring in scenes, for animal, is currently one of the most active research topics in computer vision (CV). In spite of there are a lot of research, intelligent, real-time, but the methods of dynamic object detection and recognition are still unavailable. This paper suggests using Deep Convolutional Neural Network (CNN) to detect and classify the animals (vertebrate classes) in digital images. Our dataset consists of 12000 different images, 9600 images for training stage, and the rest images (2400) for evaluation stage. After apply the proposed system, we found the best image size for this algorithm is 50x50 and the best number of epochs is 100. The total performance of the results reached to 97.5%. The experimental results reflected that the proposed algorithm has a positive effect on overall animal classification performance.
Hierarchical Gated Deep Memory Network With Position-Aware for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims at identifying the sentiment polarity of specific aspect in the sentence.Previous work has realized the importance of the information interaction between aspect term and context.However, most existing information interaction methods are coarse-grained, which results in a certain loss of information.In addition, most methods ignore the role of position information in identifying the sentiment polarity of the aspect.To better address the two problems, we propose a novel approach, called hierarchical gated deep memory network with position-aware.Our approach has two characteristics: 1) it has fine-grained information interaction attention mechanism which models the word-level interaction between aspect and context.The sentence-to-aspect attention is used to capture the most indicative sentiment words in context.And the aspect-to-sentence attention is used to capture the most important word in the aspect term.2) The position information is embedded as a feature in the sentence representation.Finally, we conduct sentiment classification comparative experiment on laptop and restaurant datasets.The experimental results show that our model achieves state-of-the-art performance on aspect-based sentiment analysis.
Image Retrieval Using a Deep Attention-Based Hash

Image retrieval is becoming more and more important due to the rapid increase of the number of images on the web.To improve the efficiency of computing the similarity of images, hashing has moved into the focus of research.This paper proposes a Deep Attention-based Hash (DAH) retrieval model, which combines an attention module and a convolutional neural network to obtain hash codes with strong representability.Our DAH has the following features: The Hamming distance between the hash codes generated by similar images is small and the Hamming distance of hash codes of dissimilar images has a larger constant value.The quantitative loss from Euclidean distance to Hamming distance is minimized.DAH has a high image retrieval precision: We thoroughly compare it with ten state-of-the-art approaches on the CIFAR-10 dataset.The results show that the Mean Average Precision (MAP) of DAH reaches more than 92% in terms of 12, 24, 36 and 48 bit hash codes on CIFAR-10, which is better than what the state-of-art methods used for comparison can deliver.
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method

This paper considers the problem of multi-agent distributed optimization. In this problem, there are multiple agents in the system, and each agent only knows its local cost function. The objective for the agents is to collectively compute a common minimum of the aggregate of all their local cost functions. In principle, this problem is solvable using a distributed variant of the traditional gradient-descent method, which is an iterative method. However, the speed of convergence of the traditional gradient-descent method is highly influenced by the conditioning of the optimization problem being solved. Specifically, the method requires a large number of iterations to converge to a solution if the optimization problem is ill-conditioned.In this paper, we propose an iterative pre-conditioning approach that can significantly attenuate the influence of the problem's conditioning on the convergence-speed of the gradient-descent method. The proposed pre-conditioning approach can be easily implemented in distributed systems and has minimal computation and communication overhead. For now, we only consider a specific distributed optimization problem wherein the individual local cost functions of the agents are quadratic. Besides the theoretical guarantees, the improved convergence speed of our approach is demonstrated through experiments on a real data-set.
Japanese Teaching Quality Satisfaction Analysis with Improved Apriori Algorithms under Cloud Computing Platform

In this paper, we use modern education concept and satisfaction theory to study the construction of a system used to evaluate Japanese teaching quality based on a satisfaction model. We use a cloud computing platform to mine... | Find, read and cite all the research you need on Tech Science Press
Application Layer Scheduling in Cloud: Fundamentals, Review and Research Directions

The cloud computing paradigm facilitates a finite pool of on-demand virtualized resources on a pay-per-use basis. For large-scale heterogeneous distributed systems like a cloud, scheduling is an essential component of resour... | Find, read and cite all the research you need on Tech Science Press
Using Web Log Files the Comparative Study of Big Data with Map Reduce Technique

This research paper, discusses, how the implementations of a planned algorithm will be processed. The data set is collected from one of the engineering colleges in India. The Datasets are collected from different sources and how the raw data is a preprocessor. The data set is analyzed by using the Map Reducing Technique and that should carry out in naive byes and KNN algorithm. The Map-Reduce technique is parallel to computation using the key/value pair. Thus, the Map-Reduce system helps to analyze the information which will give the data of the potential clients, such as, login time, credit value, and a lot more at least reaction time. Finally, execution estimation has been done by using ROC bend to discover and affirm which algorithm is best similarly as orchestrating the weblog records per the general time, the specific time they have taken on a specific website. The Naive Bayes algorithm if its anticipated characterization. Like that the same process in the KNN algorithm. It is basic to perceive which the greatest classifier method is. To carry out this, the foreseen outcomes are approved during (ROC) Receiver Operating Characteristic curves.
Two‐Qubit State Swap and Entanglement Creation in a Superconducting Circuit QED via Counterdiabatic Drivings

Abstract Optimal coherent control of many qubits is critical to quantum information processing. Here, an efficient scheme is proposed for implementing state swap and creating an entanglement with two superconducting qubits in a circuit quantum electrodynamics (QED). Two qutrits of Cooper‐pair box (CPB) circuits are placed into a cavity field and are driven by individual classical microwaves. In the two‐photon resonance with a large detuning, each CPB is reduced into a qubit effectively. Within a composite system composed of qubit states and cavity photons, two‐qubit state swap and entanglement can be fast obtained using the technique of shortcuts to adiabaticity with counterdiabatic drivings. Benefited from the shorter duration times and less sensitivity to timing errors and parameter imperfections, high‐fidelity quantum operations can be performed with the accessible decoherence rates. The present protocol could offer a feasible route towards optimized operations on superconducting qubits in a circuit QED.
Performance Analysis of Machine Learning Algorithms for IoT-Based Human Activity Recognition

Enormous growth is seen in the field of information technology and communication technology which enable the Internet of Things (IoT) technology on the boon. Nowadays, IoT technology is very common and widely used in all areas. It is also expanding its wing day by day. At the end of 2019, IoT devices will reach to 26.66 billion. Due to wide and diverse use of IoT technology, a large amount of valuable data is generated which gives researchers to deal with the huge amounts of real-time data. Machine learning plays a vital role in making a IoT environment, but it is a very complex task to build a smart environment. For personal healthcare monitoring, IoT-based sensors and mobiles devices play a crucial role in the betterment of the human lifestyle. Wearable sensor technology which is incorporated with mobile devices is more commonly used in monitoring personal health and well-being. In this research work, we examine the capability and performance of machine learning algorithms over the built-human activity recognition (HAR) dataset. Based on the performance evaluation results, gradient boosting classifier (GBC), support vector machine (SVM), random forest (RF), bagging classifier (BAG), classification and regression trees (CART), k-nearest neighbors (KNN), and extra trees classifier (ETC) algorithms have the better accuracy and suitable for real IoT datasets.
Bridging Data Science and Dynamical Systems Theory

This short review describes mathematical techniques for statistical analysis and prediction in dynamical systems. Two problems are discussed, namely (i) the supervised learning problem of forecasting the time evolution of an observable under potentially incomplete observations at forecast initialization; and (ii) the unsupervised learning problem of identification of observables of the system with a coherent dynamical evolution. We discuss how ideas from from operator-theoretic ergodic theory combined with statistical learning theory provide an effective route to address these problems, leading to methods well-adapted to handle nonlinear dynamics, with convergence guarantees as the amount of training data increases.
Twitter User Profiling: Bot and Gender Identification

Social bots are automated programs that generate a significant amount of social media content. This content can be harmful, as it may target a certain audience to influence opinions, often politically motivated, or to promote individuals to appear more popular than they really are. We proposed a set of feature extraction and transformation methods in conjunction with ensemble classifiers for the PAN 2019 Author Profiling task. For the bot identification subtask we used user behaviour fingerprint and statistical diversity measures, while for the gender identification subtask we used a set of text statistics, as well as syntactic information and raw words.
Domain-Based Fuzzing for Supervised Learning of Anomaly Detection in Cyber-Physical Systems

A novel approach is proposed for constructing models of anomaly detectors using supervised learning from the traces of normal and abnormal operations of an Industrial Control System (ICS). Such detectors are of value in detecting process anomalies in complex critical infrastructure such as power generation and water treatment systems. The traces are obtained by systematically "fuzzing", i.e., manipulating the sensor readings and actuator actions in accordance with the boundaries/partitions that define the system's state. The proposed approach is tested in a Secure Water Treatment (SWaT) testbed -- a replica of a real-world water purification plant, located at the Singapore University of Technology and Design. Multiple supervised classifiers are trained using the traces obtained from SWaT. The efficacy of the proposed approach is demonstrated through empirical evaluation of the supervised classifiers under various performance metrics. Lastly, it is shown that the supervised approach results in significantly lower false positive rates as compared to the unsupervised ones.
A Random Forest Classification Model for Transmission Line Image Processing

Random Forest algorithm is widely used in transmission line image processing, which is one of the important applications of classifier integration. But in the process of classification, when the classification results of each base classifier have similar error distribution, the final reduction of the classification effect. Aiming at this problem, this paper proposes a method of similarity measure of decision tree based on confusion Matrix. This method considers the number of trees in different categories, the classification of the correct and wrong cases, and the classification of the wrong classification. At the same time, combined with the classification performance of the decision tree, the model selection of random forest is completed by using the strategy of "deleting inferior". The experimental results show that the proposed method has higher average classification accuracy and higher stability than the original algorithm in three data sets. Therefore, the random forest image classification model based on confusion Matrix can improve the classification ability of random forest.
Collective Learning: A 10-Year Odyssey to Human-centered Distributed Intelligence

This paper illustrates a 10-year research endeavor on collective learning, a paradigm for tackling tragedy of the commons problems in socio-technical systems using human-centered distributed intelligence. In contrast to mainstream centralized artificial intelligence (AI) allowing algorithmic discrimination and manipulative nudging, the decentralized approach of collective learning is by-design participatory and value-sensitive: it aligns with privacy, autonomy, fairness and democratic values. Engineering such values in a socio-technical system results in computational constraints that turn collective decision-making into complex combinatorial NP-hard problems. These are the problems that collective learning and the EPOS research project tackles. Collective learning finds striking applicability in energy, traffic, supply-chain and the self-management of sharing economies. This grand applicability and the social impact are demonstrated in this paper along with a future perspective of the collective learning paradigm.