Reviewerly/exHarmony huggingface dataset:
On the use of icobrain's prepopulated radiology reporting template for multiple sclerosis follow-up

Poster: ECR 2020 / C-11342 / On the use of icobrain's prepopulated radiology reporting template for multiple sclerosis follow-up  by: Sima1, G. Wilms1, T. Vande Vyvere2, W. Van Hecke3, D. Smeets 1; 1Leuven/BE, 2Leuven, Be/BE, 3icometrix, Belgium/BE
An Error Analysis Of Narrative Text Written By Eight Grade Students Of SMP Muhammadiyah 1 Of Kartasura

This study aims to describe the analyis error made by eighth grade students of Muhammadiyah 1 Kartasura Middle School in writing Narrative text. The purpose of this study is to find out what errors are made by students, what errors are often made by students in writing Narrative text and what causes students to make these errors. This research is a qualitative descriptive study. The research subjects were class VIII students totaling 30 people. The method of data collection in this study is error analysis. The research data were taken from the work of students in the form of text Narrative entitled Mouse deer and Crocodile. The results of the study showed that there were some errors that occured such as omission, misformation and misordering. The type of error that often appears and was made by class VIII students is misformation of irregular verb. The cause of the error is that students still do not understand well about the material and still could not distinguish irregular verb writing changes from v1, v2 and v3 therefore the students make many errors.
The Multi-Level Dimensional Coordinate Spaces (MLDC-Spaces)

The visualization, understanding, and interpretation of dimension(s) directly depend on the uses of multidimensional graphs as the most effective visual technique to understand any natural or non-natural phenomena as a whole. This research will introduce a new concept of multidimensional graphs under “The Multi-Level Dimensional Coordinate Spaces (MLDC-Spaces).” The main objective behind creating MLDC-Spaces is to observe many and different dimensions in the same graphical space and time simultaneously. Thereby, the mission of MLDC-Spaces is to offer scientists and researchers an alternative graphical modeling approach to understand different dimensions from a multidimensional perspective. Hence, this alternative multidimensional graphical visualization technique offers different Sub-General Dimensions, Sub-Dimensions, Sub-Main Pivot Dimensional, Dimension Levels, and General Main Pivot Dimensional.
LibGuides: Help for Students: Academic Success & Tutoring

Start here for basic information on the STLCC Libraries and where to ask for help. Useful information and resources for student success.
Συνεργασία μεταξύ αυτόνομου ιπτάμενου και επίγειου οχήματος με δυνατότητα ανταλλαγής δεδομένων αποστολής

The aim of the present thesis is to develop and study an automated system consisting of areal and ground unmanned vehicles, which cooperate to complete multiple missions. The aerial vehicles act supportively to the ground vehicles sending them information about the environment around the position of the ground vehicle. Path planning algorithms have been studied and applied in order to avoid vehicle to obstacles and vehicle to vehicle collisions. A major interest of the thesis was to design a robust and scalable system. The tests were implemented as simulations using the program GAZEBO and the code was developed utilizing the ROS libraries.
Corrections to “Vision-Based Moving UAV Tracking by Another UAV on Low-Cost Hardware and a New Ground Control Station”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , our aim was to automatically detect and track target moving Unmanned Aerial Vehicle (UAV) <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> by another UAV in real-time applications. For that purpose, we developed a vision-based low-cost hardware system and analyzed the tracking algorithm performances. In addition, we developed a ground control station that is integrated with the low-cost hardware system. We initially created a new public data set including different types of UAV images mostly recorded from experiments and search on Google images. The data set is available on the website linked as <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://cogvi.atauni.edu.tr/ResearchLab/PageDetail/Our-ATAUAVs-Dataset-86</uri> that is accessible to everyone.
Library education: Global and regional problems (experience of Chelyabinsk State Institute of Culture)

The higher library education is examined within the framework of reforming higher education in Russia. Incoherence of the education reforms produced the dilemmas between the demands of universal character and specialization in library education, preservation of traditional content and need for innovativeness; orientation toward library context and beyond-library context; priority of learning process modelling and quality of education; possibility of implementing e-learning and institutes’ desiring to keep academic load. The author describes how these dilemmas are being overcome at Chelyabinsk State Institute of Culture.
Open Set Driver Activity Recognition.

A common obstacle for applying computer vision models inside the vehicle cabin is the dynamic nature of the surrounding environment, as unforeseen situations may occur at any time. Driver monitoring has been widely researched in the context of closed set recognition i.e. under the premise that all categories are known a priori. Such restrictions represent a significant bottleneck in real-life, as the driver observation models are intended to handle the uncertainty of an open world. In this work, we aim to introduce the concept of open sets to the area of driver observation, where methods have been evaluated only on a static set of classes in the past. First, we formulate the problem of open set recognition for driver monitoring, where a model is intended to identify behaviors previously unseen by the classifier and present a novel Open-Drive&Act benchmark. We combine current closed set models with multiple strategies for novelty detection adopted from general action classification [1] in a generic open set driver behavior recognition framework. In addition to conventional approaches, we employ the prominent I3D architecture extended with modules for assessing its uncertainty via Monte-Carlo dropout. Our experiments demonstrate clear benefits of uncertainty-sensitive models, while leveraging the uncertainty of all the output neurons in a voting-like fashion leads to the best recognition results. To create an avenue for future work, we make Open-Drive&Act public at www.github.com/aroitberg/open-set-driver-activity-recognition.
What’s new with blockchain?

Blockchain has long since stepped out of Bitcoin’s shadow, proving itself to be a highly innovative technology capable of so much more than handling ...
LibGuides: COVID-19 Library Services: Research, Books, Printing, Equipment & Supplies: During the Quarter: Laptops, Swivels, Calculators & Faculty Supplies/Digitization

No-Contact Holds Pick-Up: Instructions on how to request books, printing, equipment, and faculty supplies from YVC's Library. Instructions on how to request and pick up equipment during COVID-19 restrictions. Faculty can also request supplies and/or digitization of a VHS/DVD to show remotely for a course.
Rancang Bangun Website Layanan Kepada Masyarakat Tingkat Desa / Kecamatan di Kabupaten Bandung

The implementation of community services consists of a state that accommodates the basic needs of the rights of every citizen of goods, services, and administrative services provided by the public service provider. Community services in villages and sub-districts of West Bandung Regency, still require hardcopy which requires a lot of paper. The development of information technology (hardware, software, and data communication networks) which is increasing, is a necessity in improving the service of certificates, as well as information management. E-Government implementation website is needed to provide services to the public more quickly, accurately, and reliably. The E-Government website system developed in villages and sub-districts provides data on ministry results to be accurate and reliable, because recording data directly from the website services to database and data processing. E-Government website establishes as a place for new interaction between the government and the community and other interested parties, where the use of communication technology with the aim of improving the quality of community services.
MEDIA DEVELOPMENT OF MATERIAL BOOKLETS PRAYING PERSONAL POETRY FOR GRADE IV SD STUDENTS

Based on pre-research in class IV SDN Tambangan 01 found a lack of innovation. So it was been an impact on the low learning outcomes. Submission of material by the teacher still seems dominant. So a researcher wants to develop a media booklet. This study aims to develop a media booklet, studies the feasibility and effectiveness of the media booklet on the material express personal poetry in class IV SDN Tambangan 01 Semarang City. The method wich had been used in this research was a Sugiyono Research and Development (R&amp;D) which consists of (1) potential and problem stages, (2) data collection, (3) product design, (4) design validation, (5) design revision, (6) product trials, (7) product revisions, (8) trials usage. The subjects of this stdy were several students in class IV SDN Tambangan 01 Semarang. Data collecting techniques were using observation, interviews, documentation, questionnaires, and tests. Data analyzing techniques were using product feasibility, initial and final data analysis (t test and N-Gain test). The result of the booklet feasibility test that from media experts obtained the percentage of 93.45% and the assessment of material experts obtained the percentage 91.6%. The booklet media effectiveness used in accordance with the result of the t test showed t arithmetic&gt; t table (11,367&gt; 1,697), then Ho was rejected and the pretest and posttest gain a result which was 0,552 with a criteria that support the effectiveness booklet media . The result of the questionnaire responses of teachers and students showed a very positive answer that make booklet media was more effective in its users.
Experience and prediction: a metric of hardness for a novel litmus test

Abstract In the past decade, the Winograd schema challenge (WSC) has become a central aspect of the research community as a novel litmus test. Consequently, the WSC has spurred research interest because it can be seen as the means to understand human behavior. In this regard, the development of new techniques has made possible the usage of Winograd schemas in various fields, such as the design of novel forms of CAPTCHAs. Work from the literature that established a baseline for human adult performance on the WSC has shown that not all schemas are the same, meaning that they could potentially be categorized according to their perceived hardness for humans. In this regard, this hardness metric could be used in future challenges or in the WSC CAPTCHA service to differentiate between Winograd schemas. Recent work of ours has shown that this could be achieved via the design of an automated system that is able to output the hardness indexes of Winograd schemas, albeit with limitations regarding the number of schemas it could be applied on. This paper adds to previous research by presenting a new system that is based on machine learning, able to output the hardness of any Winograd schema faster and more accurately than any other previously used method. Our developed system, which works within two different approaches, namely the random forest and deep learning (LSTM-based), is ready to be used as an extension of any other system that aims to differentiate between Winograd schemas, according to their perceived hardness for humans. At the same time, along with our developed system we extend previous work by presenting the results of a large-scale experiment that shows how human performance varies across Winograd schemas.
Ab Initio Molecular Dynamics on Quantum Computers

Ab initio molecular dynamics (AIMD) is a valuable technique for studying molecules and materials at finite temperatures where the nuclei evolve on potential energy surfaces obtained from accurate electronic structure calculations. In this work, we present an approach to running AIMD simulations on noisy intermediate-scale quantum (NISQ)-era quantum computers. The electronic energies are calculated on a quantum computer using the variational quantum eigensolver (VQE) method. Algorithms for computation of analytical gradients entirely on a quantum computer require quantum fault-tolerant hardware, which is beyond NISQ-era. Therefore, we compute the energy gradients numerically using finite differences, the Hellmann-Feynman theorem, and a correlated sampling technique. This method only requires additional classical calculations of electron integrals for each degree of freedom without any additional computations on a quantum computer beyond the initial VQE run. As a proof of concept, AIMD simulations are demonstrated for the H2 molecule on IBM quantum devices. In addition, we demonstrate the validity of the method for larger molecules using full configuration interaction wave functions. As quantum hardware and noise mitigation techniques continue to improve, the method can be utilized for studying larger molecular systems.
Contrastive Video Textures

Existing methods for video generation struggle to generate more than a short sequence of frames. We introduce a non-parametric approach for infinite video generation based on learning to resample frames from an input video. Our work is inspired by Video Textures, a classic method relying on pixel similarity to stitch sequences of frames, which performs well for videos with a high degree of regularity but fails in less constrained settings. Our method learns a distance metric to compare frames in a manner that scales to more challenging dynamics and allows for conditioning on heterogeneous data, such as audio. We learn representations for video frames and probabilities of transitioning by fitting a video-specific bi-gram model trained using contrastive learning. To synthesize the texture, we represent the video as a graph where the nodes are frames and edges are transitions with probabilities predicted by our video-specific model. By randomly traversing edges with high transition probabilities, we generate diverse temporally smooth videos with novel sequences and transitions. The model naturally extends with no additional training to handle the task of Audio Conditioned Video Synthesis, when conditioned on an audio signal. Our model outperforms baselines on human perceptual scores, can handle a diverse range of input videos, and can combine semantic and audio-visual cues in order to synthesize videos that synchronize well with an audio signal.
Trademark Search, Artificial Intelligence and the Role of the Private Sector

Almost every industry today is confronting the potential role that artificial intelligence and machine learning can play in its future. While there are many, many studies on the role of AI in marketing to the consumer, there is less discussion of the role of AI in creation and selecting a trademark that is both distinctive, recognizable and meaningful to the average consumer. As we argue, given that the role of AI is rapidly increasing in trademark search and similarity areas, lawyers and scholars should be apprised of some of the dramatic implications that its role can produce.We begin, mainly, by proposing, as a general matter, that AI should be of interest to anyone studying trademarks and the role that they play in economic decision-making. By running a series of empirical experiments regarding search, we show how comparative work can help us to assess the efficacy of various trademark search engines, many of which draw on a variety of machine learning methods. Traditional approaches to trademarks, spearheaded by economic approaches, have focused almost exclusively on consumer-based, demand side considerations regarding search. Yet as we show in this paper, these approaches are incomplete because they fail to take into account the substantial costs that are also faced by not just consumers, but trademark applicants as well. In the end, as we show, machine learning techniques will have a transformative effect on the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. In an age where artificial intelligence will increasingly govern the process of trademark selection, we argue that the classic division between consumers and trademark owners is perhaps deserving of an updated, supply-side framework. As we argue, a new framework is needed, one that reflects that putative trademark owners, too, are ALSO consumers in the trademark selection ecosystem, and that this insight has transformative potential for encouraging both innovation and efficiency.
SiamCAN:Simple yet Effective Method to enhance Siamese Short-Term Tracking

Most traditional Siamese trackers are used to regard the location of the max response map as the center of target. However, it is difficult for these traditional methods to calculate response value accurately when face the similar object, deformation, background clutters and other challenges. So how to get the reliable response map is the key to improve tracking performance. Accordingly, a simple yet effective short-term tracking framework (called SiamCAN),by which bridging the information flow between search branch and template branch, is proposed to solve the above problem in this paper. Moreover, in order to get more accurate target estimation, an anchor-free mechanism and specialized training strategy are applied to narrow the gap between the predicted bounding box and groundtruth. The proposed method achieves state-of-the-art performance on four visual tracking benchmarks including UAV123, OTB100, VOT2018 and VOT2019, outperforming the strong baseline, SiamBAN, by 0.327 → 0.331 on VOT2019 and 0.631 → 0.638 success score, 0.833 → 0.850 precision score on UAV123.
DESIGN OF ADVANCE ROBOT FOR SURVEILLANCE AND RISK-FREE MOVEMENT: A SIMULATION APPROACH

IOT is the serious organization framework of availability, transportation and innovation. IOT shrewd gadgets can execute the offices of far off wellbeing observing and crisis notice framework. IOT has considerable utilization of shrewd medical services framework. In the medical services framework the featured arrangements and methodologies that help to the specialists and researchers and specialists who create savvy gadget which is the up degree to the current innovation.This paper will help specialists to think about the condition of patient wellbeing and screen anyplace on the planet.. Medical services framework has the observation that proposed the need of savvy gadgets and shrewd items to diminish the shortcoming of accessible medical care framework. The IOT based medical services has improved innovation which is selective from the conventional medical care and entire clinical framework.How to cite this article:Singh S, Shukla S. Smart Health MonitoringSystem for Patients using Internet of things. JAdv Res Instru Control Engg 2020; 7(3&4): 14-19.
Identifying Content Blocks on Web Pages using Recursive Neural Networks and DOM-tree Features

The internet is a source of abundant information spread across different web pages. The identification and extraction of information from the internet has long been an active area of research for m ...
Comprendere il linguaggio

How do we understand each other? That is, what does language understanding consist of? If we limit ourselves to the last hundred years, two families of theories have placed the notion of understanding at the center of their interests. For a group of theories, discovering what happens in people’s heads is not illuminating to clarify the nature of understanding. Understanding is an essentially social capacity, which we attribute based on publicly observable behaviors. This anti-psychological stance has been assumed by those philosophers who, faithful to Ludwig Wittgenstein's teaching, stressed the uses of language in ordinary communicative contexts (for example, Michael Dummett and Robert Brandom). According to this view, understanding can be studied utilizing the traditional philosophical methodologies (e.g., conceptual analysis), without “getting up from the armchair.” For another group of theories, understanding is essentially a psychological process. Hence, studying understanding means studying this process empirically, discovering which mechanisms in the head allow us to grasp what other speakers communicate linguistically to us. This psychological view has its roots in the AI works on natural language starting from the seventies and eighties of the last century, as well as in the research in cognitive science on the border between psychology and linguistics. In recent years, psychological theories have relied heavily on neuropsychological and neuroimaging data, an impressively growing trend. The current debate is dominated by advocates of simulation theories, according to which understanding requires the reactivation of sensorimotor brain areas, and advocates of amodal theories of language understanding, according to which understanding is made possible by the activation of abstract, symbolic representations.The purpose of this book is to offer a concise, highly readable, and reasoned interdisciplinary introduction to the phenomenon of linguistic understanding, and to the philosophical and scientific problems that it raises. The text is divided into two sections. The first part is devoted to the discussion of the concept of understanding and the main problem associated with it (that is, the problem of psychologism). The second part is devoted to a reasoned presentation of the recent research on the psycho-neural basis of understanding.
Contour loss for instance segmentation via <i>k‐step</i> distance transformation image

Instance segmentation aims to locate targets in the image and segment each target at the pixel level, which is one of the most important tasks in computer vision. Mask R-CNN is a classic method of instance segmentation, but we find that its predicted masks are unclear and inaccurate near contours. To cope with this problem, we draw on the idea of contour matching based on distance transformation image and propose a novel loss function called contour loss. Contour loss is designed to specifically optimise the contour parts of the predicted masks, thus can assure more accurate instance segmentation. To make the proposed contour loss be jointly trained under modern neural network frameworks, we design a differentiable k-step distance transformation image calculation module, which can approximately compute truncated distance transformation images of the predicted mask and the corresponding ground-truth mask online. The proposed contour loss can be integrated into existing instance segmentation methods such as Mask R-CNN, and combined with their original loss functions without modification of the structures of inference network, thus has strong versatility. Experimental results on COCO show that contour loss is effective, which can further improve instance segmentation performances.