Reviewerly/exHarmony huggingface dataset:
The Joint Effort Workshop as a tool for Knowledge Management and competence development

Cybersecurity is a complex global phenomenon where the risk for individuals, organisation and the society at large are at risk. These risks need to be in focus and solutions for the prevention and developing countermeasures. In this paper, we describe the Joint Effort Workshop as an approach to raise awareness to these threads and the possibility to generate and exchange knowledge between students and experts. We conclude that mechanisms for systematic response to attacks need the developed technical requirements, but foremost human behaviour, knowledge and resilience to response to risks, which can be experienced through the collaborative environment of the Joint Effort Workshop
Improve L2-normalized Softmax with Exponential Moving Average

In this paper, we propose an effective training method to improve the performance of L2-normalized softmax for convolutional neural networks. Recent studies of deep learning show that by L2-normalizing the input features of softmax, the accuracy of CNN can be increased. Several works proposed novel loss functions based on the L2-normalized softmax. A common property shared by these modified normalized softmax models is that an extra set of parameters is introduced as the class centers. Although the physical meaning of this parameter is clear, few attentions have been paid to how to learn these class centers, which limits futher improvement.In this paper, we address the problem of learning the class centers in the L2-normalized softmax. By treating the CNN training process as a time series, we propose a novel learning algorithm that combines the generally used gradient descent with the exponential moving average. Extensive experiments show that our model not only achieves better performance but also has a higher tolerance to the imbalance data.
Bayesian Predictive Inference and Feature Selection for High-Dimensional Data

This thesis discusses Bayesian statistical inference in supervised learning problems where the data are scarce but the number of features large. The focus is on two important tasks. The first one is the prediction of some target variable of interest. The other task is feature selection, where the goal is to identify a small subset of features which are relevant for the prediction. A good predictive accuracy is often intrinsically valuable and a means to understanding the data. Feature selection can further help to make the model easier to interpret and reduce future costs if there is a price associated with predicting with many features.Most traditional approaches try to solve both problems at once by formulating an estimation procedure that performs automatic or semiautomatic feature selection as a by-product of the predictive model fitting. This thesis argues that in many cases one can benefit from a decision theoretically justified two-stage approach. In this approach, one first constructs a model that predicts well but possibly uses many features. In the second stage, one then finds a minimal subset of features that can characterize the predictions of this model. The basic idea of this so called projective framework has been around for a long time but it has largely been overlooked in the statistics and machine learning community. This approach offers plenty of freedom for building an accurate prediction model as one does not need to care about feature selection at this point, and it turns out solving the feature selection problem often becomes substantially easier given an accurate prediction model that can be used as a reference.The thesis focuses mostly on generalized linear models. To solve the problem of predictive model construction, the thesis introduces novel methods for encoding prior information about sparsity and regularization into the model. These methods can in some cases help to improve the prediction accuracy and robustify the posterior inference, but they also advance the current theoretical understanding of the fundamental characteristics of some commonly used prior distributions. The thesis explores also computationally efficient dimension reduction techniques that can be used as shortcuts for predictive model construction when the number of features is very large. Furthermore, the thesis develops the existing projective feature selection method further so as to make the computation fast and accurate for large number of features. Finally, the thesis takes the initial steps towards extending this framework to nonlinear and nonparametric Gaussian process models. The contributions of this thesis are solely methodological, but the benefits of the proposed methods are illustrated using example datasets from various fields, in particular from computational genetics.; Tama vaitoskirja kasittelee bayesilaista tilastollista paattelya ohjatuissa oppimistehtavissa, joissa havaintoja on niukasti, mutta piirteiden maara on suuri. Tyossa keskitytaan kahteen osaongelmaan. Ensimmainen naista on jonkin…
A Lower Bound of the Number of Rewrite Rules Obtained by Homological Methods

It is well-known that some equational theories such as groups or boolean algebras can be defined by fewer equational axioms than the original axioms. However, it is not easy to determine if a given set of axioms is the smallest or not. Malbos and Mimram investigated a general method to find a lower bound of the cardinality of the set of equational axioms (or rewrite rules) that is equivalent to a given equational theory (or term rewriting systems), using homological algebra. Their method is an analog of Squier's homology theory on string rewriting systems. In this paper, we develop the homology theory for term rewriting systems more and provide a better lower bound under a stronger notion of equivalence than their equivalence. The author also implemented a program to compute the lower bounds.
THE ANALYSIS OF STUDENTS’ ABILITY ON LISTENING COMPREHENSION OF TOEFL TESTAT SIXTH SEMESTER STUDENTS OF ENGLISH EDUCATION DEPARTMENT LANCANG KUNING UNIVERSITY PEKANBARU

Listening comprehension of TOEFL test is seemingly a difficult part for the students of English Education Department Lancang Kuning University Pekanbaru. To figure out the scientific reason for this case, the researcher was intended to conduct a research to clarify the students’ ability on listening comprehension of TOEFL Test and the factors influence it at sixth semester students of English Education Department Lancang Kuning University Pekanbaru. It was a mix model research. It was involved two research design, qualitative and quantitative. A test of TOEFL Listening comprehension part was a quantitative data and interview was qualitative data. After collecting the data, the researcher found that students’ students’ ability on listening comprehension of TOEFL Test and the factors influence it at sixth semester students of English Education Department Lancang Kuning University Pekanbaru was categorized into fair. The researcher hopes the students’ and lecturer can work together to figure out the best strategy for Listening Comprehension of TOEFL test At Sixth Semester Students of English Education Department Lancang Kuning University Pekanbaru. So, students  Listening Comprehension of TOEFL test At Sixth Semester Students of English Education Department Lancang Kuning University Pekanbaru.
KLASIFIKASI TINGKAT KERENTANAN MALARIA PADA SUATU WILAYAH MENGGUNAKAN NAÃVE BAYES DATA MINING

The number of people affected by malaria often increases along with climate change which encourages the growth of vectors (vector borne disease) as an object that transmits the disease. The quality of the body's low condition will be vulnerable resulting in the spread of diseases transmitted by insects and animals. Global commitment regarding malaria elimination which began in 2007 was based on the high infant, under-five and pregnant mortality rates. One thing that needs to be done is the need for a classification of the vulnerability level of each region against malaria from the quantitative data that has been obtained so that it will provide more emphasis on malaria, especially in areas with higher levels of vulnerability. Calculation of the classification of the vulnerability level of the region against malaria using Naive Bayes was able to produce an accuracy value of 93.75%. Keywords: Classification, Vulnerability, Malaria, NaA¯ve Bayes
Abstract 4214: A microfluidic platform to capture and detect cancer cells in Leukemia patients

Introduction : BioFluidica has developed a platform, The Liquid Scan™ to analyze circulating cancer cells in whole blood. The Liquid Scan is unique in its performance metrics compared to existing platforms by employing fully automated instrumentation in conjunction with a highly sensitive and a specific cell detection microfluidic chip. This technology can be applied to a broad variety of tumors, including liquid tumors such as leukemias and lymphomas. The unique design of the microfluidic chip permits the capture and analysis of live circulating leukemic cells (CLCs) from whole blood, even when these cells appear undetectable by other methods.The Liquid Scan has been applied to capture CLCs from patients suffering from acute myeloid, as well as acute lymphoblastic leukemia (AML and ALL, respectively). These leukemias are acute diseases of the blood and bone marrow, with mutational changes and clonal expansion of abnormal myeloid and lymphoblastic precursors, respectively claiming a great number of lives every year. Bone marrow biopsies/aspirations have been the standard methods for reliably diagnosis and monitoring these diseases. These procedures are extremely painful for patients, necessitate specialized centers, and trained personnel, which increases the overall cost of treatment.Methods: We have applied the Liquid Scan™ to capture CLCs and characterization, using specific antibodies to capture known clones of leukemic blasts. As a model, we use Kasumi-1, KG-1 and HL-60 cells for AML, and SupB15 cells for ALL, spiked in healthy whole blood.Results: We characterized the ability of the The Liquid Scan™ to capture and release circulating leukemic cells from whole blood, by using cell line models spiked-in whole healthy donor blood and applied to the microfluidic chip. Our results are directly applicable to patient in different stages of the disease, both as an initial diagnostic tool, and as a way to monitor recovery and diagnose relapse.Conclusions: Precision medicine (PM) is significantly changing the treatment of cancer and is bringing new hope for patients. Diagnostic testing for circulating tumor and leukemic cells are promising prognostic and predictive tools for selecting optimal therapies based on the patient’s genetic content. Highly sensitive, and selective BioFluidica’s system is highly sensitive and selective with the capability of rapidly delivering purified and concentrated circulating cancer cells compatible with downstream molecular diagnostics.Citation Format: Rolf Muller, Rachel Toughiri, Alena Bartakova, Paul Diaz, Craig Carson, Paul Armistead, George Fedoriw, Mathew Foster, Maryam Zomorrodi, Jennifer Barber-Singh, Veronica Cheung, Emily Mirkin, Roksolana Melnychuk, Elizabeth Fabio, Sangeetha Purushotham, Judy Muller-Cohn. A microfluidic platform to capture and detect cancer cells in Leukemia patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4214.
SEMANTIC ANALYSIS OF FRAUDULENT ADS IN FACEBOOK

This study aims to analyse the semantic features in fraudulent ads in Facebook. This study is conducted by using two kinds of advertisement which are found in Facebook. The researchers are analyzing the word, sentence, or phrase that suspected as the way to convince the customers by following theory of meaning from Leech (1981). The data that the researchers got showed that the first type of advertisement is emphasized more to the affective and social meaning where the seller leads the customers’ opinion that the seller is a good or trusted person. Moreover, the seller uses prase that include to conceptual meaning to explain the condition or specification of the cellphone in detail.In the second type of advertisement, the element emphasized by the seller is thematic mining by referring to the emphasis of the word used in the advertisement.More over, theresult for identification of fraudulent ads in this study is still dominated by the usual way, such as by looking at the price offered. However, the results of this study can also be used as a consideration in identifying fraudulent ads.
Fourier Spectral Analysis to Reduce the Gamma Effect in Fringe Projection Profilometry

The gamma effect of commercial video projectors generates residual ripples that propagate in the continuous phase map, which generate errors in the three-dimensional reconstruction when fringe projection profilometry (FPP) is used. In this workis is proposed find a strategy based in the Fourier spectral analysis to reduce the ripples due to gamma effect, combbining passive (as Phase Shifting Algorithms-PSA) and active methods.
Random Walk-Based Semantic Annotation for On-demand Printing Products

Nowadays, the scale of real network is increasing day by day, while also brings sparse problems.It is usually necessary to maintain a large number of product information.To organize this product information, a feasible way is to add semantic tags to the information.In this article, we aim to solve the problem of semantic annotation of on-demand printing products.Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm.Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds "product-term" bipartite network.Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm.By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product.A large number of experiments have been done on Amazon dataset.The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products.
UNIVERSALIZATION OF ELEMENTARY EDUCATION UNDER DIFFERENT SCHEMES

Education is basic need of each and every child. It is an index of social, individual, economic development and progress of whole country. Elementary Education is the base of education pyramid. At this stage children learn many skill, habit formation, morality,  adjustment with environment, know the world,  culture, develop scientific temperament and logical thinking that will helped in carrier choice and succeed in future life. It is directed in article 45 of Indian Constitution provision of free and compulsory education up to age 14 and after 86 th amendment Act 2002 made education a fundamental right for all children. In spite of these efforts universalization of elementary education cannot achieved its goal so Indian government has been started many schemes to hit the target. The current paper  throws the light on various schemes like District Primary Education Program, Operation Blackboard, Sarva Shiksha Abhiyan, National Elementary Education Mission, Mid Day Meal and Non Formal Education etc.
Insights into the Economy of Open Scholarship: A look into OpenEdition with Pierre Mounier, deputy director

Following the report Knowledge Exchange Approach to Open Scholarship and in line with the recommendations resulting from the workshop Moving from Ambition to Reality, two interconnected activities have been dedicated to the Economy of Open Scholarship, one conceptual, one practical. The practical activity - Insights into the Economy of Open Scholarship: A Collection of Interviews - have collected examples which illustrate how pioneering initiatives set up and operate new or alternative business models in the area of Open Scholarship. Interviews with a selection of initiatives have been conducted to build this collection to illustrate the successes and challenges within the economy of Open Scholarship. The purpose of this practical investigation is to inform and inspire a transition to Open Scholarship. Through a collection of interviews, including highlights and currents trends, the collection displays how pioneers and early adopters of openness pave the way for unhindered access to research outputs in relation to research community needs and practices. Interviews have been conducted with ten selected initiatives including:1. Open Library of Humanities (OLH) (UK): Martin Paul Eve 2. OpenEdition (FR): Pierre Mounier 3. Opasnet (FI): Jouni Tuomisto 4. ASAPBio (US): Jessica Polka 5. ScienceOpen (GER): Stephanie Dawson 6. HRCAK (HR): Jadranka Stojanovski 7. Helsinki University Press (HUP) (FI): Leena Kaakinen 8. Impactstory (US): Heather Piwowar 9. Figshare (UK): Mark Hahnel 10. Zenodo (CH): Tim SmithFull details of all interviews are available on the Knowledge Exchange website: http://www.knowledge-exchange.info/event/insights-into-open-scholarship
Awareness About Information Security And Privacy Among Healthcare Employees

Aim: The aim of this study was to analyze healthcare employees’ knowledge of information security and potentially risky behavior on the Internet considering demographic parameters and in comparison with the standardized behavioral norms among Internet users in Croatia. Methods: The study was conducted as a cross- sectional study. Healthcare employees from three hospitals in different geographical areas (Osijek, Pula and Zagreb) were included in this study. The validated UISAQ (Users’ Information Security Awareness Questionnaire) was used for data collection. The questionnaire contains 33 questions, grouped in two scales and six subscales, and participants were self-evaluated using Likert scale. The time period of data collection was the summer of 2017. Results: Surveyed healthcare employees show significantly less risky behavior and overall better knowledge than the average Internet user in Croatia. Female participants display online behavior that is less risky than that of the male participants ; participants with a university degree are better at PC maintenance, while participants with a high school diploma are more skeptical in regard to loss of personal or professional data. Older people are significantly more careful and lend their access data to other colleagues at work less often. Conclusion: Healthcare employees included in this study display partially better results than the average Internet users in Croatia when it comes to their knowledge and potentially risky online behavior. However, their average estimations are only partially better than referent estimations and their scores are not very high, especially when it comes to their awareness measured in the “Security in Communications” and “Secured Data” subscales. As there is high risk of losing data because of the nature of business protocols, healthcare employees need more education and training in order for their awareness regarding the importance of information security and privacy to increase.
The Structure and Characteristics of Narrative Framing of Modern History in State-Published High School History Textbooks in Korea

This paper focuses on the narrative style of the modern period in state-issued Korean high school history textbooks. Mainly, the paper will look at the setting, contents, characteristics, and meaning of the narrative framing of the modern period in Korea called ‘the pre-modern-the early modern-the development of modern’ which was widely placed in Korean high school history textbooks during the 3rd to the 7th national curriculum.BR The problem is that, regardless of their surface type or title, the structure and logic of composition were not consistent, and the sequential connectivity was not systematic nor organized. Therefore confusion became a standardized image of the modern era presented in Korean history textbooks due to the lack of connectivity and mismatch between the title and its content. BR Major features of the state-published history textbooks include: adhering to the modernization-oriented perspective by keeping the Western-centric classification and standardizing the western modernization; setting the pre-modern and early modern periods as independent periods; displaying a list of facts in the development of modern period that are fragmented and decentralized as it is detached from the previous or the next period; and taking syntactic approach for composition and reading-focused structure.
New Multi-secret Sharing Scheme based on Superincreasing Sequence for Level-Ordered Access Structure

A new multi-secret sharing scheme (MSSS) that uses a superincreasing sequence and realizes level-ordered access structure (LOAS) is proposed. The novelty of the scheme is that not only a single share for all the secrets is sufficient but also the secrets can be changed without renewing the shares of the participants. The proposed scheme has low communication cost and less overhead for the participant.
Concluding Remarks and Perspectives in Computer Vision

The previous chapters covered some topics relating to computer vision: how global and local features are generated, how to fast index them and how to implement content-based retrieval algorithms in relational database management systems. Chapter 1 is an introduction to the book subject. Chapter 2 presents several methods for image feature detection and description, starting from image interest points, through edge and blob detection, image segmentation till global features. Chapter 3 concerns feature comparison and indexing for efficient image retrieval and classification. Chapter 4 presents novel methods for feature description and Chap. 5 consists of a set of relational database implementation. Computer vision is not a mature discipline and is continually developing and evolving. Therefore, it is not possible to cover all the directions and solve all challenges within the scope of one book.
Toward an aspect-oriented cache autoloading framework with annotation

In recent years, researches focus on addressing the query bottleneck issue using data cache in the internet-of-things. However, the challenges of this method are how to implement autonomous management of data cache. In this paper, we propose an aspect-oriented cache autoloading framework with annotation (ACALFA). The architecture, annotation, expression are introduced to address cache autoloading. There are some features for improving performance, such as avoiding cache breakdown and cache penetration using load waiting and autoloading, loose coupling of business and cache logic using AOP, and batch delete of cache. The result of experiments indicated that our method is nearly 25% faster than other cache frameworks in case of high concurrency.