Reviewerly/exHarmony huggingface dataset:
Reconstruction of Power System Measurements Based on Enhanced Denoising Autoencoder

This paper presents a new solution for reconstructing missing data in power system measurements. An Enhanced Denoising Autoencoder (EDAE) is proposed to reconstruct the missing data through the input vector space reconstruction based on the neighbor values correlation and Long Short-Term Memory (LSTM) networks. The proposed LSTM-EDAE is able to remove the noise, extract principle features of the dataset, and reconstruct the missing information for new inputs. The paper shows that the utilization of neighbor correlation can perform better in missing data reconstruction. Trained with LSTM networks, the EDAE is more effective in coping with big data in power systems and obtains a better performance than the neural network in conventional Denoising Autoencoder. A random data sequence and the simulated Phasor Measurement Unit (PMU) data of power system are utilized to verify the effectiveness of the proposed LSTM-EDAE.
Dataset Recommendation via Variational Graph Autoencoder

This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user's research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the user's research need.The motivation of building such a system is to save users from spending time on heavy literature review work to find usable datasets.We start by constructing a two-layer network: one layer of citation network, and the other layer of datasets, connected to the firstlayer papers in which they were used.A query highlights a set of papers in the citation layer.However, answering the query as a naive retrieval of datasets linked with these highlighted papers excludes other semantically relevant datasets, which widely exist several hops away from the queried papers.We propose to learn representations of research papers and datasets in the two-layer network using heterogeneous variational graph autoencoder, and then compute the relevance of the query to the dataset candidates based on the learned representations.Our ranked datasets shown in extensive evaluation results are validated to be more truly relevant than those obtained by naive retrieval methods and adoptions of existing related solutions.
DBT Masses Automatic Segmentation Using U-Net Neural Networks

To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.
ICDAR 2019 Competition on Scene Text Visual Question Answering

This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23,038 images annotated with 31,791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding.
Improving CNN-based activity recognition by data augmentation and transfer learning

Activity classification is a challenging problem due to large signal dimensionality, high intra-and inter-subject variability in activity patterns, presence of transitional classes showing mixture of patterns, and dominance of the null class. Supervised learning has been the prevalent choice with deep neural networks (DNNs) showing some promising potential. Deep learning however requires a large number of labeled samples which is difficult to acquire, especially from vulnerable older people. In this paper we implement 3 different convolutional neural network architectures trained on data from older people, incorporating Bayesian optimization for efficient hyperparameter tuning. We exploit various augmentation methods for time-series to make invariant predictions and also cross-utilize knowledge about physical activity of younger persons in order to improve generalization in our models designed for older adults.
Data Papers as a New Form of Knowledge Organization in the Field of Research Data

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.
Abnormal Behavior Recognition using CNN-LSTM with Attention Mechanism

There is a rising trend of security issues in our society nowadays. Every now and then, there are news such as robberies, fighting or terrorism around the world. Hence, some robust measurements need to be done to ensure public safety. This is when computer vision techniques come into play. Conventional surveillance cameras lack the capability of autonomously detecting abnormal behaviors in footages, and hence the determination of abnormal activities is solely dependent on human judgement. There is no absolute meaning of what abnormal behavior is, it depends on the settings. For example, fighting in a martial art class is a normal behavior, however if there is fighting in a bank, it is considered as abnormal behavior. In this study, we focus on two scopes: two-persons interactions and crowd-based interactions. Our Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) with attention mechanism model can automatically extract the important features from the video frames and interpret the temporal information between the video sequences. Different from the typical neural networks, our model includes attention mechanism that focuses on salient part of human action. Five benchmark datasets are used to validate the performance of the proposed model.
Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.
Toward A Ballbot for Physically Leading People: A Human-Centered Approach

This work presents a new human-centered method for indoor service robots to provide people with physical assistance and active guidance while traveling through congested and narrow spaces. As most previous work is robot-centered, this paper develops an end-to-end framework which includes a feedback path of the measured human positions. The framework combines a planning algorithm and a humanrobot interaction module to guide the led person to a specified planned position. The approach is deployed on a person-size dynamically stable mobile robot, the CMU ballbot. Trials were conducted where the ballbot physically led a blindfolded person to safely navigate in a cluttered environment.
Convolutional neural networks for image spam detection

Spam can be defined as unsolicited bulk e-mail. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply convolutional neural networks (CNN) to this problem, we compare the results obtained using CNNs to other machine learning techniques, and we compare our results to previous related work. We consider both real-world image spam and challenging image spam-like datasets. Our results improve on previous work by employing CNNs based on a novel feature set consisting of a combination of the raw image and Canny edges.
An autonomic risk- and penalty-aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioning

Summary Cloud computing environment allows presenting different services on the Internet in exchange for cost payment. Cloud providers can minimize their operational costs by auto‐scaling of the computational resources based on demand received from users. However, the time and cost required to increase and decrease the number of active computational resources are among the biggest limitations of scalability. Thus, auto‐scaling is considered as one of the most important challenges in the field of cloud computing. The present study aimed to present a new solution to automatic scalability of resources for multilayered cloud applications under the Monitor‐Analysis‐Plan‐Execute‐Knowledge loop. In addition, the Google penalty payment model was used to model the penalty costs in the problem and to accurately evaluate the earned profit. A hybrid resource load prediction algorithm was proposed to evaluate the future of resources in each cloud layer. Further, we used statistical solution to determine the statuses of VMs in addition to presenting a risk‐aware algorithm to allocate the user requests to active resources. The experimental results by Cloudsim indicated the improvement of the proposed approach in terms of operational costs, the number of used resources, and the amount of profit.
Feature Extraction using Self-Supervised Convolutional Autoencoder for Content based Image Retrieval

This paper presents Autoencoder using Convolutional Neural Network for feature extraction in the Content-based Image Retrieval. Two type of layers are in the convolutional autoencoder architecture, they are encoder and decoder layer. The encoder layer extracts the important representation of the image using feature learning capability of the convolutional neural network, and reduces the dimension of the image. The decode layer reconstructs the representation, such that, the output of the autoencoder is close to the input data. The important representation of the image from the encoder layer in convolutional autoencoder, is used as the extracted features in the content-based image retrieval. Similarity distance between the extracted feature of the query image and the database is calculated to retrieve relevant images. The images in Corel dataset are used for the experiment and tested using the proposed model. The experiments show that the extracted features are representable for the images, and can be used to retrieve relevant images in the content-based image retrieval.
First-Year Performance of a Nationwide Earthquake Early Warning System Using a Wavefield-Based Ground-Motion Prediction Algorithm in Japan

Abstract The propagation of local undamped motion (PLUM) algorithm is a wavefield-based method that predicts ground motions using direct observations. In March 2018, the Japan Meteorological Agency (JMA) implemented PLUM into its nationwide earthquake early warning (EEW) system, in order to enhance system robustness for complex earthquake scenarios in which traditional source-based algorithms fail to provide accurate and timely ground-motion predictions. This was the first nationwide EEW system to implement a wavefield-based methodology. Here, we evaluate the performance of PLUM during its first year of implementation in the JMA EEW system, using earthquakes that occurred between March 2018 and March 2019; these include 13 earthquakes that satisfied the public warning issuance criteria. Our analysis shows that PLUM predicted ground motions without significant errors and reduced the number of missed warnings. These findings indicate that introducing the wavefield-based methodology benefits EEW users with high tolerance of false alarms, including the general public.
Prediction of Indonesia Presidential Election Results for the 2019-2024 Period Using Twitter Sentiment Analysis

The main purpose of this research is to predict the election of the President and Vice President of the Republic of Indonesia from 2019-2024 through the mining process of public opinion on twitter and test it accurately with classification algorithms namely Support Vector Machine (SVM) with selection features of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). As the fourth largest democracy and the fifth largest twitter user in the world, twitter in Indonesia is very influential as a place for people to fight each other's arguments. Public talk about the election of two candidates for President and Vice President of the Republic of Indonesia for the 2019-2024 on Twitter, became an interesting topic for Twitter users with a variety of public sentiment both positive and negative. Therefore, there is a need for a method that helps to see public opinion effectively. The researchers used tweets in Indonesian as research data with keywords #jokowi2periode and #2019tetapjokowi, and #2019prabowosandi and #2019gantipresiden with a total of 4000 tweets. This study uses a classification technique, namely SVM Algorithm. This method is chosen based on many of the best classification techniques commonly used for the analysis of opinion sentiments. It is because of SVM has weaknesses for selecting the appropriate parameters or features. In this study, researchers made improvements to previous research using a feature selection comparison method, PSO & GA. The results of this study are in the form of a prediction of the pairs of candidates for the President and the Vice President of Indonesia for the period of 2019-2024 who have more positive sentiments. Based on public opinion on Twitter, the pair Prabowo Subianto-Sandiaga Uno is predicted to be elected as President and Vice President of Indonesia for the period of 2019-2024 with the most positive sentiment, reaching 830 out of 1000 tweets entered. And the SVM method of the combination of PSO is the best method with accuracy reaching 86.20% and the AUC value reaching 0.934.
Learning Norms from Stories

Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior. We introduce a complementary technique in which a value-aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the children's educational comic strip, Goofus & Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non-normative by identifying if they align with the main characters' behavior. We also report the models' performance when transferring to two unrelated tasks with little to no additional training on the new task.
Improved Neural Network using Integral-RELU based Prevention Activation for Face Detection

Numerous variation of neural networks improved the face detection performance significantly. However, extravagant computation becomes a major problem found when the window explored the non-face candidate. To prevent useless detection in the non-face area, our proposed method is the addition of a prevention function using integral representation in the RELU (Rectified Linear Units) activation function. If the integral of RELU does not reach the threshold, the convolution is skipped and the window shifts to the neighboring area. Both functions are selected because they are easy to calculate and achieve convergence speed rapidly. Based on the results of trials on 10 data, the Integral CNN + Integral is faster than CNN + RELU with a speed ratio of 125: 285 FPS. Besides, ‘RELU CNN + Integral’ has fewer face detection redundancies compared to Viola & Jones algorithms. This result shows that our proposed method is superior to the state-of-the-art algorithms.
Auditing Algorithms

In this paper, we present the Algorithmic Audit (AA) of REM!X, a personalized well-being recommendation app developed by Telefónica Innovación Alpha. The main goal of the AA was to identify and mitigate algorithmic biases in the recommendation system that could lead to the discrimination of protected groups. The audit was conducted through a qualitative methodology that included five focus groups with developers and a digital ethnography relying on users comments reported in the Google Play Store. To minimize the collection of personal information, as required by best practice and the GDPR [1], the REM!X app did not collect gender, age, race, religion, or other protected attributes from its users. This limited the algorithmic assessment and the ability to control for different algorithmic biases. Indirect evidence was thus used as a partial mitigation for the lack of data on protected attributes, and allowed the AA to identify four domains where bias and discrimination were still possible, even without direct personal identifiers. Our analysis provides important insights into how general data ethics principles such as data minimization, fairness, non-discrimination and transparency can be operationalized via algorithmic auditing, their potential and limitations, and how the collaboration between developers and algorithmic auditors can lead to better technologies
A Survey of Evolutionary Computation for Web Service Composition: A Technical Perspective

Service oriented computing has emerged as a popular software development paradigm. In the era of Cloud computing, Big data, the Internet of Things (IoT) and Smart Cities, Web service composition has been extensively researched. Web service composition aims to find the best way of combining services, which accomplish simple tasks, into a more sophisticated composite application. Evolutionary computation lends itself to tackling the problem of Web service composition, since it allows for the optimisation of the overall Quality of Service attributes of the composite solution. In order to gain a better understanding of the different evolutionary computation-based approaches applied to this problem, a number of literature surveys have been written in this area. However, these surveys do not focus on the technical aspects of using evolutionary computation to this end, instead focusing on the general application of methods. Thus, the focus of this survey is on analysing existing works from a technical perspective, paying particular attention to the following key decisions when choosing an evolutionary computation-based approach for Web service composition: a) the representation of candidates, b) the fitness evaluation strategy, c) the handling of correctness constraints, d) the choice of evolutionary algorithms and operators. Based on these analyses, current trends, limitations, and future research paths are identified.
Pre-train, Interact, Fine-tune: a novel interaction representation for text classification

Text representation can aid machines in text understanding. Previous work on text representation often focuses on the so-called forward implication, i.e., preceding words are taken as the context of later words for creating representations, effective it is, yet ignoring the fact that the semantics of a text segment is a product of the mutual implication of words in the text: later words contribute to the meaning of preceding words. To bridge this gap, we introduce the concept of interaction and propose a two-perspective interaction representation, in which it encapsulates a local and a global interaction representation. Here, a local interaction representation is one that interacts among words with parent-children relationships on the syntactic trees whereas a global interaction interpretation is one that interacts among all the words in a sentence. We combine these two interaction representations to develop a Hybrid Interaction Representation (HIR). Inspired by existing feature-based and fine-tuning-based pretrain-finetuning approaches to language models, we integrate the merits of feature-based and fine-tuning-based methods to propose the Pre-train, Interact, Fine-tune (PIF) architecture. We evaluate our proposed models on five widely-used datasets for text classification tasks. It turns out that our ensemble method, HIRP, outperforms state-of-the-art baselines with improvements ranging from 2.03% to 3.15% in terms of error rate. In addition, we find that, the improvements of PIF against most state-of-the-art methods is not affected by increasing of the text length.