Reviewerly/exHarmony huggingface dataset:
Patient-specific deep deformation models (PsDDM) to register planning and interventional ultrasound volumes in image fusion-guided interventions

Image fusion-guided interventions often require planning MR/CT and interventional U/S images to be registered in realtime. Organ motion, patient breathing and inconsistent ultrasound probe positioning during intervention, all contribute to the challenges of real-time 3D deformable registration, where alignment accuracy and computation time are often mutual trade-offs. In this work, we propose a novel framework to align planning and interventional 3D U/S by training patientspecific deep-deformation models (PsDDM) at the planning stage. During intervention, planning 3D U/S volumes are efficiently warped onto the interventional 3D U/S volumes using the trained deep-deformation model, thus enabling the transfer of other modality (planning MR/CT) information in real-time on interventional images. The alignment of planning MR/CT to planning U/S is not time-critical as these can be aligned before the intervention with desired accuracy using any known multimodal deformable registration method. The feasibility of training PsDDM is shown on liver U/S data acquired with a custom-built MR-compatible, hands-free 3D ultrasound probe that allows simultaneous acquisition of planning MR and U/S. Liver U/S volumes exhibit large motion in time due to respiration and therefore serve as a good anatomy to quantify the accuracy of the PsDDM. For quantitative evaluation of the PsDDM, a large vessel bifurcation was manually annotated on 9 U/S volumes that were not used for training the PsDDM but from the same subject. Mean target registration error (TRE) between the centroids was 0.84mm &plusmn; 0.39mm, mean Hausdorff distance (HD) was 1.80mm &plusmn; 0.29mm and mean surface distance (MSD) was 0.44mm &plusmn; 0.06mm for all volumes. In another experiment, the PsDDM was trained using liver volumes from one scanning session, while the model was tested on data from a separate scanning session of the same patient, for which qualitative alignment results were presented.
It's Time for Smartphone Sanity

When iPhones first hit the market in 2007, they were cool but clunky. Since then, they've gotten smaller, then sleeker, then bigger again. Then they added a camera, then a better one, now three of them. These things are always changing. They're always getting better.
Akakite Mai Itaau Tua

'Akakite Mai Itaau Tua - Tell Me A Story' is a show written and performed by Benny Marama, with live music composed and conducted by Jeremy Mayall and Taonga Puoro by Horomona Horo. It tells the story of Benny’s parents as they fall in love, emigrate to Aotearoa, separate and find each other again. Weaving together real life stories with Māori and Polynesian mythology, an aural experience unlike anything heard...World Premiere at 2019 Hamilton Gardens Arts FestivalComposition for narrator, taonga puoro and chamber orchestra.Approx 55mins
Quantum state discrimination for optimal classical communications (Conference Presentation)

Exabytes of data are sent through the internet monthly, and the demand grows exponentially. Quantum measurement enables sensitivity beyond the shot noise, with a potential to minimize the energy and bandwidth required to transmit a bit. We developed a fully quantum-mechanical treatment of this measurement problem. We introduce new protocols of data exchange that are designed to take full advantage of quantum measurement, unlike legacy communication methods that seek to optimize classical measurements. We discuss the theoretical bounds of the new protocols and demonstrate proof of principle experiments.
Research on the Influence of Kmeans Cluster Preprocessing on Adversarial Images

Deep learning is the core of the current artificial intelligence. Neural networks, represented by the depth of learning technology, has been widely applied to the field of computer vision, such as automatic driving, and face recognition. But recent research has shown that if the original picture is added visually imperceptible perturbations, it can fool the neural network to misclassify it. These adversarial images can be generated very easily, and it poses a great threat to computer vision security. Therefore, more and more researches involve the defenses against adversarial images. With the use of the characteristics of the neural network's own fitting and generalization, we perform Kmeans clustering on the images that need to be identified, and then evaluate the impact of different clustering values on the classification of adversarial images. The experimental results show that for small amplitude perturbations images, the use of smaller clustering values can largely reverse the decline of neural network accuracy. However, as the magnitude of the perturbations increases, the defensive effect of simple clustering becomes weaker.
Data Acquisition On Messenger Skype Using The National Institute Of Justice Method

The development of instant messenger, especially Skype applications is growing rapidly. Skype users are usually used as tools to share information using an Android smartphone. The large number of Skype users can be misused for carrying out criminal activities such as illegal goods transactions in the form of vape drugs among Skype users. This case can be resolved by getting digital evidence from the Skype application left on an Android smartphone using the Forensic Digial method. The forensic method used in this study is the National Institute of Justice (NIJ) with several stages, namely identification, collection, examination, analysis, and reporting while the Oxygen Forensic Suite software is used for data acquisition. The results obtained successfully obtained all the desired evidence from the two smartphones that installed Skype, namely EVERCROSS and SAMSUNG J2 in the form of a profile account, contact person, conversation text and image file with the percentage of Skype data acquisition results from the EVERCROSS B75 smartphone succeeding in getting 48% data, and Skype data acquisition results from SAMSUNG J2 managed to get 100% data.
Regularized Adaptation for Stable and Efficient Continuous-Level Learning

In Convolutional Neural Network (CNN) based image processing, most of the studies propose networks that are optimized for a single-level (or a single-objective); thus, they underperform on other levels and must be retrained for delivery of optimal performance. Using multiple models to cover multiple levels involves very high computational costs. To solve these problems, recent approaches train the networks on two different levels and propose their own interpolation methods to enable the arbitrary intermediate levels. However, many of them fail to adapt hard tasks or interpolate smoothly, or the others still require large memory and computational cost. In this paper, we propose a novel continuous-level learning framework using a Filter Transition Network (FTN) which is a non-linear module that easily adapt to new levels, and is regularized to prevent undesirable side-effects. Additionally, for stable learning of FTN, we newly propose a method to initialize non-linear CNNs with identity mappings. Furthermore, FTN is extremely lightweight module since it is a data-independent module, which means it is not affected by the spatial resolution of the inputs. Extensive results for various image processing tasks indicate that the performance of FTN is stable in terms of adaptation and interpolation, and comparable to that of the other heavy frameworks.
A Secured Blockchain Regime for the Insurance Industry- A Legal Analysis

The Blockchain is an emerging technology that has been adopted by many countries and in many companies too. It is a record-keeping technology that stores data of the transactions as well as the participants. In insurance sector it will be very helpful because there would be no middlemen in any insurance transaction. It would be only the assured and the policyholder, and this can be very important in eliminating the frauds. Such digital smart insurances can provide transparency on existing or past insurance policies and claims associated with property and/or insured.The smart contracts are binding in nature on both the parties because these contracts are immutable and irreversible in nature.
THE EFFECTS OF APPROACH INSTRUCTION ON STUDENT’S READING PERFORMANCE

The research study investigated Indonesian EFL learners’ approach of two reading approaches (cognitive and metacognitive), their perceived contact on effectiveness, and the association between reading approach and effectiveness on their English reading comprehension. Fifty-Three English-major freshmen from STKIP Muhammadiyah of Enrekang participated in these lessons. Two principal questions namely: (1) what is the most frequent use of reading approach reported by individual students? (2) Is there any significant association between reading approach and effectiveness on their English reading comprehension? To examine the effects of approach instruction on students’ reading performance, a qualitative interview technique and quantitative research methods including a paired-sample t-test and Person Product Moment Correlation were used to estimate the relationship between reading approach use and effectiveness on students’ reading accomplishment. Significance showed that the most frequent use of reading approach was found to be metacognitive approach, followed by the cognitive approach. In addition, there was a significant positive connection between reading approach and effectiveness on their English reading comprehension. Reading approach, on the other hand, was unrelated to reading achievement. Results of interview findings were analyzed to explore in-depth in sequence about the condition of approach used. The implications of these findings for implementing effective reading strategy instruction are discussed.
Penerbitan Mading melalui Dodorobe Inkrepatif di SMA Muhammadiyah Pangkalpinang

ABSTRACTThis study aims to describe the process of publishing a wall magazine through dodorobe inkrepatif in Muhammadiyah High School, Pangkalpinang. This research is a qualitative research using ethnographic approach. Data collection methods used in this study were interviews, observation, and documentation. The publication of wall magazines are: dodorobe inkrepatif is one of the strategies in increasing the low interest of student literacy. In the modern era the School Literacy Movement (GLS) needs to be designed in a simple and attractive way, one of which is through the publication of wall magazines. The selection of themes in the publication of a wall magazine through Dodorobe. Dodorobe is a traditional game, how to play it is like a social gathering. The results showed that there are two regulations that became the legal umbrella given by the government to students in the school literacy movement. The publication of the wall magazine includes three components, namely motivation, learning to read, write and read and write independently. Wall magazine publishing as an activity in developing creative ideas and creating literacy.Keywords : Publishing, Wall magazine, Dodorobe, Initiative, Creative, Participatory
Research on No-Failure Data Evaluation Method Based on Imported Failure Information

For the case of no-failure data under the weibull distribution, when the prior distribution is Beta distribution, the ranges of super-parameters a and b are determined by the characteristics of probability density function. After introducing failure information, the method of the multi-level bayes estimation is modified. The robustness and feasibility of the method are verified by comparing the error of failure probability and the reliability calculated.
Data Acquisition and Experiment Control with Python

This chapter utilises waveform generation and analysis of sound waves as an example problem. It provides student a first look at how to automate all aspects of an experiment, including experimental planning, experimental setup, data acquisition, and data analysis. Computer software is commonly used for automating experiments. The level of automation can range from minimal to fully autonomous. For highly specialized experiments, like the Large Hadron Collider, all the software and hardware needed to acquire data is custom developed as part of the experiment. Computer programming languages, such as C, Java, and Python, have long been used to automate repetitive calculations and simple software tasks. Over time, computer-controlled hardware emerged. The visual nature of LabVIEW makes it more intuitive than other programming languages. It is also well supported by hardware manufacturers. LabVIEW has excelled because it has succeeded in making this process intuitive to users.
Binary thinking, complex thinking

In January 2019 (Futuribles 428), we began a series on the human brain, with the first dossier devoted mainly to what the cognitive sciences and neurosciences teach us about our capacities for lifelong learning. A second dossier appeared in July 2019 (issue 431) on the plasticity of the brain, its anatomy and its capacity to modify its own functioning as it receives new experiences. This autumn, we open up a third strand on human-machine interactions and, more particularly, the impact of screens (TV, computers, mobile phones) on brain development and function, particularly among young people.Jean-Pierre Bellier, who took part in producing this third strand, introduces the subject by reminding us how far human-machine interactions have now developed and evolved, and by stressing a number of questions inherent in these interactions which will be treated in greater detail in the other articles in this new dossier.
Designing Teaching Learning Activities For Writing Report Text Based On Students Problem At Eleventh Grade MAN Batubara

This research aimed to find out improving of the students’ achievement in writing report text learning by using picture. The subject of this research were the eleventh grade of MAN BatuBara,which consisted of 35 students. This research applied classroom action research. The data of this research were qualitative and quantitative data. The qualitative data were taken from observation sheet, interview sheet, and diary notes. The quantitative data were taken from tests, that were carried out in the end of every cycle. The tests were given to the students in form of pre-test, post test 1 in the first cycle and the post test 2 in the second cycle. The result of the data analysis showed that the score of students increased from the first post-test I to post-test II. It showed that the mean of orientation test was 63, there were 8 students (22,8%) who got point 75 or more. In the post test of cycle I, the mean was 71,4 (19 students) it was higher than orientation test. There was 54,3% (19 students) who got point 75 or more. The percentage of the students’ score in the orientation test to post-test I was improvement 54,3%. In the post test II, the mean was 82,2 and the percentage of students who got point 75 or more was 85,7% (30 students). It indicated that the used of picture can improve the students’ ability in writing learning.
Image Registration and Rectification Using Background Subtraction Method for Information Security to Justify Cloning Mechanism Using High-End Computing Techniques

The present research paper deals with the registration and rectification of images for study of multimodal biometric systems through image cloning mechanisms using high-end techniques. Image cloning is a key research problem in information security applications. Most of the researcher addresses the above-said problem using template-based method which failed to deals with the soft computing and high-end computing techniques. Here, we propose knowledge-based or model-based technique for cloning. In this method, one can achieve more reliable information without any password or keys. Here the algorithm that has been proposed is going to be used for image cloning and at the same time securing reliable information through a double-authenticated based generation of secure codes after employing image cloning mechanism using high-end computing techniques. The proposed algorithm is tested and the results found are satisfactory.
The Effect of Serious Leisure through Senior Health Exercise Competition on Self-efficacy and Meaning in Life

The purpose of this study is to investigate the influence of serious leisure through senior health exercise competition on self-efficacy and meaning in life. Furthermore, it is aimed at activating the senior health exercise competition. Total 180 questionnaires were distributed to the elderly and 158 samples were used for practical analysis. The collected data were individually input to a computer and processed according to the purpose of analysis using SPSS 25.0. The data were analyzed using frequency analysis, correlation analysis, simple regression and multiple regression analysis. The results of this study were as follows: First, serious leisure through senior health exercise competition affected the self-efficacy. Second, serious leisure through senior health exercise competition influenced meaning in life. Third, senior’s self-efficacy was found to affect meaning in life of elderly.
FCPL Curated Content: Curbside and Virtual Services: Top 10 Ways to FCPL at Home

Great ways to use FCPL products and services at home with library digital materials and services
Analisis Kecelakaan Berlalu Lintas Di Kota Jakarta Dengan Menggunakan Metode K-Means

In the city area of Jakarta, accidents in traffic enter problems that almost often occur at any time. Not a few victims suffered property damage, but victims also suffered minor injuries, serious injuries and even lost their lives. To analyze the number of accident incidents in traffic around the Jakarta city area which took casualties, researchers used a clustering method with the K-Means algorithm which was processed through rapidminer software. From the clustering of traffic accidents data obtained the level of accuracy in clusters 1 and 2 with the same accuracy that is equal to 20%, while cluster 3 gets an accuracy value of 10%
ONLINE LIBRARY MANAGEMENT SYSTEM

As the library and information services community seeks to cope with new patterns of information provision, new technology and changing financial circumstances, it is critical to gain new thinking across the profession. The Latest research, innovative theory and best organizational practice are all presented in Library Management System.Library Management System website which is used to supply the books to the user. This is done through JAVA technologies. This has various technical areas. It includes WINDOWS XP as the operating System, Oracle as Database. The front end deals with GUI and source code deals with Oracle (Backend).Library Management System provides knowledge and practical perspectives all aspects of management of libraries and information services, which will prove invaluable to managing a library or information services cost effectively, while meeting the needs of its users.Library Management System is one of the most tedious processes, which involves the regular updating of many files and records. As new members enroll for membership the management has to issue a unique membership code for that member. For this they have to keep a track of many records such as student details, book details and maintain a number of registers. Doing this manually involves a lot of time and labor. Hence this can be made easy through automation.
Designing a cloud tier for the IoT platform EMULSION

This paper presents some design aspects of the cloud tier of a generic multi-service cloud-based IoToperational platform EMULSION, which is developed as a non-expensive IoT platform primarily to serve theneeds of small and medium business enterprises (SMEs). The EMULSION is a representative of the newhorizontal type, next-generation, IoT platforms that come as a replacement of the existing vertical type platforms.The architectural design and main characteristics of the platform are presented and its multi-tiered structure isexplained with particular attention paid to the cloud tier. This proposed cloud tier, with a Data ManagementPlatform (DMP) based on a three-layer Lambda architecture, achieves improved high throughput and low latency.This is done through the inclusion of two distributed ‘publish-subscribe’ Kafka-based modules which aredesigned for data processing, and for data subscribing and message storage, respectively. Initial trials have begunwith two pilot platform-demonstration IoT systems, utilizing this EMULSION platform. These are shortly to bepresented in separate research papers.
Distributed Controller for Load Balancing with VM Migration

In this paper, we propose a model named Hugo, that acts as a distributed, scalable cloud controller which can handle basic operations like creation, deletion and migration of virtual machines(VMs). It achieves higher utilization by overcommitting the physical machine(PM). We describe each and every component along with their functionalities that garner to form the structural support for the model proposed. Algorithmic implementations namely Wiener filter algorithm for resource prediction, heuristic for energy efficient VM migration are discussed. Distributed nature of the model along with its contribution to scalability is fairly explained in this paper.
A Parallel Strategy Applied to APSO

Particle Swarm Optimization (PSO) is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. Parallel strategy is an excellent method which separate the population into some subgroups, the subgroups can communicate with each other to improve algorithms’ performance significantly. In this paper, we apply a parallel method on Adaptive Particle Swarm Optimization (APSO), to further improve convergence speed and global search ability of Parallel PSO. The novel Parallel APSO algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization and the experimental results showed the proposed Parallel APSO algorithm was competitive with the Parallel PSO.
Ice Hockey Research Data Platform from Official Records Data and Verification

In this study, a database was established by analyzing the data research record produced in ice hockey sports. The deployed data verification with Ice hockey reference service   was demonstrated with ice hockey officials and players. This research utilized the data stored in the KNSU Datanest data repository and developed PDF parsers for batch processing of records. Among the types of records, the game summary, team roster, team statistics, and player statistics files were collected, and tables were extracted from the records. PDF records were converted to text in CSV format which are   converted to DataFrame, and loaded into the database. Out of the total 22 types of records, 4 types were constructed with 66,830 data parsed as element values. Data verification has found no problems with the quality of the data deployed, showing a high satisfaction with providing 66 factors against the 30 factors provided by the service previously used.
Security Metrics and Risk Analysis for Enterprise Systems

Protection of enterprise systems from cyber attacks is a challenge. Vulnerabilities are regularly discovered in software systems that are exploited to launch cyber attacks. Security Analysts need objective metrics to manage the security risk of an enterprise systems. In this talk, we will give an overview of our research on security metrics and challenges for security risk analysis of enterprise systems. A standard model for security metrics will enable us to answer questions such as "are we more secure than yesterday" or "how does the security of one system compare with another?" We will present a methodology for security risk analysis that is based on the model of Attack Graphs and the Common Vulnerability Scoring System (CVSS). We will also present a framework for detection and forensic analysis of Advanced Persistent Threats.