Reviewerly/exHarmony huggingface dataset:
Object Tracking in Videos Using CNN

Object tracking in Videos is challenging because of appearance changes caused by occlusion, illumination, variation change, etc. Visual Tracking is one of the essential components in computer vision nowadays. It can be used for surveillance, medical imaging, Navigation, computer-human interaction, etc. In visual tracking given an unknown object specified by bounding box in the first frame, track this in the subsequent frames. In this paper, we implemented object tracking in a video using Convolutional Neural Network (CNN) architecture. To train this network, we have used ALOV 300+ dataset for which includes frames of videos of different classes (Light, Surface Cover, Specularity, Transparency, Shape, Motion Smoothness, Motion Coherence, Clutter, etc.) with their corresponding target object bounding boxes. Further, we used two Inception V3 architectures that contain the CNN framework. Inception V3 has shown to outperform other architectures for object classification tasks. Most of the generic object trackers are trained from scratch online and do not benefit from a large number of videos that are available for offline training. Our method uses extensive offline training of neural networks for tracking different objects at high speed in real time. The algorithm learns a general relationship between an object’s appearance and its motion in an offline manner, allowing the network to track new objects. The model shows 78% accuracy on the train set of 10,000 images and an accuracy of 76% on the test set. Also increasing the dropout factor from 0.5 to 0.7 variance reduced by 2% whereas regularization helped reduce the variance by 0.1%.
Domain adapted breast tissue segmentation in magnetic resonance imaging

For women of high risk ($&gt;25&#37;$ lifetime risk) for developing Breast Cancer combination screening of mammography and magnetic resonance imaging (MRI) is recommended. Risk stratification is based on current modeling tools for risk assessment. However, adding additional radiological features may improve AUC. To validate tissue features in MRI requires large scale epidemiological studies across health centres. Therefore it is essential to have a robust, fully automated segmentation method. This presents a challenge of imaging domain adaptation in deep learning. Here, we present a breast segmentation pipeline that uses multiple UNet segmentation models trained on different image types. We use Monte-Carlo Dropout to measure each model's uncertainty allowing the most appropriate model to be selected when the image domain is unknown. We show our pipeline achieves a dice similarity average of 0.78 for fibroglandular tissue segmentation and has good adherence to radiologist assessment.
Cross-filter compression for CNN inference acceleration

Convolution neural network demonstrates great capability for multiple tasks, such as image classification and many others. However, much resource is required to train a network. Hence much effort has been made to accelerate neural network by reducing precision of weights, activation, and gradient. However, these filter-wise quantification methods exist a natural upper limit, caused by the size of the kernel. Meanwhile, with the popularity of small kernel, the natural limit further decrease. To address this issue, we propose a new cross-filter compression method that can provide $\sim32\times$ memory savings and $122\times$ speed up in convolution operations. In our method, all convolution filters are quantized to given bits and spatially adjacent filters share the same scaling factor. Our compression method, based on Binary-Weight and XNOR-Net separately, is evaluated on CIFAR-10 and ImageNet dataset with widely used network structures, such as ResNet and VGG, and witness tolerable accuracy loss compared to state-of-the-art quantification methods.
LibGuides: IN151 - CWRR II - Braniger: Books

Research resources and techniques for Professor Braniger's IN151 class. Techniques for finding books at Staley Library
Mixed Reality Displays in Warehouse Management : A study revealing new possibilities for Warehouse Management and Tangar

This work has investigated how head-mounted-displays can enable more efficient and better work conditions for warehouse workers. Head-mounted-displays have increased in popularity among companies b ...
Situation Representation Model Implemented by Granule Fuzzy Characteristics in Mobile Autonomous System

Creation of the mobile autonomous control system has specifics. The decision-making and assessment of the large scale situation are based on the global situation model presented in the form of a multilevel structure at different abstraction levels. Three types of abstraction are considered: quantitative, deterministic, and generalized abstraction. The report examines the algorithm for processing data from sensors at the first two levels. Algorithm is programmed in Python and implemented on ESP 8266 microcontroller.
Download KingRoot for PC Windows 10/7/8 Laptop (Official)

Are you having a hard time rooting your phone? Is the KingRoot APK not working for your Android phone? Well, if all else fails, even the 360 Super Root ...
Review of the year in German patent litigation 2019

... The question of whether the requirement of an inventive step (section 1(1) of the German Patent Code (GPC)) was met in a case where the plaintiff argued that the technical method had already been disclosed in one specific part of another patent was subject of a recent 2019 decision of the German Federal Supreme Court (Bundesgerichtshof).1 In this case, the defendant was the owner of a patent covering a method for bit-rate reduction in the coding of a signal which is used in image or video data processing. This case had already been subject to an earlier decision of the German Federal Supreme Court,2 which ultimately referred the case back to the Federal Patent Court for further examination of patentability. Eventually, the Federal Patent Court declared the patent invalid due to the lack of an inventive step. The Federal Patent Court basically argued that the last paragraph in a US patent had already provided an alternative to the described method, which was then disclosed in the defendant’s patent.
The availability of eye tracking technology with UI

In the case of experiments through survey methods, accurate measurements were difficult because they were influenced by the feelings of participants in the experimentation. Thus, in this paper, using eye-tracking technology, the shortcomings of existing survey methods were supplemented and quantitative assessments were made possible. A new method was presented to analyze the player's eye tracking based on the specific points on the mobile screen. The player's eye contact information is accurate because it appears immediately. A more accurate and reliable measurement is possible by comparing and analyzing the eye-tracking data and survey methods proposed in this paper.
The Implementation of Scaffolding Game Media To Improve Student Learning Outcomes In SDN 27 Pontianak

The study aimed to describe the implementation of scaffolding game media to improve learning outcomes of thematic learning in VI-grade at SDN 27 Pontianak (Pontianak 27 State Elementary School). The research method used by researchers was descriptive. The form of research was Classroom Action Research. Data collection techniques in this study were observed and questionnaire responses of students. Research collection tools in the form of observation sheets. This research was conducted for two cycles. The results of this study included the ability of teachers to plan learning using scaffolding game media, namely cycle I (85.81%), increased 9.94% in cycle II (95.75%) with an average value of 87.41% (category very good). The ability of teachers to implement learning in the first cycle (76.19%) increased by 16.27%, and the second cycle to 92.46% with an average value of both cycles of 92.46% (very good category), an increase of 16, 27%. The first cycle learning activities with an average of 76.45% increased by 14.84% in the second cycle with an average value of 89.07%, the average score of the second cycle learning activities 81.65% (once category) and the questionnaire response positive students as much as 85.93% (very good category). The first cycle learning outcomes test with an average value was 76.45, and the second cycle was 79.48%, the average value of the learning outcomes test was 77.97% (good category), and an increase of 30.3%. Based on data analysis of student learning outcomes, shows that the use of Scaffolding Game media can improve student learning outcomes.
Management of educational and scientific laboratory: new requirements and competences

The role of educational and scientific laboratories in the University environment has increased in recent eight years due to the transformation of education in the Russian Federation. New requirements to education, to the competencies that students acquire, the shift of the educational vector towards technological disciplines, the participation of Russian universities in the 5–100 project-all this makes it necessary to develop a new class of educational and scientific laboratories in Russian universities. At the same time, the requirements for the teaching staff of the Universities are changing new requirements for educational technologies are formulated. The implementation of the concept of University 2035, and in particular the emergence of the new National Technology Initiative (NTI) markets, such as: “Aeronet”, “Technet”, “Energynet” etc. imply the creation of a flexible model of education based on the individual learning trajectory. At the same time, the requirements for educational and scientific laboratories are changing ˗ today they must conduct research in various areas, respectively, laboratories, which imposes new requirements to the management of the laboratories and also to the format and laboratory equipment. It has been shown in the article that in the conditions of the educational environment transformation, the head of modern teaching and research laboratory must have a fundamentally new list of skills ˗ from the ability to plan and organize the scientific process, to managing the laboratory as a structural unit, which implies the presence of managerial skills.
EinsteinPy: A Community Python Package for General Relativity

This paper presents EinsteinPy (version 0.3), a community-developed Python package for gravitational and relativistic astrophysics. Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years. Specifically, a lot of recent studies show that the use of Python in Astrophysics and general physics has increased exponentially. We aim to provide a very high level of abstraction, an easy to use interface and pleasing user experience. EinsteinPy is developed keeping in mind the state of a theoretical gravitational physicist with little or no background in computer programming and trying to work in the field of numerical relativity or trying to use simulations in their research. Currently, EinsteinPy supports simulation of time-like and null geodesics and calculates trajectories in different background geometries some of which are Schwarzschild, Kerr, and KerrNewmann along with coordinate inter-conversion pipeline. It has a partially developed pipeline for plotting and visualization with dependencies on libraries like Plotly, matplotlib, etc. One of the unique features of EinsteinPy is a sufficiently developed symbolic tensor manipulation utilities which are a great tool in itself for teaching yourself tensor algebra which for many beginner students can be overwhelmingly tricky. EinsteinPy also provides few utility functions for hypersurface embedding of Schwarzschild spacetime which further will be extended to model gravitational lensing simulation.
The drivers of cyber risk

Cyber incidents are becoming more sophisticated and their costs difficult to quantify. Using a unique database of more than 100,000 cyber events across sectors, we document the characteristics of cyber incidents. Cyber costs are higher for larger firms and for incidents that impact several organisations simultaneously. The financial sector is exposed to a larger number of cyber attacks but suffers lower costs, on average, thanks to proportionately greater investment in information technology (IT) security. The use of cloud services is associated with lower costs, especially when cyber incidents are relatively small. As cloud providers become systemically important, cloud dependence is likely to increase tail risks. Crypto-related activities, which are largely unregulated, are particularly vulnerable to cyber attacks.
Research on the Value and Construction Strategy of College Campus Sports Culture

College students are an important reserve force for national prosperity and national rejuvenation.  Campus sports culture is a combination of campus culture and sports culture, which can significantly promote the overall development of college students’ physical and mental health and cultivate their good moral character.  Therefore, colleges should strengthen the construction of campus sports culture.
林谷 林眞怤 後裔들의 학문 활동

Brothers Galcheon and Cheommodang were named as scholars in the 16th century at Anui County. Limgok, the grandson of Cheommodang has accomplished a study to converge Toegye 's and Nammyong' s studies. He read many books, taught many disciples, and built many poems and sentences. So he became a very famous scholar. Later, the king invited him as a teacher to teach his son and gave him official office. Many of his descendants studied until the end of the Joseon Dynasty. However, their works were not published and were not known to others. So many people mistakenly believe that the discipline of this family is cut off, but in fact it is not. The descendants will soon have to publish the ancestral literature of their ancestors. These works are important not only for individual families, but also for the nation. Their academic tendency was a practice-oriented discipline that did not go beyond the scope of linguistics.
Italian as second language in a migratory context. A didactic proposal

This paper illustrates a language teaching experience in a migratory context at the CPIA Ct1. After a short introduction on immigration and the new Italian legislation in the area of education to migrants, the article analyzes the strengths and the critical points of the teaching process and describes some case studies. The article concludes with the proposal of a didactic unit, which takes into account the profile of the learners and their communication needs.
Investigating the Effects of Utilizing Cognitive Listening Strategies on EFL College Students’ Listening Comprehension and Self-Efficacy

This research was carried out under some objectives: first, to find out whether or not the EFL college students taught explicitly using cognitive listening strategies significantly had better listening comprehension than those taught embeddedly using conventional listening strategy; and second, to examine whether or not the EFL college students taught explicitly using cognitive listening strategies significantly acquired better self-efficacy than those taught embeddedly using conventional listening strategy. This present research employed quantitative method which included a quasi-experimental design. This research was conducted in the English Department of Universitas Islam Negeri Maulana Malik Ibrahim Malang. A total of 98 students participated in the research, including 48 in the treatment group and 50 in the control group. The quantitative data are a Listening Comprehension Test and a Listening Self-efficacy Questionnaire. Pre-test and post-test were designed with the similar types of IELTS test adopted from Cambridge Practice Tests for IELTS. SPSS was used to examine whether the listening test and questionnaire instruments were valid and reliable. Quantitative findings indicated that EFL college students taught explicitly using cognitive listening strategies significantly had better listening comprehension and self-efficacy than those taught embeddedly using conventional listening strategy. The findings also revealed a significant difference in the means of gain scores in IELTS listening test and listening self-efficacy between the control and treatment groups resulting in statistical difference at the .05 level of significance. Based on the result of Independent Sample T-Test,  (1) by comparison with the posttest listening skill between control and experiment group gains tobserved value 10.532 with the significance value 0.000. For tobserved value > ttable value or the significance value is smaller than the significant level 5%, there is a difference of posttest listening skill between control and experiment group; (2) by comparison with posttest listening self-efficacy between control and experiment group, tobserved value is 3.800 with the significance value 0.000. Since tobserved value > ttable value or the significance value is smaller than the significant level 5%, there is a difference of posttest listening self-efficacy between control and experiment group.
Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers

The threat of spoofing can pose a risk to the reliability of automatic speaker verification. Results from the bi-annual ASVspoof evaluations show that effective countermeasures demand front-ends designed specifically for the detection of spoofing artefacts. Given the diversity in spoofing attacks, ensemble methods are particularly effective. The work in this paper shows that a bank of very simple classifiers, each with a front-end tuned to the detection of different spoofing attacks and combined at the score level through non-linear fusion, can deliver superior performance than more sophisticated ensemble solutions that rely upon complex neural network architectures. Our comparatively simple approach outperforms all but 2 of the 48 systems submitted to the logical access condition of the most recent ASVspoof 2019 challenge.
Expletivt objekt på reträtt : En studie av platshållare för objektssatser i äldre svenska

This article presents a diachronic study of clause-anticipating object constructions in the history of Swedish. Corpus findings show that the object position is represented by an expletive in const ...
Text Extraction from Images Using OCR

Nowadays, there is an enormous demand in storing information available on papers, such as books or newspapers.There is an existing way to store information by scanning the desired text, but it will be stored as an image that won't help for further processing. For instance, if stored scanned text images, can't read the text word by word, or line by line; the text in these scanned images can't be reused unless we rewrite that whole content by ourselves. Detection of text from documents in which text is embedded in complex colored document images is a very challenging problem. There are a lot of potential users who want to extract the text from images, archiving documents etc. For this reason, user need an Optical Character Recognition (OCR). It aims at detecting textual regions from the document and separating it from the graphics portion. Getting information directly from applications forms and it saves a lot of time.I.
문화 접근성 향상과 지역 정체성 확립을 통한 뮤지엄 공공성 강화: 파리 시립 뮤지엄 그룹 (Paris Musées) 사례를 중심으로

The aim of this paper is to identify the nature of the museum as a space where the concept of publicness is activated, and as an institution that embodies the public values to confirm the social role of museum education that catalyzes public practice. For this purpose, I look into the cases of the Paris Musees, which has been successfully implementing a joint management strategy for public museums. The Paris Musees is a public entity created by the City of Paris on June 20, 2012, operates and manages fifteen museums and monuments in the city. The Paris Musees aims to expand the public cultural infrastructure of the citizens of Paris by organising exhibitions reflecting new technological developments, by preserving and expanding collections, and by cooperating with various cultural and artistic institutions both at home and abroad. Their ultimate goal is to improve the opportunities for citizens to access cultures.
Can You Tell by My Grades That I Am a Blogger? A Longitudinal Study of the Use of Blogging as a Pedagogical Tool and Effects on Expected Grades

This is the submitted version (preprint) of an article published by SciTePress in the Proceedings of the 12th International Conference on Computer Supported Education, available at https://doi.org/10.5220/0009562804140421
LibGuides: Health, Physical Activity and Sport: Working Remotely with SUC Library - FAQs

Library support for Health, Physical Activity and Sport students in Stranmillis University College Guidance on resources and using the Library remotely
Out-of-Core GPU Gradient Boosting

GPU-based algorithms have greatly accelerated many machine learning methods; however, GPU memory is typically smaller than main memory, limiting the size of training data. In this paper, we describe an out-of-core GPU gradient boosting algorithm implemented in the XGBoost library. We show that much larger datasets can fit on a given GPU, without degrading model accuracy or training time. To the best of our knowledge, this is the first out-of-core GPU implementation of gradient boosting. Similar approaches can be applied to other machine learning algorithms
Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos

Activity detection in security videos is a difficult problem due to multiple factors such as large field of view, presence of multiple activities, varying scales and viewpoints, and its untrimmed nature. The existing research in activity detection is mainly focused on datasets, such as UCF-101, JHMDB, THUMOS, and AVA, which partially address these issues. The requirement of processing the security videos in real-time makes this even more challenging. In this work we propose Gabriella, a real-time online system to perform activity detection on untrimmed security videos. The proposed method consists of three stages: tubelet extraction, activity classification, and online tubelet merging. For tubelet extraction, we propose a localization network which takes a video clip as input and spatio-temporally detects potential foreground regions at multiple scales to generate action tubelets. We propose a novel Patch-Dice loss to handle large variations in actor size. Our online processing of videos at a clip level drastically reduces the computation time in detecting activities. The detected tubelets are assigned activity class scores by the classification network and merged together using our proposed Tubelet-Merge Action-Split (TMAS) algorithm to form the final action detections. The TMAS algorithm efficiently connects the tubelets in an online fashion to generate action detections which are robust against varying length activities. We perform our experiments on the VIRAT and MEVA (Multiview Extended Video with Activities) datasets and demonstrate the effectiveness of the proposed approach in terms of speed (~100 fps) and performance with state-of-the-art results. The code and models will be made publicly available.