Reviewerly/exHarmony huggingface dataset:
CONVINCED -– Enabling privacy-preserving survival analyses using Multi-Party Computation

Integraal Kankercentrum Nederland (IKNL) maintains the Netherlands Cancer Registry (NCR). The NCR is a comprehensive, population-based registry with data on diagnosis and treatment. However, in an increasing amount of research projects, there is a need for additional data items that can be found in other databases. In particular the setting where the data is vertically partitioned is relevant when considering data that is distributed in the Netherlands: in this case, each party maintains a portion of the attributes, e.g. clinical, demographic and psycho-social variables, of the same group of patients. This is needed to generate a more complete picture of the patient, since not all data necessarily needs to be part of a cancer registry. However, these underlying attributes are privacy-sensitive and cannot be readily shared amongst involved parties. In a joint research project, TNO and IKNL investigated the possibilities to analyze vertically partitioned data without compromising the patient’s privacy, and developed new solutions to do this using Multi-Party Computation (MPC). In particular this project focused on survival analysis: an often-used technique in oncology data research that can be used to indicate how likely someone is to be alive a few years after diagnosis. Additionally, it can give insight in which characteristics might relate to the chances of survival. This report describes the results and conclusions of this project in more detail. In particular, it has been shown that MPC can indeed be employed to perform survival analysis on vertically-partitioned data, without having to reveal any sensitive data. For some survival analysis techniques (Kaplan-Meier and log rank) this can be done while keeping the performance of the secure solution high enough for practical application on realistic data sets. For more complex survival analysis techniques (Cox Proportional Hazards) additional research is needed to improve the performance before it can be applied in practice. Follow-up steps to bring the developed solutions towards operational applications have been identified.
Towards a Unified Theory of Mind and Brain

A unified theory of how brains give rise to minds has been getting steadily developed for 60 years. This theory has explained data from thousands of interdisciplinary experiments, and scores of its predictions have been confirmed. The theory includes new computational paradigms called Complementary Computing and Laminar Computing that clarify the global organization of brain dynamics and behavior. It uses a small number of basic equations and modules to form modal architectures that model different modalities of intelligence. One of its models, Adaptive Resonance Theory, or ART, is currently the most advanced cognitive and neural theory of how advanced brains incrementally and stably learn to attend, recognize, and predict objects and events in a changing world. All the basic ART predictions have been confirmed by psychological and neural data. These results provide a firm foundation for further development of a Standard Theory of the Mind.
Exploration and Practice of Cultivation Mode of Manufacturing Industry Talent in Higher Vocational Colleges

Manufacturing industry is a main stay of national economy. To be aligned with national development strategy of manufacturing industry, it is imperative for higher vocational colleges specialised in manufacturing industry to embark on reform of talent cultivation mode. Higher vocational colleges integrate government resources, social resources and school resources to establish alarge-speciality-teaching platform in manufacturing industry. In terms of the reform, enrollment should be based on large categories, so “1.5+1+0.5” staged talent cultivation mode is adopted. Through reform of talent cultivation mode in manufacturing industry, remarkable achievements have been made such as students' flexible choice of courses, flexible setting of new speciality, number of courses being reduced significantly and mechatronics technology speciality being recognized as a national backbone speciality. Therefore, the success experience of reform of talent cultivation modein manufacturing industry sets a good example for other specialities and other higher vocational colleges
Cryptanalysis Attacks and Techniques

This chapter is dedicated to the most important and useful cryptanalytic and cryptanalysis standards, validation methods, classification, and operations of cryptanalysis attacks. The cryptanalysis discipline is very complex, and writing about it takes thousands of research hours. The following sections contain a survey of the most important elements that are vital for you to use in your daily activities.
Hyperspectral Image Denoising Using Tensor Decomposition under Multiple Constraints

Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.
LibGuides: Graphic Novels Aren't Just for Fun!: Home

This LibGuide will show how Graphic Novels can be a gateway to learning. Whether a graphic novel provides content or just engages a reader, they can be used to inspire students of all backgrounds and reading levels to read.
Utilization of Information and Communication Technology in Teaching and Learning at the Primary School Level: A Developing Country Perspective

The study was conducted to assess the usage of Information Communication and Technology (ICT) in teaching and learning in primary schools within the OLA Circuit of the Cape Coast Metropolis.Again, the study was done to find out the availability of ICT facilities, efficient and accessible this infrastructure and challenges of integration.The study was carried out in five Government (Public) schools within the OLA Circuit in Cape Coast, Ghana.Descriptive survey was conducted using questionnaires to collect data on 50 teachers.Descriptive and inferential statistical tools such as frequency, correlation and linear regression were used to analyse the data.The findings indicated that teachers teaching in the primary schools were willing to use ICT for their teaching but due to some challenges of inadequate facilities such as computer laboratory, electricity, projectors, scanners, printers and inadequate computer technicians hindering seamless integration of ICT in their teaching and learning.Also, the study showed that teachers had positive perceptions on the benefit of ICT on their teaching.Furthermore, most teachers requested for training in ICT, support services for technology, sufficient software to help sustain their interest in teaching and learning of the subject.It was recommended that technology and technical support unit should be instituted in the various schools to handle technology integration challenges and provide rapid responses to teachers' technological needs.
Evaluating and prioritizing the failure factors and cause of delays in IT projects using FMEA: Towards project continuity

Today, the use of information technology and software applications in organizations is inevitable. To maintain competitiveness, companies need to define different projects in IT areas and also need to keep the risk level of the defined projects at an acceptable level. In this paper, it has been tried to develop a framework for assessing the readiness of IT projects by using the relationship between concepts of business continuity management and project management. Therefore, by reviewing the literature on causes of delays and failure factors in IT projects, as well as interviews with some experts and business continuity consultants, essential criteria for delaying and failing IT projects are selected, and the impact of these criteria on each of the five phases of IT project development are checked and prioritized. By presenting the extended formula to calculate the weighted risk priority number according to the Failure mode and effect analysis approach, a framework has been provided for assessing readiness based on the priority level of these criteria.
EFEKTIVITAS MODEL PEMBELAJARAN GROUP INVESTIGATION TERHADAP HASIL BELAJAR MATEMATIKA KELAS 5 SEKOLAH DASAR

This research is motivated by the fact that the mathematics learning process is less than optimal, seen from the incomplete learning outcomes of the students. The purpose of this study was to determine the effectiveness of using the group investigation learning model in improving mathematics student learning outcomes in the material. The approach used in this research is a quantitative approach with the experimental method nonequivalent control group design. Sampling using nonprobability sampling. The data collection technique was obtained from a multiple choice written test. The data analysis technique used descriptive and statistical analysis with the help of the SPSS version 21 application. The results showed that data processing using the independent sample test data posttest experimental class and control, showed the sig value. (2-tailed) of 0.042, which means that it is smaller than 0.05. According to the basis of decision making in the independent sample t test, if the sig. (2-tailed) value <0.05 then Ha is accepted and H0 is rejected, or in other words there is a significant difference between the experimental and control classes in improving student learning outcomes in the material Build a 5th grade Mathematics subject room. Thus, the group investigation learning model was effective in improving mathematics learning outcomes for grade 5 SD at SDIT Ali Bin Abi Thalib, Sendang Village, Kalinyamatan District. Keywords: Learning Outcomes, Learning Models, Group Investigation
Mobile Oral Cancer Image Classification based on Efficient Deep Learning for Low-resource Settings

We will report a mobile-based dual-mode image classification method for oral cancer detection in low-resource settings.
Cyber-Attack Detection in Networked Control Systems via Encrypted Watermarks

Cyber-attacks long have been a topic reserved for sci-fi movies and books. With the advance of internet and the globalisation of technology supply chains, as well as the growing political and economic pressure around the world, cyber warfare has become the new weapon of choice for covert state operations, but also rogue organizations. In the last 2 decades multiple major industries have suffered some kind of outage – power generation, manufacturing, oil a gas, transport, and others. A typical industrial system is designed for a life span of more than 20 years, making future issues hard to protect against at the planning phase. No widespread efforts exist to identify such threats, to detect attacks and counteract them. This thesis proposes a practical approach for lower level protection of control systems based on linear watermarking as a transparent process to provide detection for any malicious activity that might significantly impact the operations of the plant.
The Female Kindergarten Teachers’ Relationships with Children with Insecure Attachment and Their Relationship with the Verbal Abilities of Those Children

The present study aimed to explore the female kindergarten teachers' relationships with children with insecure attachment.It aimed to explore the relationship between such relationships and the verbal abilities of those children in University district, Amman.The sample consists from 110 children (i.e.71 male children and 39 female children).Those children with insecure attachment with caregivers.The researchers adopted a descriptive correlative approach.They used a scale to identify the insecure attachment pattern.The latter scale was used to collect data from the primary caregivers of the children.The correlation coefficient values of the latter scale are within the range of (0.260-0.631).The Cronbach alpha coefficient value of the latter scale is 0.903.The researchers also used a scale for exploring the female kindergarten teachers' relationship with the children.The latter scale was used to collect data from the female kindergarten teachers.The correlation coefficient values of the latter scale are within the range of (0.552-0.169).The Cronbach alpha coefficient value of the latter scale is 0.875.The researchers used a scale for assessing the verbal abilities of each child.The correlation coefficient values of the latter scale are within the range of (0.187-0.624).The Cronbach alpha coefficient value of the latter scale is 0.875).The level of the positive relationships between the female kindergarten teachers and the children with insecure attachment in Amman is moderate (m=2.600).The mean of the (closeness) dimension is high.The mean of the (conflict) demission and the mean of (dependency) dimension are low.51.8% of the sampled children have moderate verbal abilities level.There are statistically significant correlation between the kindergarten teachers' relationships with those children and the children's verbal abilities.It was found that 24.9% of the changes in the female kindergarten teacher-children relationship can be attributed to the children's verbal abilities
NÍVEL DE ATIVIDADE FÍSICA DE PRATICANTES DE EXERCÍCIO FÍSICO DO SUL DE SANTA CATARINA DURANTE A PANDEMIA DO COVID-19

This study aims to identify the level of P.A (physical activity) of a group of people from the Southern region of Santa Catarina during the COVID-19 pandemic, describing the findings about changes in habits and motivation, physical and sports exercises practiced before the pandemic, and what changed during the period of social isolation, as well as the frequency of training and whether they were impaired or not , if they were managing to remain active and which physical and sports modalities were being more practiced during quarantine. A descriptive cross-sectional, quantitative study was conducted using the online questionnaire methodology for data collection. The population was composed of 192 people, in which 46% were 18 to 29 years old, and most were very active, practicing physical and sports exercises. It was found that modalities that used the structure of training centers and gyms were directly affected, and their practitioners should choose to train at home, often in an adapted way or adhere to other modalities of physical exercise, in order to remain active and healthy, amid the insecurity and anguish of social isolation necessary to face the new coronavirus.
Digital Branch: Stem Stories: About Stem Stories

Monthly nature program exploring our fascinating world. Morse Institute Library Children's Department
Enhancing the LR Parsing Strategy Using Incremental GPLR Parsing Method

Parsers in modern integrated development environments (IDEs) for generalpurpose languages are virtually all of ad hoc, recursive descent variety.While such parsers have many disadvantages when compared with machine-generated LALR(1) parsers but they have two major good qualities: they are not restricted to any finite of lookahead, and in IDE, they can re-parse parts of a file as they change rather than reparsing the entire file.Theoretically, both of these two capabilities can be achieved through variations of the traditional LR parsing techniques, but the traditional LR parsing methods still suffer two irresolvable problems; which are shift-reduce and reduce-reduce conflicts.In this research, we are trying to solve these two drawbacks with preserving the capabilities of the traditional LR parsing techniques.This has been achieved by employing the generalized piecewise LR parsing (GPLR) technique instead of the traditional LR techniques.
What’s New in C# 8.0

This chapter will cover the most important features of C# 8.0 for professionals in the fields of cryptography and cryptanalysis. For more details about which version of C# is used with different versions of .NET Framework and .NET Core, we recommend the resource from [3].
Timorese Hakka Online Community Participation in Australia: An Extension of the Theory of Planned Behavior

澳洲當地有為數可觀的東帝汶客家人，這些人的原鄉以大陸梅州為主。1975年印尼入侵東帝汶，當地的華人不堪戰爭所苦，多數二次移民到澳洲。有鑑於澳洲地廣人稀，無遠弗屆的傳播科技，似乎具備連結族群、凝聚認同的功能，因此本研究企圖探討傳播科技在族群不斷的遷徙和移動中扮演的角色。本研究以計畫行為理論、輔以族群認同概念作為分析工具，以立意採樣和滾雪球方式進行問卷調查，對象涵蓋採用和不採用網路社群的東帝汶客家人，蒐集有效樣本374份。研究結果發現，東帝汶客家人參與客家網路社群的態度、主觀規範和知覺行為控制都是影響意願的顯著因素。族群認同雖然無法直接提高意願，但是透過正向態度，親友規範和較高的自我效能，可以發揮促進的作用。
An Adaptive Algorithm for Image Encryption: Pseudo Random Generator with Crossover of Genetic Algorithm

In this paper proposed an adaptive approach for image encryption.We used linear congruential random number generators to produce the pseudo random sequence.The input image is encrypted using pseudo random with crossover of genetic algorithm.This new encryption method can be used to encrypt an image with high secure, fast and lossless.It can be used to send the encrypted image through network and secure data storage (such as important documents).To validate our work, several experiments are executed on a number of different features (details) images.The proposed method reduces the weakness area in the encryption method and increasing the randomness of the encrypted image.
L2 읽기에서의 L1 읽기 이해력의 역할

This study was designed to explore the relationships between L1 (first language) literacy and its lower skills (literal, inferential, and critical comprehension skills) and L2 (second language) reading ability in the Korean EFL context. The contribution of each L1 comprehension skill to explain L2 reading ability was also measured. For this study, 108 Korean middle school students were chosen as participants, and their L1 comprehension skills (literal, inferential, and critical), L2 linguistic knowledge (vocabulary and grammar), and L2 reading ability were assessed and analyzed. Results show that all variables in this study were significantly related to L2 reading ability, but L1 comprehension skills (literal and critical comprehension skills, in particular) were more predictable than L2 linguistic knowledge in explaining L2 reading ability. Therefore, in order to properly understand L2 reading ability, it is necessary to take a closer look at L2 learners’ L1 reading comprehension skills other than various L2 induced factors.
Predicting Total Business Sales using Time Series Analysis

aEconomic activity undergoes 4 phases (expansion, peak, contraction, trough/recession) in which recession is a period of lowest activity and peak indicates the highest activity. Total Business sales is one of the key factors that influence the economic activity of a country. Total sales or gross sales is the grand total of all sales revenues a business generates from normal activities. The frequency of time series sales data can be monthly, quarterly, or annually. Prediction of business sales is highly important as it determines various factors in the market including Gross Domestic Product (GDP). The algorithms or models required for prediction of time series data are different from other machine learning models. Since sales is affected by time, a time series data should be stationary. Only when the data is stationarized, we can apply the algorithms on them. In this paper, monthly sales data is collected and predictions are done using moving average, simple exponential smoothing, Holt’s model, ARIMA, and SARIMAX. Root Mean Square(RMS) is the accuracy metric of time series models and lower RMS indicates higher accuracy. In this paper, a lower value of RMS is obtained for the SARIMAX model.
LibGuides: Economics: Home

Economics is a social science concerned with the production, distribution, and consumption of goods and services. introduction
LibGuides: Library Research Skills: Finding Print Books: Call Numbers

This LibGuide has the same content as the Finding Print Books module your professor may have in Canvas.
Decision Support System for The Selection of North Sumatra Atlet Drumband By Applying Elimination and Choice Translation Reality (ELECTRE) (Case Study: United States of Drumband Indonesia North Sumatra Region)

Unity Drumband Indonesia (PDBI) Region of North Sumatra is an organizational body engaged in sports groups, which involves players in various musical instruments. In selecting players, PDBI North Sumatra through the committee to conduct strict selection, through a judging system with several stages of a rigorous process. From the selection of participants, compare each criterion one by one to get the best athletes. Implementation of decision support systems is expected to provide the right solution and can assist the jury in generating a decision. Thus the process of selecting athletes can produce a decision directly without a long time. So that accelerate the selection process of drum band athletes who will strengthen the team of North Sumatra. The Elimination and Choice Translation Reality (ELECTRE) method is required in decision support systems for the selection of drum band athletes who will strengthen the North Sumatra team.
CoReHAR: A Hybrid Deep Network for Video Action Recognition

Automating the processing of videos in applications such as surveillance, sport commentary and activity detection, human-machine interaction, and health/disability care is crucial to their correct functioning. In such video processing tasks, recognition of various human actions is a pivotal component for the correct understanding of videos and making decisions upon it. Accurately recognizing human actions is a complex process, demanding high computing capabilities and intelligent algorithms. Several factors, such as object occlusion, camera movement, and background clutter, further challenge the task and its accuracy, essentially leaving deep learning approaches the only viable option for properly detecting human actions in videos. In this study, we propose CoReHAR, a novel Human Action Recognition method that employs both deep Convolutional and Recurrent neural networks on raw video frames. Using the pre-trained ResNet152 CNN, deep features are initially extracted from video frames. The sequential information of the frames is then learned using DB-LSTM RNN. Multiple stacked layers in forward and backward passes of the DB-LSTM provide increased network depth for higher accuracy. A number of techniques are also applied to improve CoReHAR’s processing speed on heterogeneous GPU-enabled systems. The proposed method is evaluated using PyTorch, and is compared to the state-of-the-art methods, showing a considerable efficiency increase, with nearly 95% recognition accuracy measured as an average over all splits of the challenging UCF101 dataset.