Reviewerly/exHarmony huggingface dataset:
Designing SOA-based BATAN Public Services with Restful Web Service

The rapid development of the use of ICTs through e-government programs launched by the government, especially in the public service sector, has created various innovations based on information systems that cause incompatibility with business systems. Each work unit creates its own information system for business needs so that there is disharmony if it is to be drawn into the public service system at the top level. SOA is needed as a solution to overcome this problem where communication services that were originally P2P were converted into communication between services on the bus to achieve business goals optimally despite various architectures, platforms, programming languages. ESB is a very appropriate middleware for the integration of these background differences. RESTful web services are used to connect each of the existing applications both internal and external applications through ESB.
A Geometric Convolutional Neural Network for 3D Object Detection

We propose a method for accurate 3D vehicle detection based on geometric deep neural networks. From only a single RGB image, the framework is able to recover the 3D positions and predict 3D bounding boxes. In particular, the algorithm leverages single image depth estimation and semantic segmentation to produce 3D point cloud for specific objects. By geometrically constraining the object dimensions, an accurate and stable 3D bounding box which tightly fits into the real object can be estimated. We verify the effectiveness and robustness of our method by comparing with other recent state-of-art methods on the challenging KITTI 3D benchmark dataset as well as synthetic Virtual KITTI dataset. Without requiring ground truth 3D labels, our method is able to produce competitive and robust performance in 3D scene understanding and detection.
Geometric phase analysis method using a subpixel displacement match algorithm

The geometrical phase analysis (GPA) method, which is an efficient and powerful noncontact method to obtain the strain field, has already been widely applied in deformation measurement in micro- and nano-scale. It is easy to get the strain field accurately; however, the displacement field is unreliable in some cases. Therefore, a subpixel displacement match method hereby is applied in the GPA method for the first time, to the best of our knowledge, to overcome this defect. The presented algorithm’s limit error of 0.01 pixel under ideal conditions can match two corresponding local areas in reference and deformation image, and, thus, the displacement with subpixel precision of this point can be established. Owing to the continuity of the displacement field, the displacements of other points can be obtained subsequently. The error that is associated with the existing method will be dealt with in detail and verified by simulation further. Combined with simulation, the performance of the presented method is demonstrated; furthermore, the noise introduced by the imaging system is taken into consideration. Finally, a typical bending test was performed, and the result agrees well with the theoretical analysis. Both the simulation and experiment results prove that the presented method is effective and robust.
Efficiently Learning and Sampling Interventional Distributions from Observations.

We study the problem of efficiently estimating the effect of an intervention on a single variable (atomic interventions) using observational samples in a causal Bayesian network. Our goal is to give algorithms that are efficient in both time and sample complexity in a non-parametric setting. Tian and Pearl (AAAI `02) have exactly characterized the class of causal graphs for which causal effects of atomic interventions can be identified from observational data. We make their result quantitative. Suppose P is a causal model on a set $\vec{V}$ of n observable variables with respect to a given causal graph G with observable distribution $P$. Let $P_x$ denote the interventional distribution over the observables with respect to an intervention of a designated variable X with x. Assuming that $G$ has bounded in-degree, bounded c-components ($k$), and that the observational distribution is identifiable and satisfies certain strong positivity condition, we give an algorithm that takes $m=\tilde{O}(n\epsilon^{-2})$ samples from $P$ and $O(mn)$ time, and outputs with high probability a description of a distribution $\hat{P}$ such that $d_{\mathrm{TV}}(P_x, \hat{P}) \leq \epsilon$, and: 1. [Evaluation] the description can return in $O(n)$ time the probability $\hat{P}(\vec{v})$ for any assignment $\vec{v}$ to $\vec{V}$ 2. [Generation] the description can return an iid sample from $\hat{P}$ in $O(n)$ time. We also show lower bounds for the sample complexity showing that our sample complexity has an optimal dependence on the parameters $n$ and $\epsilon$, as well as if $k=1$ on the strong positivity parameter.
Visual Sequence Place Recognition with Improved Dynamic Time Warping

Recently, the methods based on Deep Convolutional Neural Networks have achieved state-of-the-art performance in visual place recognition. In this paper, we utilize deep feature representations of images and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n3) to O(n). Extensive experiments are conducted on two challenging datasets, the results show high precision-recall characteristics in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which makes us believe that the spatiotemporal information contained in the image sequence is significant for place recognition. Furthermore, the proposed method outperforms the classical sequence-based method SeqSLAM [1].
Fujian Anti-Disaster Baseline Power Grid Planning Method Based on Network-Source-Load Cooperation

In order to reduce the disastrous blackouts caused by the damage of natural disasters to power facilities, this paper proposes the method of differential planning to construct a anti-disaster baseline power grid, starting from the view of protecting key power users and balancing the relationship between input and benefit of anti-disaster. The methods and principles of systematically constructing the power grid based on network-source-load cooperation, such as the selection of guaranteed scope of key users, the planning of anti-disaster guaranteed power supply and the planning of baseline power grid, are projected from. Taking the power grid of a city in Fujian province as an example, this paper constructs a anti-disaster baseline power grid scheme, and puts forward some suggestions for the differential construction of anti-disaster baseline power grid.
Las ayudas en indagaciones científicas escolares mediadas por herramientas tecnológicas. Investigaciones de la última década

This paper presents a literature review about the investigations focused on the study of technology as a support to inquiry-based science learning in primary education over the last decade.44 studies are analyzed and it is disclosed information about the type of supports and technological tools, technology adequacy to learners needs and the evolution of teachers' guidance in these contexts.Educational research in this area is clearly insufficient given the social significance of the tackled questions.
An image compressed sensing algorithm based on adaptive nonlinear network*

Traditional compressed sensing algorithm is used to reconstruct images by iteratively optimizing a small number of measured values. The computation is complex and the reconstruction time is long. The deep learning-based compressed sensing algorithm can greatly shorten the reconstruction time, but the algorithm emphasis is placed on reconstructing the network part mostly. The random measurement matrix cannot measure the image features well, which leads the reconstructed image quality to be improved limitedly. Two kinds of networks are proposed for solving this problem. The first one is ReconNet's improved network IReconNet, which replaces the traditional linear random measurement matrix with an adaptive nonlinear measurement network. The reconstruction quality and anti-noise performance are greatly improved. Because the measured values extracted by the measurement network also retain the characteristics of image spatial information, the image is reconstructed by bilinear interpolation algorithm (Bilinear) and dilate convolution. Therefore a second network USDCNN is proposed. On the BSD500 dataset, the sampling rates are 0.25, 0.10, 0.04, and 0.01, the average peak signal-noise ratio (PSNR) of USDCNN is 1.62 dB, 1.31 dB, 1.47 dB, and 1.95 dB higher than that of MSRNet. Experiments show the average reconstruction time of USDCNN is 0.2705 s, 0.3671 s, 0.3602 s, and 0.3929 s faster than that of ReconNet. Moreover, there is also a great advantage in anti-noise performance.
Automated Scoring of Extended Spontaneous Speech

In this chapter we provide an overview of SpeechRater ®, a state-of-the-art system for AS and feedback for extended spontaneous speech that is used operationally in a practice context by language learners preparing for a high-stakes international language test. In addition to a detailed description of the components of this automated speech scoring system, we provide the rationales and motivations for key choices in its design and system architecture. Furthermore, we place SpeechRater in the context of automated speech scoring in general, discuss how differences in task types and response characteristics, and use scenarios necessitate different choices with respect to the design and implementation of different system components. We conclude the chapter with a critical discussion of the current state-of-the-art of spontaneous speech scoring, its remaining challenges, and an outlook into future research as well as operational practice in this area.
A Spatial Density and Phase Angle Based Correlation for Multi-type Family Photo Identification

Due to change in mindset and living style of humans, the numbers of diversified marriages are increasing all around the world irrespective of race, color, religion and culture. As a result, it is challenging for research community to identify multi type family photos, namely, normal family (family of the same race, religion or culture), multi-culture family (family of different culture, religion or race) from the family and non-family photos (images with friends, colleagues, etc.). In this work, we present a new method that combines spatial density information with phase angle for multi-type family photo classification. The proposed method uses three facial key points, namely, left-eye, right-eye and nose, for the features which are based on color, roughness and wrinkleless of faces, these are prominent for extracting unique cues for classification. The correlations between features of Left & Right Eyes, Left Eye & Nose and Right Eye & Nose are computed for all the faces in an image. This results in feature vectors for respective spatial density and phase angle information. Furthermore, the proposed method fuses the feature vectors and feeds them to the Convolutional Neural Network (CNN) for the classification of the above-three class problem. Experiments conducted on our database which contains three classes, namely, multi-cultural, normal and non-family images and the benchmark databases (due to Maryam et al. and Wang et al.) which contain two class-family and non-family images, show that the proposed method outperforms the existing methods in terms of classification rate for all the three databases.
Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute–funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders’ titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was “intersections between informatics, data science, and population science.” We conclude with a discussion on “hot topics” on the horizon for cancer informatics.
Saccade and Fix Detection from EOG Signal

The physical activities of human eye constitutes of three repetitive activities, viz saccade, fix and blink. Blink being an involuntary action, the saccades and fix correspond to different cognitive actions of human being. Often this actions are guided with active correlation of actions of neurological activities of the brain. A saccade is defined by the movement of the eyeball from one viewpoint to another. A saccade action can be captured through the EOG signal which is characterized by sudden deflection of the voltage fluctuation in the EOG. Study and analysis of saccadic movement and its pattern has many applications which can lead to understanding of cognitive capabilities of human beings.
Model Pembelajaran Respons Verbal dalam Kemampuan Berbicara

The purpose of this study is to find out the effect of the implementation of verbal response learning model to the speaking ability students of Class X Madrasah Aliyah PP DDI As-Salman Allakuang Sidenreng Rappang Regency. This research was conducted in Madrasah Aliyah PP DDI As-Salman Allakuang Sidenreng Rappang Regency. The research method used quantitative method. The subject of the research is the students of class X Madrasah Aliyah PP DDI As-Salman Allakuang Sidenreng Rappang Regency academic year 2016/2017. The data collection technique used a test with two stages of data retrieval, the first test to determine the speaking ability before the students were given treatment (pretest) and the second stage to determine students' speaking ability after being given treatment (posttest). Results based on db of 52 converted to t-table at the 5% significance level obtained the price of 1.671. Criteria of submission are if t-test ≥ t-tabel then Ha accepted and Ho rejected, otherwise if t-test ≤ t-table then Ha rejected and Ho accepted. It turns out that t-test is magnitude 2.33 more than t-table, hence alternative hypothesis accepted and null hypothesis rejected. Based on these results it can be concluded that the application of verbal response learning model significantly affects the speaking ability of grade X Madrasah Aliyah PP DDI As-Salman Allakuang of Sidenreng Rappang Regency.
Synchronously-pumped OPO coherent Ising machine: benchmarking and prospects

The coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs) that solves for the ground state of Ising problems through OPO bifurcation dynamics. Here, we present experimental results comparing the performance of the CIM to quantum annealers (QAs) on two classes of NP-hard optimization problems: ground state calculation of the Sherrington-Kirkpatrick (SK) model and MAX-CUT. While the two machines perform comparably on sparsely-connected problems such as cubic MAX-CUT, on problems with dense connectivity, the QA shows an exponential performance penalty relative to CIMs. We attribute this to the embedding overhead required to map dense problems onto the sparse hardware architecture of the QA, a problem that can be overcome in photonic architectures such as the CIM.
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network

We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles in out of distribution detection on notable difficult dataset pairs such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN.
Reflecting upon Perceptual Speed Tests in Information Retrieval

Perceptual Speed (PS) is a cognitive ability defined by an individual's accuracy and speed to scan information while completing visual search tasks. Prior studies using PS tests have demonstrated that PS affects multiple factors in Information Retrieval (IR), such as a user's search performance, interaction with the system, time spent completing tasks, and subjective impression of their workload. With greater knowledge of PS, systems could be designed that accommodate users with low PS to improve their overall search experience. However, in this perspectives paper, we analyse how PS tests have been used in IR, and identify multiple uncertainties regarding PS content, administration, analysis, and reporting of findings. Consequently, we aim to stir discussion between IR researchers by drawing awareness to these issues. As a result, we further discuss challenges involved in advancing how future PS tests are used in IR. Finally, we propose recommendations that have the potential for enhancing the reliability and validity of current PS tests.
Real-time Robust Object Detection Using an Adjacent Feature Fusion-based Single Shot Multibox Detector

A single shot multibox detector (SSD) is used as a baseline for many object detection networks, since it can provide sufficiently high accuracy in real time. However, it cannot deal with objects of various sizes, because features used in an SSD are not robust to multi-scale objects. To solve this problem, we present an improved feature pyramid for using multi-scale context information. The proposed feature pyramid fuses only adjacent features of the conventional SSD to achieve high accuracy without decreasing the processing speed. Our detector, with a 320×320 input, achieved 79.1% mean average precision (mAP) at 63 frames per second on a Pascal Visual Object Classes Challenge 2007 test set using a single Nvidia 1080 Ti graphics processing unit. This result shows better performance than existing SSDs.
Hardware Architecture and Algorithm Co-design for Multi-Layer Photonic Neuromorphic Network with Excitable VCSELs-SA

We design a multi-layer photonic spiking neural network with excitable VCSELs-SA. Numerical results based on the rate-equation models show that the proposed neuromorphic network architecture is capable of solving the classical XOR problem by supervised-learning.
Multimodal Interaction for Real and Virtual Environments

Multimodal interfaces can leverage the information from multiple modalities to provide robust and error-free interaction. Early multimodal interfaces demonstrate the feasibility of building such systems but focused on specific applications. The challenge in building adaptive systems is lack of techniques for input data fusion. In this direction, we have developed a multimodal head and eye gaze interface and evaluated it in two scenarios. In aviation scenario, our interface has reduced the task time and perceived cognitive load significantly from the existing interface. We have also studied the effect of various output conditions on user's performance in a Virtual Reality (VR) task. Further, we are making our proposed interface to include additional modalities and building novel haptic and multimodal output systems for VR.
Contributions of Ivan Illich to Education in a Digital Society

Ivan Illich was a heavy critic of traditional schooling. His proposals were disregarded, perhaps too quickly, for various reasons. This paper, based on review research, aims to add to a current (re)reading of Illich, seeking to answer the following question: what is the relevance of Illich’s proposal for a successful education in an increasingly digitalised society? The results of this research allow concluding, on the one hand, that Illich’s proposal to replace strict schooling with (self)training networks in a society that is increasingly digitalised and linked by the internet may offer potential benefits, and it is worth, at least, of an in-depth analysis. On the other hand, provocative scholars that allow us to get out of any ideologically and socially delimited system have the merit of helping to provide instruments that enable a better understanding of the present and, consequently, a rationale for the options for the future. Ivan Illich is one of these scholars.
An alpha matting algorithm based on collaborative swarm optimization for high-resolution images

High-resolution image matting is one of the challenges in image composition and foreground extraction. It is essentially a large-scale combinatorial optimization problem for foreground/background pixel pairs. However, little attention has been paid to this issue. A multiclass collaborative optimization strategy based on RGB color clustering is proposed to reduce the dimension of this problem, addressing the issues caused by its ultrahigh dimension. This paper presents a collaborative feedback grouping strategy to solve this large-scale combinatorial optimization problem. Based on these two strategies, a competitive swarm optimization algorithm based on group collaboration (GC-CSO) is proposed. Its performance is verified experimentally by using an alpha matting dataset, showing that it can significantly reduce the dimension of the image matting problem and outperform the existent large-scale optimization algorithms with grouping strategies in the alpha matte comparison.
Training a Chatbot with Microsoft LUIS

Microsoft LUIS is a natural language understanding service used to train Chatbots. Imbalance in the utterance training set may cause the LUIS model to predict the wrong intent for a user's query. We discuss this problem and the training recommendations from Microsoft to improve prediction accuracy with LUIS. We perform batch testing on three training sets created from two existing datasets to explore the effectiveness of these recommendations.
CAPTCHA Image Generation Using Style Transfer Learning in Deep Neural Network

CAPTCHA is widely used as a security solution to prevent automated attack tools on websites. However, CAPTCHA is difficult to recognize human perception when it gives a lot of distortion to have resistance against the automated attack. In this paper, we propose a method to deceive the machine while maintaining the human perception rate by applying the style transfer method. This method creates a style-plugged-CAPTCHA image by combining the styles of different images while maintaining the content of the original CAPTCHA sample. We used 6 datasets in the actual site and used Tensorflow as the machine learning library. Experimental results show that the proposed method reduces the recognition rate of the DeCAPTCHA system to 3.5% while maintaining human perception.