Reviewerly/exHarmony huggingface dataset:
Analysis of the implementation of the identification system for directly marked parts - DataMatrix code

Abstract A photocode in Polish is a common name for a two-dimensional code, which is characterized by higher information capacity than a traditional barcode. The paper analyses the use of the two-dimensional coding system - DataMatrix to identify and classify the material stream in the production process. The analysis showed the effectiveness of the implementation of the coding system in the context of increased availability, machine use and increased product quality. The improvement in the production process is confirmed by an increase in the OEE index. As part of further improvement measures, the coding system should be implemented throughout the enterprise, its compatibility with existing systems should be ensured and applied to all products.
An Evaluation of Action Recognition Models on EPIC-Kitchens

We benchmark contemporary action recognition models (TSN, TRN, and TSM) on the recently introduced EPIC-Kitchens dataset and release pretrained models on GitHub (https://github.com/epic-kitchens/action-models) for others to build upon. In contrast to popular action recognition datasets like Kinetics, Something-Something, UCF101, and HMDB51, EPIC-Kitchens is shot from an egocentric perspective and captures daily actions in-situ. In this report, we aim to understand how well these models can tackle the challenges present in this dataset, such as its long tail class distribution, unseen environment test set, and multiple tasks (verb, noun and, action classification). We discuss the models' shortcomings and avenues for future research.
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin

Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the prediction of deep models. Many attack and defense mechanisms have been proposed, although a satisfying solution still largely remains elusive. In this work, we give strong evidence that during training, deep models maximize the minimum margin in order to achieve high accuracy, but at the same time decrease the \emph{average} margin hence hurting robustness. Our empirical results highlight an intrinsic trade-off between accuracy and robustness for current deep model training. To further address this issue, we propose a new regularizer to explicitly promote average margin, and we verify through extensive experiments that it does lead to better robustness. Our regularized objective remains Fisher-consistent, hence asymptotically can still recover the Bayes optimal classifier.
Proximity Queries for Absolutely Continuous Parametric Curves

In motion planning problems for autonomous robots, such as self-driving cars, the robot must ensure that its planned path is not in close proximity to obstacles in the environment. However, the problem of evaluating the proximity is generally non-convex and serves as a significant computational bottleneck for motion planning algorithms. In this paper, we present methods for a general class of absolutely continuous parametric curves to compute: (i) the minimum separating distance, (ii) tolerance verification, and (iii) collision detection. Our methods efficiently compute bounds on obstacle proximity by bounding the curve in a convex region. This bound is based on an upper bound on the curve arc length that can be expressed in closed form for a useful class of parametric curves including curves with trigonometric or polynomial bases. We demonstrate the computational efficiency and accuracy of our approach through numerical simulations of several proximity problems.
Spotlight: Hot target discovery and localization with crowdsourced photos

Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View (FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems (GIS). Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called “Spotlight”, which performs passive localization using crowdsourced photos. Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system's localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.
Sistem Informasi Manajemen Pengarsipan Surat Masuk Dan Surat Keluar (Studi Kasus : Ma Darul Ihya Bogor)

At MA Darul Ihya Bogor correspondence is an important means of communication. One of the problems in managing letters at the MA Darul Ihya Bogor is the inefficient filing of mail data that still uses a manual system. With the number of incoming letters and outgoing letters each meeting makes a letter that is in the archive so that the search becomes inefficient because not all files are arranged properly. One effort to overcome this problem is to create an in and outgoing mail management information system. The research method used in this study is the method of Lifecycle Development (SDLC) with the Waterfall model. The waterfall model (waterfall) is often also called a linear sequential (linear sequential) model. The results of this study are to make this site-based management information system can facilitate business administration in the process of inputting incoming and outgoing mail data, searching for incoming and outgoing mail data, making reports of incoming and outgoing mail data producing the required information quickly and accurately.
A Genetic Algorithm for Finding a Small and Diverse Set of Recent News Stories on a Given Subject: How We Generate AAAI’s AI-Alert

This paper describes the genetic algorithm used to select news stories about artificial intelligence for AAAI’s weekly AIAlert, emailed to nearly 11,000 subscribers. Each week, about 1,500 news stories covering various aspects of artificial intelligence and machine learning are discovered by i2k Connect’s NewsFinder agent. Our challenge is to select just 10 stories from this collection that represent the important news about AI. Since stories and topics do not necessarily repeat in later weeks, we cannot use click tracking and supervised learning to predict which stories or topics are most preferred by readers. Instead, we must build a representative selection of stories a priori, using information about each story’s topics, content, publisher, date of publication, and other features. This paper describes a genetic algorithm that achieves this task. We demonstrate its effectiveness by comparing several engagement metrics from six months of “A/B testing” experiments that compare random story selection vs. a simple scoring algorithm vs. our new genetic algorithm.
Probing Bayesian Credible Regions Intrinsically: A Feasible Error Certification for Physical Systems

Standard computation of size and credibility of a Bayesian credible region for certifying any point estimator of an unknown parameter (such as a quantum state, channel, phase, etc.) requires selecting points that are in the region from a finite parameter-space sample, which is infeasible for a large dataset or dimension as the region would then be extremely small. We solve this problem by introducing the in-region sampling theory to compute both region qualities just by sampling appropriate functions over the region itself using any Monte Carlo sampling method. We take in-region sampling to the next level by understanding the credible-region capacity (an alternative description for the region content to size) as the average ${l}_{p}$-norm distance $(p&gt;0)$ between a random region point and the estimator, and present analytical formulas for $p=2$ to estimate both the capacity and credibility for any dimension and a sufficiently large dataset without Monte Carlo sampling, thereby providing a quick alternative to Bayesian certification. All results are discussed in the context of quantum-state tomography.
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview

Graph neural networks denote a group of neural network models introduced for the representation learning tasks on graph data specifically. Graph neural networks have been demonstrated to be effective for capturing network structure information, and the learned representations can achieve the state-of-the-art performance on node and graph classification tasks. Besides the different application scenarios, the architectures of graph neural network models also depend on the studied graph types a lot. Graph data studied in research can be generally categorized into two main types, i.e., small graphs vs. giant networks, which differ from each other a lot in the size, instance number and label annotation. Several different types of graph neural network models have been introduced for learning the representations from such different types of graphs already. In this paper, for these two different types of graph data, we will introduce the graph neural networks introduced in recent years. To be more specific, the graph neural networks introduced in this paper include IsoNN, SDBN, LF&amp;ER, GCN, GAT, DifNN, GNL, GraphSage and seGEN. Among these graph neural network models, IsoNN, SDBN and LF&amp;ER are initially proposed for small graphs and the remaining ones are initially proposed for giant networks instead. The readers are also suggested to refer to these papers for detailed information when reading this tutorial paper.
Observation and Simulation of Available Solar Energy at N’Djamena, Chad

The objective of this work is to evaluate the available solar potential at N'Djamena (12˚08N, 15˚04E) from 2017 to 2018.To achieve this goal, we used various datasets and model including: the in situ shortwave radiation (by pyranometer) measurement and sunshine duration (by Campbell-Stokes heliograph) obtained from N'Djamena station, observations from MODIS (aerosol optical depth (AOD) and precipitable water) satellite sensors, and simulations from Streamer radiative code.The results show the presence of a good available solar potential with an annual global potential of 4.71 kWh/m 2 /d.At the intra-seasonal time scale, there are two maximums for the global solar potential.The first maximum is registered in the month of March (spring) with value of 5.7 kWh/m 2 /d and the second in October (autumn) with value of 5.18 kWh/m 2 /d.However, the minimum of global potential is recorded in winter (from December to February) with values around 3.86 kWh/m 2 /d.Then, the measured global irradiation allowed validating the Streamer radiative transfer code with a score of more than 98%.Subsequently, this model was used to simulate direct normal and diffuse irradiation for several types of days (clear, dusty and cloudy days).An examination of the dust influence on solar radiation based on selected cases (AOD = 2.05) indicates a mean decrease of 3.33 and 3.17 kWh/m 2 /d, respectively, for the total and direct normal potential.This corresponds to an increase of the diffuse potential of 0.52 kWh/m 2 /d.Finally, an increase of 5.82 cm of precipitable water per day tends to decrease the overall potential of 0.73 kWh/m 2 /d and the direct normal potential of 1.74 kWh/m 2 /d.For this cloudy day, the potential has increased more than 0.89 kWh/m 2 /d.
Multimodal Segmentation of Brain Tumours in Volumetric MRI Scans of the Brain Using Time-Distributed U-Net

Brain tumour segmentation poses a challenging task even in the eyes of a trained medical practitioner. Traditional machine learning algorithms require hand-coding features from images before they can learn to identify the regions. Deep learning can solve the problem of detecting tumours with precision and even segment it. Neural networks can learn a hierarchical representation of features from the data by itself. We use a time-distributed architecture for U-Net based deep convolutional neural networks (TD-UNET). We tested our network against the MICCAI BRATS 2015 dataset that comprised 220 high-graded gliomas (HGG) and 54 low-graded gliomas (LGG) and yielded a test case accuracy of 58.3%.
Detecting P- and S-wave arrivals with a recurrent neural network

PreviousNext You have accessSEG Technical Program Expanded Abstracts 2019Detecting P- and S-wave arrivals with a recurrent neural networkAuthors: David KirschnerNick HowesConor DalyJoyeeta MukherjeeJunlun LiDavid KirschnerRoyal Dutch ShellSearch for more papers by this author, Nick HowesMathworksSearch for more papers by this author, Conor DalyMathworksSearch for more papers by this author, Joyeeta MukherjeeMathworksSearch for more papers by this author, and Junlun LiUniversity of Science and Technology of China (formerly w/RDSA)Search for more papers by this authorhttps://doi.org/10.1190/segam2019-3215081.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractA LSTM network was used to automatically pick the arrival times of P- and S-waves. The network picks events accurately and quickly, obviating the need for human intervention. Although the LSTM was trained to detect small, local earthquakes in an onshore fold-thrust belt, it is effective in picking small earthquakes in other geologic settings and large earthquakes recorded on global seismic networks based on some initial results.Presentation Date: Wednesday, September 18, 2019Session Start Time: 9:20 AMPresentation Time: 11:00 AMLocation: Poster Station 13Presentation Type: PosterKeywords: artificial intelligence, machine learning, earthquake, multicomponentPermalink: https://doi.org/10.1190/segam2019-3215081.1FiguresReferencesRelatedDetailsCited byRecurrent autoencoder model for unsupervised seismic facies analysisYanhui Zhou and Wenchao Chen15 June 2022 | Interpretation, Vol. 10, No. 3Accurate determination of the first arrival time of elastic wave traveled through rock sample by machine learningJournal of Applied Geophysics, Vol. 203Gated Recurrent Units Based Recurrent Neural Network for Forecasting the Characteristics of the Next Earthquake29 September 2021 | Cybernetics and Systems, Vol. 53, No. 2 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION David Kirschner, Nick Howes, Conor Daly, Joyeeta Mukherjee, and Junlun Li, (2019), "Detecting P- and S-wave arrivals with a recurrent neural network," SEG Technical Program Expanded Abstracts : 2659-2662. https://doi.org/10.1190/segam2019-3215081.1 Plain-Language Summary Keywordsartificial intelligencemachine learningearthquakemulticomponentPDF DownloadLoading ...
Evaluating Software Developers’ Acceptance of a Tool for Supporting Agile Non-Functional Requirement Elicitation

Due to the need for flexibility to requirements changes, agile software development methods have been attracting the attention of academic and industrial domains.Unlike traditional approaches, agile methods focus on the rapid delivery of business value to customers through empirical and incremental development processes.Despite being effective in delivering quality functional requirements, agile practices generally neglect non-functional requirements until the later stages of software development.However, neglecting non-functional requirements during requirements analysis can lead to project failures.In this paper, we present the NFRec tool, which aims to support software developers in the elicitation of non-functional requirements in the context of agile software development.Additionally, we report the results from a case study to evaluate the acceptance of the NFRec tool from the point of view of software developers of four projects from a Brazilian software company.To gather information about the tool acceptance, we applied a questionnaire based on the indicators from the Technology Acceptance Model.Overall, the four teams considered the NFRec tool useful and easy to use for supporting the management of non-functional requirements in agile projects.
Bitcoin as an alternative digital currency: exploring the publics' perception vs. experts

This paper attempts to find out if the view of the public coincides with the opinion of the experts on the Bitcoin's subject and to determine if is information can be implemented in order to establish a use of Bitcoin in our everyday lives. During the first stage of this research, methods and rules of analysis such as descriptive statistics and tables and charts were used. Firstly, a questionnaire was used and secondly, the answers were gathered and classified according to the participants' choices. During the second stage, the data was collected with the use of random sampling, while various techniques, such as simple and multiple regressions were utilised for analysing the results. The conclusion is that the public perceives Bitcoin differently than the experts do. Individuals believe that Bitcoin is mainly a means of transactions/payments, contrary to the experts' opinion, which is that it is foremost an investment asset.
Explicit Shape Encoding for Real-Time Instance Segmentation

In this paper, we propose a novel top-down instance segmentation framework based on explicit shape encoding, named \textbf{ESE-Seg}. It largely reduces the computational consumption of the instance segmentation by explicitly decoding the multiple object shapes with tensor operations, thus performs the instance segmentation at almost the same speed as the object detection. ESE-Seg is based on a novel shape signature Inner-center Radius (IR), Chebyshev polynomial fitting and the strong modern object detectors. ESE-Seg with YOLOv3 outperforms the Mask R-CNN on Pascal VOC 2012 at mAP$^r$@0.5 while 7 times faster.
FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems

These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this paper. In the proposed algorithm, search factors are indeed members of the community who try to improve the community by ‘following’ each other. FOA implemented on 23 well-known benchmark test functions. It is compared with eight optimization algorithms. The paper also considers for solving optimal placement of Distributed Generation (DG). The obtained results show that FOA is able to provide better results as compared to the other well-known optimization algorithms.
HetEnc: a deep learning predictive model for multi-type biological dataset

Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a single or multi-task classification problem; however, is critical and challenging. In this study, we proposed HetEnc, a novel deep learning-based approach, for information domain separation.HetEnc includes both an unsupervised feature representation module and a supervised neural network module to handle multi-platform gene expression datasets. It first constructs three different encoding networks to represent the original gene expression data using high-level abstracted features. A six-layer fully-connected feed-forward neural network is then trained using these abstracted features for each targeted endpoint. We applied HetEnc to the SEQC neuroblastoma dataset to demonstrate that it outperforms other machine learning approaches. Although we used multi-platform data in feature abstraction and model training, HetEnc does not need multi-platform data for prediction, enabling a broader application of the trained model by reducing the cost of gene expression profiling for new samples to a single platform. Thus, HetEnc provides a new solution to integrated gene expression analysis, accelerating modern biological research.
A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook

To analyze the content of publications within the medical Natural Language Processing (NLP) domain in 2018.Automatic and manual pre-selection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.Two best papers have been selected this year. One dedicated to the generation of multi- documents summaries and another dedicated to the generation of imaging reports. We also proposed an analysis of the content of main research trends of NLP publications in 2018.The year 2018 is very rich with regard to NLP issues and topics addressed. It shows the will of researchers to go towards robust and reproducible results. Researchers also prove to be creative for original issues and approaches.
Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

High-Performance Computing (HPC) is rapidly moving towards the adoption of nodes characterized by an heterogeneous set of processing resources. This has already shown benefits in terms of both performance and energy efficiency. On the other side, heterogeneous systems are challenging from the application development and the resource management perspective. In this work, we discuss some outcomes of the MANGO project, showing the results of the execution of real applications on a emulated deeply heterogeneous systems for HPC. Moreover, we assessed the achievements of a proposed resource allocation policy, aiming at identifying a priori the best resource allocation options for a starting application.
Adding Cues to Binary Feature Descriptors for Visual Place Recognition

In this paper we propose an approach to embed multi-dimensional continuous cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present a concrete application of our methodology, demonstrating the considered type of continuous cue. Additionally, we conducted a broad quantitative and comparative evaluation on that application, covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.