Reviewerly/exHarmony huggingface dataset:
Will This Course Increase or Decrease Your GPA? Towards Grade-Aware Course Recommendation.

In order to help undergraduate students towards successfully completing their degrees, developing tools that can assist students during the course selection process is a significant task in the education domain. The optimal set of courses for each student should include courses that help him/her graduate in a timely fashion and for which he/she is well-prepared for so as to get a good grade in. To this end, we propose two different grade-aware course recommendation approaches to recommend to each student his/her optimal set of courses. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student’s GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in both approaches, we adapt two widely-used representation learning techniques to learn the optimal temporal ordering between courses. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the grade-aware course recommendation methods can do better on recommending more courses in which the students are expected to perform well and recommending fewer courses which they are expected not to perform well in than grade-unaware course recommendation methods.
Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks

Accurate localization of catheters or guidewires in fluoroscopy images is important to improve the stability of intervention procedures as well as the development of surgical navigation systems. Recently, deep learning methods have been proposed to improve performance, however these techniques require extensive pixel-wise annotations. Moreover, the human annotation effort is equally expensive. In this study, we mitigate this labeling effort using generative adversarial networks (cycleGAN) wherein we synthesize realistic catheters in flouroscopy from localized guidewires in camera images whose annotations are cheaper to acquire. Our approach is motivated by the fact that catheters are tubular structures with varying profiles, thus given a guidewire in a camera image, we can obtain the centerline that follows the profile of a catheter in an X-ray image and create plausible X-ray images composited with such a centerline. In order to generate an image similar to the actual X-ray image, we propose a loss term that includes perceptual loss alongside the standard cycle loss. Experimental results show that the proposed method has better performance than the conventional GAN and generates images with consistent quality. Further, we provide evidence to the development of methods that leverage such synthetic composite images in supervised settings.
RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension.

Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of RC systems. To address this issue, we introduce R4C, a new task for evaluating RC systems' internal reasoning. R4C requires giving not only answers but also derivations: explanations that justify predicted answers. We present a reliable, crowdsourced framework for scalably annotating RC datasets with derivations. We create and publicly release the R4C dataset, the first, quality-assured dataset consisting of 4.6k questions, each of which is annotated with 3 reference derivations (i.e. 13.8k derivations). Experiments show that our automatic evaluation metrics using multiple reference derivations are reliable, and that R4C assesses different skills from an existing benchmark.
Development of a Lightweight, Low-cost, Self-balancing Personal Mobility Vehicle for Autonomous Indoor Navigation

Travelling along routine paths on foot is an exhausting and mundane task. Current personal mobility vehicles (PMVs), which address this issue, are driven manually and are too heavy to be carried by the user when needed. As a solution to this, an autonomous, self-balancing, lightweight, compact and low-cost PMV is developed. The device is capable of simultaneously self-balancing and navigating autonomously in an indoor environment. Moreover, it can be manually controlled using the developed smart mobile phone application. Self-balancing, localization, navigation and obstacle avoidance are achieved in real time using on-board sensors and processing units. Systematic experiments are carried out with the developed PMV in typical indoor environments in order to assess the robustness of the above features. The results show that the robot is able to successfully achieve its navigation tasks both with and without a human passenger. In addition, feedback obtained from the general public during a 3-day exhibition shows the public acceptance of the vehicle.
Non-Volatile Photonic Weights and their Impact on Photonic Reservoir Computing Systems

Reservoir computing (RC) is a promising implementation of a non-von Neumann computing architecture. It is well-suited to solve time-dependent and dynamic problems like speech recognition or bitwise operations. In principal integrated photonic RC offers energy-efficient, high-speed signal processing capabilities. However, current photonic RC systems perform the linear combination of the complex reservoir states in software. Software weights are ideal (high precision, no drift), but limit the processing speed and energy efficiency of the RC system. Here, we discuss a hardware implementation of photonic weights compatible with silicon photonic RC architectures. First, we demonstrate non-volatile synaptic weights based on ferroelectric barium titanate (BTO) thin films. Second, we explore how imperfections in these hardware weights impact the reservoir performance.
Order effect on relevance judgment: An exploratory study on the detection of quantum interference

ABSTRACT This poster explores the existence of order effect on relevance judgment, especially the interference of one document on the other while evaluating the relevance. A between‐subject experiment with 78 undergraduate students was reported. The results demonstrated the statistically significant existence of order effect for some topics and spotted quantum interference while evaluating the relevance of documents. This preliminary study provides insights into how documents' evaluation is contextualized and interdependent with each other. It has some implications for future theoretical and practical research on the human cognitive aspect of relevance judgment in information retrieval.
Design and implementation of forecast and early warning system for offshore platform based on GIS

A forecast and early warning system for offshore platform was developed to reduce security risks of offshore operations. The tool uses an ArcGIS Server as electronic chart display and system platform, in combination with Silverlight, WCF Service and AIS. The system incorporates management of offshore platforms, subsea pipelines and warning zones, as well as real-time monitoring and early warning for typhoon and ships around the platform. This system has been put into use on offshore platforms, and the system's powerful early warning capabilities, excellent experience and performance have been recognized by the user.
PENGARUH PENGGUNAAN MEDIA POP UP BOOK TERHADAP KECERDASAN LINGUISTIK ANAK USIA 5-6 TAHUN DI RA NURHAYATI KECAMATAN MEDAN TEMBUNG

&lt;p&gt;&lt;em&gt;This research was conducted in RA Nurhayati, Medan Tembung District, on February 25 to March 9, 2019. The type of research carried out was an experimental study with a quantitative approach. This research method uses Quasi Experimental Designs with Non Equevalent Control Group Design. The population of 29 children by sampling using the Total Sampling technique, so that the entire population is used as a sample. This study aims to determine: (1) Children's linguistic intelligence (2) Children's linguistic intelligence using pop up book media (3) Significant influence of pop up book media on children's linguistic intelligence in RA Nurhayati District Medan Tembung T.A. 2018/2019. The results showed that the use of pop-up media influenced linguistic intelligence of children aged 5-6 years. This is illustrated by the results of the study obtained an average pre-test 26.72 and an average post-test of 52.08. So that the linguistic intelligence of children in the experimental class is better than the control class with an average pre-test 25 and an average post-test of 44.83. Based on the results of hypothesis testing obtained tcount&amp;gt; ttable is 3.226&amp;gt; 2.131 with a significant number of α = 0.05. Thus the hypothesis Ho is rejected and Ha is accepted so that it can be stated There is a Significant Effect of the Use of Pop Up Book Media on Linguistic Intelligence of Children Aged 5-6 Years in RA Nurhayati Medan Tembung District T.A. 2018/2019.&lt;/em&gt;&lt;/p&gt;
A Method of Firing Nearly Invisible 2D Code with Public/Secret Information onto Ceramic Products

We introduce a method for firing nearly invisible, two-dimensional (2D) code on ceramic product surfaces and a 2D code-reader system, equipped with a smartphone and an ultraviolet (UV) light, to extract encoded character information (public and encrypted secret information). We created the nearly invisible 2D code to add character information to ceramic surfaces without impairing the ceramics’ colors and patterns as much as possible. When irradiated by UV light, the code is visible to the naked eye, emitting a pale-blue light. The 2D code-reader system uses a smartphone camera and a 2D code-recognition algorithm to extract character information from the 2D code reflected on the smartphone’s display. However, only smartphones that have access to digital keys can decrypt the encrypted secret information. We successfully fired this 2D code on ceramic surfaces and extracted public and secret information by using iOS and Android-based 2D code-reader system.
Main Interdisciplinary Aspects of the Development and Program Implementation of Electronic Textbooks on Technical Disciplines on the Example of SDA MAI

Distance learning systems in the form of electronic textbooks being actively developed and used in real educational process of school and university. Such a spread of technology necessitates scientific research in the field of their design and application. Scientific researches currently being conducted in the framework of didactics, psychology, information technologies, computational mathematics, are multidisciplinary, since the solution of didactic problems is the object of research in other scientific fields. At the same time, there are many prerequisites for research, the subject of which can be an electronic textbook. Reason of the need for interdisciplinary research in the development of electronic textbooks, as a result of solving a multitasking problem, is presented as the goal of this article. The main part of the work presents the key features of the architecture of a distance learning system (DLS) from the standpoint of designing it as a software product. In addition, the proposed description of specific mathematical algorithms that have been implemented in practice in the construction of electronic textbooks of the Moscow Aviation Institute, as well as new mathematical approaches to the design of an electronic textbook. One of such approaches is represented by graphed orientation. The model of generating of time-limited text is analyzed, in which weighted convolution of normalized values, namely deviations of the test complexity from a given level and quantile of test run time, is used as an optimization criterion. The basic modern technologies and programming tools that are used in designing the architecture of the control shell and make it possible to effectively apply the obtained mathematical algorithms when solving didactic problems are described. The conclusion contains a conclusion about the effectiveness of the cumulative research of specialists in various scientific fields, which is supported by the implementation in practice.
Simulation of Air Velocity and Its Effects on Mini Plant Factory Temperature and Relative Humidity

Limited land for agricultural cultivation, especially in urban areas, makes it difficult to provide healthy and sustainable food.This research is part of the development of the Smart Mini Plant Factory (SMIPY), a household scale mini plant factory used for vegetable production using hydroponic technology to support urban farming.SMIPY is equipped with sensors for monitoring environmental conditions, automatic nutrition control systems, and artificial lighting using Light Emitting Diode (LED).In particular, this research aims to simulate airflow and analyze its effects on temperature and relative humidity inside SMIPY using Computational Fluid Dynamics (CFD).Data validation were done by collecting temperature and relative humidity inside SMIPY with air velocity 0 m/s, 1 m/s, 1.5 m/s and 1.8 m/s.The error obtained from the data in temperature and relative humidity were 1.69+1.47%and 2.94+1.57%respectively.Thus, the result showed that the CFD simulation was reliable to predict temperature and humidity inside SMIPY.The airflow moved vertically, but an air turbulance occured between two opposite air inlets.The higher the air velocity, the lower the temperature and the higher the relative humidity.
Lateral Astroturfing Attacks on Twitter Trending Topics.

Astroturfing attacks use automated accounts to artificially propel a chosen keyword to the top of Twitter trending topics. Lateral astroturfing is a sophisticated subset of such attacks in which the automated tweets 1) are posted by compromised accounts and 2) are deleted immediately after they are created. The former makes the attack more effective and the latter aids in evading detection. We present the first large-scale analysis of lateral astroturfing attacks. We detected over 20 thousand astroturfing accounts that were used between February 2019 and June 2019 to manipulate 3,710 unique keywords --- at least 10% of daily trending topics in the region analyzed. Lateral astroturfing pollutes trending topics; allows for the manipulation of users' opinions; and permits content that could otherwise be filtered by the platform, such as illicit advertisements. Our results aid in understanding user manipulation on social media and more generally shed light on the type of adversarial behavior that arises to evade detection.
A Multiband Structure based on Hammerstein Model for Nonlinear Audio System Identification

In recent years, the increasing computational power has enabled the large-scale use of techniques for modeling real world systems and their non-linear characteristics. Hammerstein model is a well known structure for non-linear identification of memoryless systems. The proposed work aims at showing two innovative approaches based on multiband structure adaptive filters for Hammerstein adaptive identification process. In particular two multiband structures have been developed using the discrete cosine transform and the wavelet transform for the filterbank construction. The proposed approaches have been compared with a fullband normalized least mean square (NLMS) Hammerstein model, taking into consideration synthetic and real world audio devices.
Multi-objective GA for Collision Avoidance on Robot Manipulators Based on Artificial Potential Field

This paper presents a path planning strategy for robotic manipulators based on genetic algorithms, dual quaternions and artificial potential field, designing a multi-objective function that allow trajectories be planned avoiding collisions in the workspace and singularity-free kinematic restrictions for manipulators as an optimization problem, satisfying position and orientation conditions. Its analysis is based on the problem of generating a trajectory followed by a sequence of coordinated movements capable of moving the manipulator to perform tasks in the workspace, the problem is not only generated these movements, but also implement strategies that define the path with tools that are easy to implement and avoid obstacles autonomously. Robot kinematics solved by dual quaternion can be used to combine translation with orientation on robotic manipulators in a systematic way, simplifying calculation operations compatible with conventional methods. The artificial potential field approach has been extended to collision avoidance for all manipulator links. A genetic algorithm is used to solve the problem, which the fitness of the problem can be measured by a multi-objective function that involves the distance between the initial and desired position/orientation, minimum joint displacement, dual quaternion configuration, the use of attraction potential to the goal and a repulsion potential to the obstacles and its own links. This method has been implemented in MatLab© for an ABB© IRB1600 robot. Collision avoidance demonstrations have been performed by simulating equipment and static objects in the robot’s workspace.
FreeStyle: A Sketch-Based Wireframing Tool

User interfaces have classically involved WIMP (windows, icons, mouse, and pointing) paradigm. Current wireframing tools in the early stages of web interface design focus on using drag-and-drop features, that is, selecting elements from a menu and inserting them in the interface. Most designers prefer to sketch early interface ideas on paper. A system that allows them to draw sketches would make the process more intuitive, thus going beyond traditional methods of website interface designing. We have developed an interactive tool called FreeStyle that allows designers the ability to quickly sketch a web-based interface electronically. The designer can draw web-based HTML elements on the canvas and in response, FreeStyle transforms the sketch into an interface. Additionally, we also provide the functionality of downloading the code developed from the sketch.
A Deep Learning Approach for Hybrid Hand Gesture Recognition

Emerging depth sensors and new interaction paradigms enable to create more immersive and intuitive Natural User Interfaces by recognizing body gestures. One of the vision-based devices that has received plenty of attention is the Leap Motion Controller (LMC). This device models the 3D position of hands and fingers and provides more than 50 features such as palm center and fingertips. In spite of the fact that the LMC provides such useful information of the hands, developers still have to deal with the hand gesture recognition problem. For this reason, several researchers approached this problem using well-known machine learning techniques used for gesture recognition such as SVM for static gestures and DTW for dynamic gestures. At this point, we propose an approach that applies a resampling technique based on fast Fourier Transform algorithm to feed a CNN+LSTM neural network in order to identify both static and dynamic gestures. As far as our knowledge, there is no full dataset based on the LMC that includes both types of gestures. Therefore, we also introduce the Hybrid Hand Gesture Recognition database, which consists of a large set of gestures generated with the LMC, including both type of gestures with different temporal sizes. Experimental results showed the robustness of our approach in terms of recognizing both type of gestures. Moreover, our approach outperforms other well-known algorithms of the gesture recognition field.
Tactical Rings: A Visualization Technique for Analyzing Tactical Patterns in Tennis

Sports data visualization can be a useful tool for analyzing or presenting sports data. In this paper, we present a new technique to visualize and analyze shot-by-shot tactical patterns in tennis. Previous work in tennis data analysis and visualization focus more on high-level statistics and overviews such as heat-maps but did not handle data analytics at micro-level. Our visualization technique can reveal patterns and imbalances in a tennis match that lead to a deeper understanding of the game. We demonstrate the application and benefits of our visualization technique with case studies.
You’re not the POS of me: part-of-speech tagging as a markup problem

Part of speech tagging, labeling every token in a text with its grammatical category, is a complicated business. Natural language is messy, especially when that language consists of social-media conversations between bilinguals. The process can be done with or without human intervention, in a supervised or unsupervised manner, on a statistical basis or by the application of rules. Often, it involves a combination of these methods. It is, on the one hand, an obvious markup problem: mark up the tokens with appropriate grammatical categories. But it is also much richer than that. Theoretical problems that have been identified in the domain of markup can throw light on the problem of grammatical category disambiguation. Topics considered include subjectivity and objectivity, the semantics of tag sets, licensing of inference, proleptic and metaleptic markup, and the interesting characteristics of the Welsh “verbnoun”.
Effect of Digitally Augmented Toys on Young Children's Experience in Pretend Play

While children have shown a surge of interest and engagement in interactive toys, it is still not clear whether interactive toys using digital technology can play a productive and positive role in promoting the experiential outcomes of pretend play, such as creativity and social interaction. Thus, this Ph.D. research focuses on exploring how children adopt and utilize interactive toys in symbolic and social ways and what interactional design features contribute to their playful experience, within a pretend play context. To this end, instances ranging from experience prototypes and a tabletop toy system to a wearable device with the use of digital augmentation were proposed and investigated - in particular, visual and auditory ways - as interventions for pretend play. The results to date imply that children utilize digital augmentation as an impetus to assign varying symbolic meanings to toys and as a trigger to interact and become socially aware.
Hardware-aware One-Shot Neural Architecture Search in Coordinate Ascent Framework

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35x inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49x speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.
Managing imbalanced training data by sequential segmentation in machine learning

Imbalanced training data is a common problem in machine learning applications. Thisproblem refers to datasets in which the foreground pixels are significantly fewer thanthe background pixels. By tr ...