Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Video Editing Toolbox for Automatic Summary of Medical Videos

Recording of medical procedures makes it possible for medical staff to reviewtheir work, learn from the mistakes and produce material for education. Medical videos tend to be long, which has an impact on usability and raises issues concerning memory storage.The aim of this master thesis is to make the material more user-friendly withan intelligent toolbox based on machine learning and image analysis techniques. The tools divide the video into chapters with a k-means++ clustering technique, detect when an X-ray source is active with ROI based processing, track camera movements by comparing frames with the optical flow algorithm by Gunnar Farneback and identify when medical instruments are present using an artificial neural network. Based on the information from these tools a combined timelineand an automatic summary is created.The results indicate that the chapter tool is especially promising when thevideos include pre and post procedure sections, since these are easier to separate. The ROI based tool detects all the frames with an active X-ray. Theneural network performs well on classifying frames containing an instrument,but requires annotated data. The majority of camera movements are found,but the algorithm sometimes fails to detect zoom in the video.This thesis is intended as a proof of concept of the potential in automatic processing of medical videos. The tools can create reference points to important sequences. More data and evaluation of the tools are necessary for the further development of an automatic summary system.
A load aware matrix approach Load balancing in cloud computing

Cloud computing is originate or grouping of Distributed computing, Grid computing and Parallel computing. Various computers and storages devices are connected to make heterogeneous resources of pool. Virtualization technology enhances the utilization of clouds resources by providing the different resources on the internet which users can buy pay on demand rule. Different service propose by the clouds computing are software as service (SaaS), Infrastructure as service (PaaS) and Platform as services (PaaS). For proper utilization of the resources on virtual machines and physical machines load balancer is need for distribution of resources. In this paper we discuss the virtualization technology and load balancing algorithm. A load aware matrix has been proposed and discussed.
Human Operator Authentication Using Limited Voice Data: A Power Grid Dispatcher Instance

Automatic speaker verification (ASV) has the potential to replace the error-prone and expensive human-based security check to protect the ever increasingly interconnected complex systems, such as power grid system. However, state-of-the-art ASV system relies heavily on a large amount of matched development data and adequate long duration test utterance to maintain the acceptable performance. Unfortunately, such large amounts of data are not always feasible to collect in real world application. In this paper, we propose a new method for i-vector extraction by incorporating historical test information to reduce to requirement of long test utterance duration. The historical tests are weighted by a world MAP estimator and then used in the computation of current test’s Baum-Welch statistics. Meanwhile, we modify linear discriminant analysis (LDA) to reduce the requirement of matched development data. In modified LDA training, the variability between development and evaluation data is separated and the objective is to simultaneously minimize the within-class variability and domain variability when maximize the between-class variability. Experiments are conducted on data collected from power grid dispatchers. By adding historical test information, we observe consistent improvement over baseline system especially for shorter duration condition. With modified LDA, at least 63% of performance gap is recovered when system parameters are trained with mismatched development data. Finally, we integrate proposed methods in one system and apply it to power grid dispatching room scenario. Experimental results show our proposed methods achieve fair performance with limited voice data and successfully reduce the amount of data required by ASV system.
AKTIVITAS FISIK PADA PASIEN DIABETES MELITUS TIPE 2 DENGAN NEUROPATI PERIFER : TINJAUAN LITERATUR

Type 2 diabetes mellitus is a chronic disease that could cause damage to foot peripheral nerves, namely peripheral neuropathy. Diabetic peripheral neuropathy is a damage and inability to regenerate nerve cells due to exposure of chronic hyperglycemia which can lead to various symptopms and complications. Therapy is aimed at improving symptoms and delaying development of peripheral neuropathy, one of which is physical activity which is one of the cornerstones of management of diabetes mellitus. An explanation of the benefits of physical activity in diabetes mellitus has been widely discussed, but nore in peripheral neuropathy. This literature review aims to explain the types and benefits of physical activity in peripheral neuropathy. Seven literature reviewed were obtained through electronic database search EBSCOHost, Proquest, and Clinical Key with key words physical activity, exercise intervention, diabetic peripheral neuropathy, and type 2 diabetes mellitus. Peripheral neuropathy patients can carry out various types of physical activity, either weight bearing or non-weight bearing exercise. Patients with ulcers, wounds and severe foot deformity can do non-weight bearing exercise such as static cycling and upper body ergometer. Another can do walking, aerobics, balance exercises, stretching, tai chi, resistance training, and foot ankle exercises. Aerobics is a physical activity that can prevent and delay development of peripheral neuropathy. Balance training can improve sensory and motor neuropathy. In general, physical activity can improve innervation in the cutaneous tissue, regenerate nerve cells, improve the symptoms of neuropathy in the form of pain, balance disorders, risk of falls, and obstacles to physical mobility.
Polarization simulation and analysis of residual birefringence in optical materials for a hyper-NA lithography illumination system

A polarization simulation and analysis method was carried out for a hyper numerical apertures (NA) lithography illumination system which is affected by residual birefringence in optical materials. The lens is divided into multiple small annuli according to the finite element method, and the retardation distribution is obtained by setting the residual birefringence of each annulus. Finally, the polarized ray tracing is cleverly changed to geometric ray tracing. A hyper-NA lithography illumination system is modeled, and the residual birefringence is set between 0.1 nm/cm and 1 nm/cm. The simulation result shows that the degree of polarization performance degradation is proportional to the magnitude of residual birefringence, and the tolerance of residual birefringence in lens materials is below 1 nm/cm for the system. The polarization simulation and analysis method provides a powerful tool to calculate the polarized parameters of the system, which is helpful for selecting lens material of the hyper-NA illumination system.
Research on using library academic database for university-level staff recruitment

In recent years, domestic libraries have gradually shifted their roles from traditional literature and material center into information service institution in the background of systematic construction of Double First, namely the World-class University and First Class Discipline. They now provide information consultation and application service for key projects such as Discipline Construction in Universities and Colleges, Construction of Science and Research and construction of Teaching Staff by data collection, data storage and data mining, etc. It is a good practice to construct Community of Scholars of library in Universities and Colleges. However, judging from the present situation, there is less satisfactory outcomes of this application of Community of Scholars of library in Universities and Colleges, and it still needs improvement in services qualities. Therefore, this paper will study the application strategy and methods of Community of Scholars of library in Universities and Colleges from the perspective of Team Construction of Teachers in the background of Double-class first.
Optimizing the Tilt Angle of Solar Panels to Reduce Carbon Footprint: Case for The West Mediterranean Region of Turkey

Carbon footprint is a term used to express the lasting effect of human consumption activities on nature. A carbon footprint is often defined as CO 2 or equivalent greenhouse gas emitted as a result of an activity or process associated with a product, service or region. The carbon footprint account is one of the most interesting researches in recent years. In this study, the optimal choice of the tilt angle for the solar panels in order to collect the maximum solar irradiation is investigated in order to reduce the carbon footprint in West Mediterranean Region of Turkey.
ML Aggarwal Class 10 Solutions for ICSE Maths Chapter 22 Probability Chapter Test

ML Aggarwal Class 10 Solutions for ICSE Maths Chapter 22 Probability Chapter Test ML Aggarwal Class 10 Solutions for ICSE Maths Chapter 22 Probability ...
A practical guide to experimentation (and benchmarking)

tutorial Share on A practical guide to experimentation (and benchmarking) Author: Nikolaus Hansen Ecole polytechnique Ecole polytechniqueView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 594–613https://doi.org/10.1145/3319619.3323397Published:13 July 2019Publication History 0citation81DownloadsMetricsTotal Citations0Total Downloads81Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on Somatosensory Interaction Based on Convolutional Neural Network

Abstract Somatosensory interaction is an important part of human-computer interaction. The core of somatosensory interaction must accurately obtain the 3D spatial information of the human body. This article uses an RGBD camera to acquire both color and depth images. We Perform 2D human pose estimation in color images using hourglass network with residual structure. At the same time, we use the sparse feature points to align the color map with the depth map in order to accurately map the detection results in color images to the corresponding depth images. The experimental results show that this method can accurately and quickly obtain the 3-dimensional posture information of the human body, which is an important guarantee for somatosensory interaction.
An Effective Fusion Enhancing Approach for Single Underwater Degraded Image

The visibility in underwater images is poor because of the absorption and scattering of the light in water that causes color attenuation, low contrast and poor brightness. In this paper, a novel approach using the principle of Laplacian pyramid fusion based on single underwater image is presented. The proposed method aims to correct underwater image color, enhance contrast and improve brightness by using the improved white balance algorithm, the proposed contrast stretching and brightness enhancement. The experimental results prove that our method can enhance the quality of underwater image obviously.
A Two-Stage Deep Neural Network for Sequence Labeling

State-of-the-art sequence labeling systems require large amounts of task-specific knowledge in the form of handcrafted features and data pre-processing, and those systems are established on news corpus. English as second language (ESL) corpus is collected from articles written by English-learner. The corpus is full of grammatical mistakes, and then it is much more difficult to do sequence labeling. We propose a two-stage deep neural network architecture for sequence labeling, which enable the higher-layer to make use of the coarse-grained labeling information of the lower-level. We evaluate our model on three datasets for three sequence labeling tasks—Penn Treebank WSJ corpus for part-of-speech (POS) tagging, CoNLL 2003 corpus for named entity recognition (NER) and CoNLL 2013 corpus for grammatical error correction (GEC). We obtain state-of-the-art performance on three datasets—97.60% accuracy for POS tagging, 91.38% F1 for NER and 38% F1 for determiner error correction of GEC and 28.89% F1 for prepositional error correction of GEC. We also evaluate our system on ESL corpus PiGai for POS tagging and obtain 96.73% accuracy. The implementation of our network is publicly available.
Multi-function Quantum Cryptography Protocol Based on Bell State

Most of the current quantum cryptographic protocols can only perform a single function, and quantum resources are not fully utilized. In this paper, we propose a multi-function quantum cryptographic protocol based on the Bell state. This protocol can perform quantum private query (QPQ), quantum identity authentication (QIA), and quantum key distribution (QKD) functions. In the QPQ function part, this protocol can effectively improve database security and user privacy. In the QIA function part, this protocol can complete a two-way identity authentication function, which can effectively improve the reliability of identity authentication. In the QKD function part, this protocol can complete the key distribution function efficiently and reliably, and can maximize the utilization efficiency of quantum resources. With a rigorous security analysis, we proves that this protocol can defend against JM attacks, entanglement measurement attacks and external attacks.
Improved Modular Convolution Neural Network for Human Pose Estimation

Human pose estimation in image is an important branch of computer vision and graphics research. In this paper, an improved modular convolution neural network is proposed to solve the problem of human pose estimation in static 2D images. A cascaded three-stage full convolutional network (FCN) can learn the non-linear mapping from image feature space to human pose space in an end-to-end way. In order to improve the accuracy of predicting joints, the method of multi-feature source fusion is adopted to improve the estimation process of the human body posture. The first two stages of the network focus on learning local image features and joints neighborhood pixel features, and these features are merged in the third stage of the network. Finally, the coordinates of human joints are obtained by regression of the merged features. In our experiments, using the strict PCP criteria on the full body pose dataset LSP, the average prediction accuracy of our method is 79.3%. In addition, using the PCKh standard on the upper body pose dataset FLIC, our method achieves an average prediction accuracy of 93% without additional training.
ENABLING IDENTITY-BASED INTEGRITY AUDITING AND DATA SHARING WITH SENSITIVE INFORMATION HIDING FOR SECURE CLOUD STORAGE

With cloud storage services, users can remotely store their data to the cloud and realize the data sharing with others. Remote data integrity auditing is proposed to guarantee the integrity of the data stored in the cloud. Encrypting the whole shared ﬁle can realize the sensitive information hiding, but will make this shared ﬁle unable to be used by others. The proposed scheme is based on identity-based cryptography.
Hybrid Extended Isolation Forest : Anomaly Detection for Bird Alarm

The Isolation Forest algorithm is a random forest based anomaly detection algorithm utilizing isolation to determine anomality of data. The Hybrid Isolation Forest and Extended Isolation Forest alg ...
Calibration Procedure for the Beacon-based Tracking System to be used for Mobile Robot Convoying

The paper is focused on the mobile robot convoying along the path travelled by some leader carrying the active ultrasonic beacon. The robot is equipped with the three-dimensional receiver array in order to receive the ultrasonic wave. A system calibration procedure has been developed.
PKRS: A Product Knowledge Retrieve System

In this demo paper, we present the Product Knowledge Retrieve System (PKRS), which can retrieve the large-scale product knowledge efficiently. The PKRS has three features. Firstly, PKRS can retrieve not only the objective knowledge (e.g. categories) but also the subjective knowledge (e.g. users’ opinion). Secondly, a learned mapping dictionary (LMD) is devised to accelerate the query parsing. Thirdly, PKRS adopts optimized join strategy to improve the retrieval effectiveness. For demonstration, we compare the performance of our PKRS with a state-of-the-art knowledge management system. The experimental results show that the PKRS can process the queries on product knowledge more effectively.
Selina Concise Mathematics Class 6 ICSE Solutions Chapter 2 Estimation

Selina Concise Mathematics Class 6 ICSE Solutions Chapter 2 Estimation Selina Publishers Concise Mathematics Class 6 ICSE Solutions Chapter 2 ...
Entanglement Between a Photonic Time-Bin Qubit and a Collective Atomic Spin Excitation

Light-matter entanglement combines the long-distance transmission advantage of photonic qubits with the storage and processing capabilities of atomic qubits. In this work we used a laser-cooled atomic cloud to generate entanglement between photonic time-bin qubits and atomic spin excitations. © 2019 The Author(s)
Ray W. Howard Library: Stable Links: Database

This guide will cover proxy prefixes and the use of building URLs that will work for both on campus and off campus users within the Shoreline Community College information systems. The information here reflects the changes being made to our certificates Academic Search Complete, CINAHL Nursing Journals, ProQuest, and More need to be routed through our EZProxy service. Some of our databases need to have the proxy added and some already have the proxy added in the permalink. Many of our database links requ
ChromaGAN: An Adversarial Approach for Picture Colorization.

The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with semantic information. A generative network is used to infer the chromaticity of a given grayscale image conditioned to semantic clues. This network is framed in an adversarial model that learns to colorize by incorporating perceptual and semantic understanding of color and class distributions. The model is trained via a fully self-supervised strategy. Qualitative and quantitative results show the capacity of the proposed method to colorize images in a realistic way achieving state-of-the-art results.
Visual Search and Recognition for Robot Task Execution and Monitoring.

Visual search of relevant targets in the environment is a crucial robot skill. We propose a preliminary framework for the execution monitor of a robot task, taking care of the robot attitude to visually searching the environment for targets involved in the task. Visual search is also relevant to recover from a failure. The framework exploits deep reinforcement learning to acquire a common sense scene structure and it takes advantage of a deep convolutional network to detect objects and relevant relations holding between them. The framework builds on these methods to introduce a vision-based execution monitoring, which uses classical planning as a backbone for task execution. Experiments show that with the proposed vision-based execution monitor the robot can complete simple tasks and can recover from failures in autonomy.
Intercultural education in primary school- employees' and students' perspective

The goal of the institution of school is among others developing the ability to understand diversity, which means respecting human rights and also duties, education for tolerance, cooperation and learning about our own culture as well as learning about cultures of other nations (Krek in Metljak, 2011). Intercultural education is an important part of education in culturally heterogenous societies because it teaches us intercultural competencies. Researching the relationship of the participants of the intercultural education towards it is an important indicator for the further actions that we should take in this area. Students as well as employees in schools are the recipients of the intercultural education as well as its creators. Their personal experience of the process makes them the experts in this area. In my thesis I was researching the relationship of the students and the employed towards intercultural education and their approaches towards it in practice. As a sample I took employees on one side and the students (imigrant children as well as non-immigrant) on the other. The data was collected and processed with a qualitative approach. With the employees I performed half-structured individual interviews. I collected the data from the students (6-7 students of one class of the 4th grade, one class of the 6th grade and one class of the 7th grade plus the fourth focus group which consisted of only immigrant children from the classes mentioned beforehand) in a form of half structured focus groups. The sociograms were also made with all the students in the mentioned classes. The data was processed with qualitative analysis. Through the process of coding the text I created the categories of the higher ranks, which helped me with the interpretation of the collected data. I wanted to collect the first-hand data with which I can offer a piece in the mosaic for a better understanding of the field of intercultural education.
DEVELOPMENT OF DIGICULTURE EDUCATIONAL PROGRAM FOR DIGITAL SKILLS TRAINING FOR ADULTS IN CREATIVE INDUSTRIES

DEVELOPMENT OF DIGICULTURE EDUCATIONAL PROGRAM FOR DIGITAL SKILLS TRAINING FOR ADULTS IN CREATIVE INDUSTRIES
Business Modeling and Reasoning Based on Process Ontology

In this paper, a method of business modeling and reasoning based on process ontology is proposed to solve the problem that existing in the banking business field such as large amount of data and the complexity to analysis the relationship between data. We use process ontology technology to describe each basic element in the business process, and on this basis, design constraints and inference rules, and carry out corresponding ontology knowledge reasoning by use the inference rules. Practice results show that the establishment of process ontology model can effectively represent the knowledge involved in the business process and discover the hidden information contained in the knowledge.
Multi-language Dictionary Integration by Using Semantic Web

Some languages do not have a direct translation to another language. This condition might occur to local language in the certain country. This research tackles this issue by providing an indirect translation. Indirect translation means the translation is done by linking the closest language which has a direct translation with the target language. Furthermore, this research provides integration among several dictionaries data which lie in third-party servers and all translation at once. This research yields multi-language dictionary application which utilizes semantic web technology to provide data integration. As the conclusion, The Internet speed and the availability of the third-party servers influence the performance of the multi-language dictionary application.