Reviewerly/exHarmony huggingface dataset:
A Deep Dive into Supervised Extractive and Abstractive Summarization from Text

Since the advent of World Wide Web, the world has seen an exponential growth in the information on the internet. In the web, today, a lot of digital articles are available for users to read. However, these documents lack a concise summary. Automatic systems that summarize such information are very rare. Moreover, these systems also lack the ability of getting information from multiple places and presenting the user with a coherent, informative summary. Summarization are of two types, viz. Extractive and Abstractive. While a lot of work has been done on extractive summary, abstractive summary generation is still an unexplored area. In the current work, the focus is essentially on producing abstractive summary of a document by first getting relevant and important sentences from the corpus and feeding these sentences as input in a trained deep neural network. For extracting salient sentences two models has been proposed, both these models not only consider syntactic similarity but also semantic similarity between sentences. To capture semantic similarity, embedding models like word2vec or paragraph vectors has been used. Next, to generate abstract version of these sentences a RNN autoencoder is used, which is responsible for the representation of longer sentences into shorter sentences without loosing semantic information. ROUGE evaluation technique has been used for evaluating the generated summary quality.
Machine learning techniques for code smells detection

research-article Share on Machine learning techniques for code smells detection: an empirical experiment on a highly imbalanced setup Authors: Frederico Caram Luiz LAIS - Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, Brazil LAIS - Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, BrazilView Profile , Bruno Rafael de Oliveira Rodrigues LAIS - Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, Brazil LAIS - Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, BrazilView Profile , Fernando Silva Parreiras LAIS - Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, Brazil LAIS - Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, BrazilView Profile Authors Info & Claims SBSI'19: Proceedings of the XV Brazilian Symposium on Information SystemsMay 2019 Article No.: 65Pages 1–8https://doi.org/10.1145/3330204.3330275Published:20 May 2019Publication History 1citation171DownloadsMetricsTotal Citations1Total Downloads171Last 12 Months33Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Learning Patient Transfers with Technology

Nurses who care about patients with functional disabilities are frequently required to transfer a patient from one place to another. To prevent nurses from injuring themselves during patient transfers, many government-supported schools in Germany provide programs which teach how to conduct transfers based on the kinaesthetics care conception. However, the program is typically limited to merely three lectures. With the goal of promoting nurses' health behavior, we analyzed current practices in kinaesthetics education and explored how interactive technology can extend those practices. We interviewed nursing-care teachers (N = 5) and students (N = 27), and conducted four contextual inquiries during kinaesthetics course sessions. A qualitative analysis of the data revealed three themes. Based on these, we describe a set of implications to support the learning of kinaesthetics-based transfers by means of technology. We propose the use of the implications as initial design goals for user-centered design processes and exemplify their application by illustrating a concept for a tablet-based learning system.
Multi‐scale microstructure binary pattern extraction and learning for image representation

In this study, an image representation method based on multi-scale microstructural binary pattern extraction is proposed, which uses zero-mean microstructural pattern binarisation. This method can express all kinds of important pattern structures that may appear in the image. By using the dominant binary pattern learning model, the dominant feature pattern sets adapted to different datasets can be obtained, which have good performance in the aspects of feature robustness, recognition, and representation ability. This method can greatly reduce the dimension of feature coding and improve the speed of the algorithm. The experimental results show that this method has strong recognition ability and robustness, is superior to the traditional local binary pattern and grey image micorstructure maximum response pattern methods, and has a competitive performance compared with the results of many latest algorithms.
Accelerating Conversational Agents Built With Off-the-Shelf Modularized Services

Today's common practice in developing conversational agents is pipelining off-the-shelf modularized services as ready-made building blocks. However, the discrete and sequential nature of the modules yields long response latency. We introduce Sci-Fii, a speculative inference framework accelerating conversational agent systems built with off-the-shelf modules, while keeping the modules unchanged.
A Study on 3D Optimal Path Planning for Quadcopter UAV Based on D* Lite

Since unmanned aerial vehicle (UAV) for industries are operated in complex low altitude environments, planning feasible paths is a necessary feature to achieve mission goals. D* Lite is applicable for industrial complex that uncertainties exist. This paper focuses on 3D path planning for quadcopter UAV based on D* Lite. Simulation results show that the algorithm can be applied in cluttered static and dynamic environments including unknown obstacles. In addition, when some waypoints exist, the proposed algorithm is able to optimize the global path by determining visit order. Therefore, this study is expected to contribute to increase the application of UAVs in industrial fields.
MTCNN with Weighted Loss Penalty and Adaptive Threshold Learning for Facial Attribute Prediction

Facial attribute prediction is a meaningful but challenging task in computer vision, due to the demand of recognizing many attributes of a face image at a time. In this paper, we propose a novel multi-task convolutional neural network (MTCNN) with a weighted loss penalty scheme and an adaptive threshold learning algorithm to predict facial attributes by groups. In particular, the way of grouping leverages the spatial relationships among attributes and the design of MTCNN greatly decreases model complexities. The core idea of the weighted loss penalty is to make the prediction probability of an attribute appearing on a face close to ground truth. During validation stage, the adaptive threshold algorithm learns the threshold for every attribute prediction adaptively. Experiments on two benchmark datasets, show that our method achieves superior performance to the prior state-of-the-art methods under two different evaluation protocols.
Human motion analysis in dark surrounding using line skeleton scalable model and vector angle technique

Detecting human existence in video streams is a fundamental task in many video processing applications. It is considered as one of the most challenging task that has attracted researchers’ attentions in various fields. In this paper, a novel procedure is produced to model, analyze and recognize human motions in video streams. This project will introduced a method for human motion analysis in dark surrounding using digital image processing technique. In particular, this project aims to detect and recognize human motions performing walking and running motions. There are four major areas that are related in this project for human motion analysis: (1) developing human body structure based on human skeleton model, (2) tracking and data collecting human motion with side view, (3) recognizing human activities from image sequences, and (4) image processing technique using edge detection and vectors angle calculation. All algorithms are developed using MATLAB software. Segmentation is developed to reduce the amount of data and filters out the useless information. Two methods are proposed for angle calculation and activities classification. Results showed that angle between 153.76˚-180˚ for method 1 and 49.64˚-92.86˚ for method 2 is classified as walking while jogging is 95.17˚-138.72˚ for method 1 and 22.62˚-56.31˚ for method 2. These application has successfully manipulated complex movement which is walking and jogging in dark surrounding for a clear activity determination.
Onshore oil and gas reservoir detection through mapping of hydrocarbon microseepage using remote sensing

Abstract Remote sensing is one of an advance technology that can be used for detecting hydrocarbon microseepage onshore. One of the advantage is specialize in rapid and low cost detection. Remote sensing technique namely multispectral scanner can overview the characteristic of seepage system as an exploration indicator of subsurface hydrocarbon. Therefore, the objective of this study is to analyse the microseepage system as indication of subsurface hydrocarbon accumulation using a multispectral imagery. The hydrocarbon-induced surface alterations of soil and sediments (clay-carbonate, ferric iron, ferrous iron) and associated anomalous vegetation were used as parameters. This research used Sentinel 2 and Landsat 8 multispectral imagery combine with the Directed Principal Component Analysis method to detect mineral alteration also using vegetation normalization to detect vegetation anomalies. The results of this study indicate the distribution of hydrocarbon microseepage distributed in area around Sungai Kenawang and Pulau Gading field, and also linear distributed in Merang River and Lalan River.
A Detailed Analysis and Improvement of Feature-Based Named Entity Recognition for Turkish

Named Entity Recognition (NER) is an important task in Natural Language Processing (NLP) with a wide range of applications. Recently, word embedding based systems that does not rely on hand-crafted features dominate the task as in the case of many other sequence labeling tasks in NLP. However, we are also observing the emergence of hybrid models that make use of hand crafted features through data augmentation to improve performance of such NLP systems. Such hybrid systems are especially important for less resourced languages such as Turkish as deep learning models require a large dataset to achieve good performance. In this paper, we first give a detailed analysis of the effect of various syntactic, semantic and orthographic features on NER for Turkish. We also improve the performance of the best feature based models for Turkish using additional features. We believe that our results will guide the research in this area and help making use of the key features for data augmentation.
Tunable coupling between Xmon qubit and coplanar waveguide resonator*

Realization of a flexible and tunable coupling scheme among qubits is critical for scalable quantum information processing. Here, we design and characterize a tunable coupling element based on Josephson junction, which can be adapted to an all-to-all connected circuit architecture where multiple Xmon qubits couple to a common coplanar waveguide resonator. The coupling strength is experimentally verified to be adjustable from 0 MHz to about 40 MHz, and the qubit lifetime can still be up to <?CDATA $12\,{\rm{\mu }}{\rm{s}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>12</mml:mn> <mml:mspace width="0.50em" /> <mml:mi mathvariant="normal">μ</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:math> in the presence of the coupling element.
Semi-automatic Tool for Ontology Learning Tasks

The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed an ontology learning procedure combining information acquisition from structured resources, such as WordNet or DBpedia, and unstructured resources using text mining techniques based on an evaluation of lexico-syntactic patterns. This approach offers a robust way, how to integrate even previously unknown information disregarding target application or domain. The proposed solution was implemented in the form of semi-automatic ontology learning tool used for integration of Excel document containing spare part records and Ford Supply Chain Ontology.
The study of binarization algorithm about digital rubbings image based on threshold segmentation and morphology

This paper discusses the binarization restoration algorithms of ancient Chinese Rubbings image. According to the location of the image features, such as color, edge, stroke width and pixel location feature, the binary restoration algorithm of digital rubbings based on threshold segmentation and mor phology is discussed. If the difference between the text and the background is larger and more obvious distinguish in images, Otsu threshold segmentation method can be directly used. For images with more background noise, shadows and uneven illumination, threshold segmentation cannot achieve effective segmentation and reasonable denoising, a set of algorithms associated with the pixel position of adaptive segmentation, morphological method, connected domain and mean filtering combined of binarization restoration are needed. The experimental results show that this algorithm is very effective for image with complex and large area noise.
Value-based technical debt management: an exploratory case study in start-ups and scale-ups

Software start-ups face fierce competition in the market forcing them to release their products quickly and often under tough time constraints. To meet their deadlines, start-ups take shortcuts in software development leading to the accumulation of technical debt. They are able to put their product in users hands faster, get feedback, and improve at the expense of quality issues in the long run. As a start-up evolves through inception, stabilization and growth this debt will have to be managed. Technical debt management and software product line engineering techniques have some similar benefits of increased productivity and reduced time-to-market. Our aim is to check whether software product line engineering can be a candidate technique for start-ups to employ in managing technical debt as a response to their life-cycle phase goals and challenges. We conducted expert interviews with nine start-up professionals to identify the strategies applied in relation to technical debt management and software product lines engineering and other software engineering practices in start-ups. By analyzing the responses from the interviews we found that depending on the life-cycle phase of the start-up software product line engineering proved effective in managing technical debt and helped the start-ups to advance through the life-cycle phases.
A method for single image phase unwrapping based on generative adversarial networks

Phase unwrapping technology plays an important role in phase measurement profilometry. The unwrapping results directly affect the measurement accuracy. With the development of deep learning theory, it is opening a new direction to phase unwrapping algorithm. In this paper, a new neural network model based on an improved generation adversarial network (iGAN) is proposed for phase unwrapping. Compared with traditional methods, it can effectively suppress the influence of noise such as shadows, and does not need any referenced grating information. In addition, it can realize the phase unwrapping with a single image. Specifically, the algorithm is verified by the three-dimensional reconstruction with structured light based on the simulation data. The results indicate that the proposed method can successfully unwrap the phase via a single image. It also can well suppress the influence of frequency and shadows.
An Efficient Method for Text Classification Task

Text classification has always been a research hot-spot in the field of natural language processing. For the neural network input matrix, only the word vector of the word level is extracted, which ignores the expression of the overall semantic features of the text level, resulting in insufficient representation of text features and affecting accurate classification. In this paper, a text representation matrix combining word2vec and LDA topic models is proposed. Combining word meaning and semantic features, inputting LSTM for text classification, and introducing Attention mechanism to improve LSTM model, LSTM-Attention model is designed. The experimental results show that the LSTM classification model has better classification result than the traditional machine learning model, and the LSTM model with the Attention mechanism has a certain degree of improvement compared with the classical text classification models.
Escape Room in Mixed Reality: 10th Annual 3DUI Contest

The 10th annual IEEE 3DUI Contest focuses on the development of 3D User Interfaces (3DUIs) using passive haptic feedback in Mixed Reality Environments. Material properties of real objects are enhanced with virtual behaviors while used for interaction in a mixed reality scenario. The contest was open to anyone interested in 3DUIs, from researchers to students, enthusiasts, and professionals. The purpose of the contest is to stimulate innovative and creative solutions to challenging 3DUI problems.
Developing a multimedia-based learning media for learning matrix transformation

The development of science and technology has entered the world of education. One of the effects is multimedia. The purpose of this study was to develop multimedia-based learning in the transformation matrix learning class XI. After the development, the validity and feasibility tests were carried out by material experts, media experts, and learning practitioners and students. This study uses the Research and Development (R & D) method. There are 6 stages that must be passed, including the analysis phase, the planning stage, the development stage, the verification stage, the validation stage, and the final stage with the name TaGeo. It was obtained that the developed media was feasible to be used as a medium for learning mathematics based on validation by experts and student responses. The results of the validation by material experts showed that the media developed was variable with score of 3.53. The results of the validation by media experts showed that the media developed was valid with score of 3.49. The results of validation by learning practitioners show that the media developed is valid with score of 3.44. And the results of student responses show that the media developed is feasible with score of 3.41.
TCMD: A Two-Tier Classification Model for Anomaly-Based Detection in IoT

The Internet of Things (IoT) is a new technology paradigm that refers to distributed physical devices that are connected to the Internet. The large amount of data generated by these devices is considered to be a challenging issue. This data suffers from anomalies or abnormal behaviour for a number of reasons, such as sensor faults or attack issues. However, the data collected from IoT devices is usually unlabelled, which means that the normal or anomaly classes are unknown. This study proposes TCMD, a two-tier classification model for anomaly detection in IoT. In addition, it describes the validation methods used for the model to evaluate the quality of the clustering and the performance of the classification. TCMD firstly employs hierarchical affinity propagation (HAP) clustering to group the data into normal and anomaly clusters. Secondly, the labelled data obtained from the clustering is used to train decision trees (DTs). The results show that the TCMD is able to label the data which can be helpful to reduce human intervention. In addition, in terms of false positive rate (FPR), TCMD performs well compared with the DTs on the original dataset and outperforms the state-of-the-art model.
Temporal and spatial variability analysis of the solar radiation in a region affected by the intertropical convergence zone

Abstract The first aim of this work was to obtain temporal variability patterns for satellite‐derived solar radiation estimations in Zambia. A principal component analysis, in Zambia and the surrounding zones, was performed and from this analysis the physical phenomena associated with these patterns were established. According to the results, two temporal variability patterns stand out: the first is associated with the regional global climate characteristics, including both the deterministic and non‐deterministic components of solar radiation, and the other is strictly associated with the influence of the intertropical convergence zone (ITCZ) responsible for the behaviour of solar radiation during October–March. The second aim of the work was to analyse the spatial variability of the irradiance in the study area. For this aim, a clustering analysis based on the interquartile range of the data was performed. The analysis leads to a spatial distribution of the radiation in agreement with the influence of the ITCZ on the territory. Indeed, those stations less affected by the ITCZ, in the south and east of the territory, show a clear diminution of the radiation around June. However, the stations of the northwest zone, the most affected by the belt of low pressure during November–April, do not present this diminution.
Trust in Computational Intelligence Systems: A Case Study in Public Perceptions

The public debate and discussion about trust in Computational Intelligence (CI) systems is not new, but a topic that has seen a recent rise. This is mainly due to the explosion of technological innovations that have been brought to the attention of the public, from lab to reality usually through media reporting. This growth in the public attention was further compounded by the 2018 GDPR legislation and new laws regarding the right to explainable systems, such as the use of "accurate data", "clear logic" and the "use of appropriate mathematical and statistical procedures for profiling". Therefore, trust is not just a topic for debate - it must be addressed from the onset, through the selection of fundamental machine learning processes that are used to create models embedded within autonomous decision-making systems, to the selection of training, validation and testing data. This paper presents current work on trust in the field of Computational Intelligence systems and discusses the legal framework we should ascribe to trust in CI systems. A case study examining current public perceptions of recent CI inspired technologies which took part at a national science festival is presented with some surprising results. Finally, we look at current research underway that is aiming to increase trust in Computational Intelligent systems and we identify a clear educational gap.