Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF STORYTELLING TOWARDS STUDENTS' SPEAKING SKILL AT X GRADE STUDENTS OF MA NURUL HARAMAIN BOARDING SCHOOL

This study was aimed to investigate whether or not the use of storytelling has positive effect towards the students' speaking skills at the tenth-grade students of MA Nurul Haramain. The form of this research was pre-experimental study with comparing the scores in pre-test and post-test. The population of this research is the tenth-grade students of MA Nurul Haramain Narmada, with the sample is class X B that consists of 21 students. The research findings show that the mean score of the pre-test is 59.05 and the mean score of post-test is 78.48. It shows that storytelling has a significant impact towards the students' speaking skill. The result of sig. 2-tailed (0.00) is lower than sig. level (0.05), also the result of the t-test is higher than t-table, the value is 4.1 &gt;1.6 it means that the alternative hypothesis (Ha) is accepted. It can be concluded that there is a highly significant effect of storytelling technique towards students' speaking skill on the tenth-grade students of MA Nurul Haramain.
BASIC APPROACHES AND PRINCIPLES OF DESIGNING MASTER,S EDUCATIONAL PROGRAMS FOR TRAINING TEACHERS IN ADDITIONAL ENGINEERING EDUCATION

Statement of the problem. The solution of problems related with orientation of secondary school students towards engineering specialties has always been of interest among scientists and teachers-practitioners both in Russia and abroad. For each technological paradigm, the solution of these problems has its own specificity. In 2010, the beginning of the sixth technological paradigm was recorded. It is characterized by such industries as nano- and biotechnology, nanoenergy, molecular, cellular and nuclear technologies, nanobiotechnologies, biomimetics, nanobionics, nanotronics, etc. These industries require a new generation of engineers with systemic interdisciplinary competencies, capable of solving new challenges with new methods and means. Training of such engineers begins with the vocational orientation of schoolchildren, which should be in line with the modern technological paradigm. In order to solve the new problems of professional orientation of schoolchildren towards the profession of an engineer, special training of teachers with modern knowledge and technologies of motivation and training in the context of modern engineering education and professional activity of the engineer is required. Such training can be carried out through special master,s programmes. The purpose of the article is to define theoretical approaches and design principles of master degree educational programs for teachers training in additional engineering education for schoolchildren. The methodology of the study is the analysis of program and regulatory documents in the field of vocational orientation of secondary school students, studies of Russian and international scientists devoted to the implementation of engineering training for schoolchildren, regulatory documents and existing master,s programs in 44.04.01 Pedagogical education; analysis and synthesis of the authors,&nbsp;experience in designing and implementing master,s programs for training secondary school teachers. Research results. The main approaches and principles have been defined of designing master»s educational programs for training teachers in additional engineering education for secondary school students. Conclusion. The system analysis of pedagogical studies, program and regulatory documents in the field of engineering education for secondary school students made it possible to define approaches and formulate principles for designing effective master»s educational programs for training teachers in additional engineering education for secondary school students.
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning

Word embeddings, i.e., low-dimensional vector representations such as GloVe and SGNS, encode word meaning in the sense that distances between words' vectors correspond to their semantic proximity. This enables transfer learning of semantics for a variety of natural language processing tasks. Word embeddings are typically trained on large public corpora such as Wikipedia or Twitter. We demonstrate that an attacker who can modify the corpus on which the embedding is trained can control the meaning of new and existing words by changing their locations in the embedding space. We develop an explicit expression over corpus features that serves as a proxy for distance between words and establish a causative relationship between its values and embedding distances. We then show how to use this relationship for two adversarial objectives: (1) make a word a top-ranked neighbor of another word, and (2) move a word from one semantic cluster to another. An attack on the embedding can affect diverse downstream tasks, demonstrating for the first time the power of data poisoning in transfer learning scenarios. We use this attack to manipulate query expansion in information retrieval systems such as resume search, make certain names more or less visible to named entity recognition models, and cause new words to be translated to a particular target word regardless of the language. Finally, we show how the attacker can generate linguistically likely corpus modifications, thus fooling defenses that attempt to filter implausible sentences from the corpus using a language model.
A robust hand-eye calibration method based on schur matrix decomposition

Accuracy, efficiency and stability are important criteria of hand-eye calibration method. We proposed a novel hand-eye calibration method. As traditional two-step hand-eye calibration methods, first, we solve rotation parameters and then translation vector can be deserved immediately. We obtain a general solution from one observation and decrease degree of freedom from 3 to 2. Through this way, we can use more constraint equations to improve hand-eye calibration's accuracy within same time. Observational data preprocessing is one of the basic problems unresolved of hand-eye calibration algorithms. We derived judge-equation based on schur matric decomposition in order to delete wild data. The method is validated by both simulations and experiments. The results show that the proposed method performs better than the other four popular hand-eye calibration methods.
Empirical Investigations on Usability of Security Warning Dialogs: End Users Experience

The dependencies of the computer and the Internet keep increasing among the users. Thus, it poses to the increasing number of attacks as a result of using various application and tools. Security warning conveys an alert on the potential harm users might expose such as malware and any kind of attacks on their computer. In practice, most of the end users tend to ignore the security warning as it shows the messages repeatedly, although they have been exposed to many risks. A security warning dialogue is supposed to catch the user’s attention and comprehension however, because of users’ past experiences such habituation makes them became less focus. One-to-one interview session with 60 participants was conducted in order to gain further comprehension among the end users experiencing security warning and to investigate the usability issues of current security warning implementation. It is deemed of necessity to discover these usability issues in the current context of security warning presentations. The result revealed that the problems and challenges continue to persist such as difficulties to make a decision, difficulties to comprehend technical jargons, lack of attractiveness of current security warning and issues of habituation or repeated exposures of warnings.
Generating Derivational Relations for the Japanese WordNet: The Case of Agentive Nouns

This paper presents work in progress on the development of derivational links for the Japanese WordNet, with a focus on the retrieval, validation and elaboration of nouns and verbs linked by the agentive noun derivation. 2,340 such links are generated, of which we validated 833 such pairs. We briefly discuss some challenges in determining valid link pairs as well as their morphosemantic natures. We also consider the possibilities and challenges of automating the discovery of morphosemantic links, by linking our results with current theoretical issues in agentive nominais. In addition, we are currently corroborating these Japanese agentive derivations with English counterparts from the Princeton WordNet and intend to perform a more rigorous cross-lingual comparison.
Low-Complexity Deep Neural Networks for Image Object Classification and Detection

Deep Neural Networks (DNN) have been successfully used in object classification and detection. However, the computation complexity and memory requirement of DNN are high. In this paper, we propose a series of methods applied to the original VGG DNN model to reduce the complexity without sacrificing too much the accuracy in image object classification and detection. Furthermore, two compression methods, depthwise separable convolution and pruning, are employed to further reduce the numbers of weights and operations. Experimental results show that the proposed low complexity DNN models significantly reduce the number of parameters and computation complexity compared to the original VGG, GoogLeNet and ResNet-50. The proposed DNN model is also used as feature extraction in the one-stage object detection method, Single Shot Detector (SSD).
LabelMerger: Learning Activities in Uncontrolled Environments

While inferring human activities from sensors embedded in mobile devices using machine learning algorithms has been studied, current research relies primarily on sensor data that are collected in controlled settings often with healthy individuals. Currently, there exists a gap in research about how to design activity recognition models based on sensor data collected with chronically-ill individuals and in free-living environments. In this paper, we focus on a situation where free-living activity data are collected continuously, activity vocabulary (i.e., class labels) are not known as a priori, and sensor data are annotated by end-users through an active learning process. By analyzing sensor data collected in a clinical study involving patients with cardiovascular disease, we demonstrate significant challenges that arise while inferring physical activities in uncontrolled environments. In particular, we observe that activity labels that are distinct in syntax can refer to semantically-identical behaviors, resulting in a sparse label space. To construct a meaningful label space, we propose LabelMerger, a framework for restructuring the label space created through active learning in uncontrolled environments in preparation for training activity recognition models. LabelMerger combines the semantic meaning of activity labels with physical attributes of the activities (i.e., domain knowledge) to generate a flexible and meaningful representation of the labels. Specifically, our approach merges labels using both word embedding techniques from the natural language processing domain and activity intensity from the physical activity research. We show that the new representation of the sensor data obtained by LabelMerger results in more accurate activity recognition models compared to the case where original label space is used to learn recognition models.
A Variable Neighborhood Search-Based Method with Learning for Image Steganography

Image steganography is a security technique that used to hide secret information such as text or image in another cover image. The cover image, including the secret information, seems to be unchanged, and the hidden information can only be recovered by using a particular decoding technique. This paper proposes a variable neighborhood search (VNS)-based method for image steganography. The proposed VNS is combined with the least significant bits method (LSB) and enhanced with a learning process. LSB is the process of adjusting the lower bits of the pixels of the cover image. The least significant bit which is the eighth bit of some or all bytes inside the cover image is replaced by bits of the secret information. We improve LSB by combining it with VNS. The VNS method is a local search meta-heuristic working on a set of different neighborhoods. The basic idea is a systematic change of a certain number of neighborhoods combined with a local search. The objective is to explore the search space efficiently in order to locate the appropriate positions in the cover image where inserting the secret information. Further, a learning process is added to VNS in order to enhance the performance. The proposed methods are evaluated on some series of images. The numerical results are exciting and demonstrate the benefits of the new techniques for image steganography.
Credit card fraud detection using naive Bayesian and C4.5 decision tree classifiers

Growing problem of card payment fraudulent abuse is a main focus of banks and payment Service Providers (PSPs). This study is using naive Bayes, C4.5 decision tree and bagging ensemble machine learning algorithms to predict outcome of regular and fraud transactions. Performance of algorithms is evaluated through: precision, recall, PRC area rates. Performance of machine learning algorithms PRC rates between 0,999 and 1,000 expressing that these algorithms are quite good in distinguishing binary class 0 in our data set. Amongst all algorithms best performing PRC class 1 rate has Bagging with C4.5 decision tree as base learner with rate of 0,825. For prediction of fraud transactions with success of 92,74% correctly predicted with C4.5 decision tree algorithm.
Blockchain-Based Shared Security Architecture

A prototype model named Shared Security Architecture is introduced, that enables to link relational database management system, distributed DBMS and blockchain technology. Blockchain technology is commonly used in cryptocurrencies but the innovative use of this technology can boost cybersecurity and protect databases more effectively. Three different models have been explored with a combination of traditional data storage (RDBMS), distributed data storage and blockchain technology to construct new security for the data storage system. These models allow direct access to frequently used data, which requires read-write property and restricted access to data which requires write property with immutability without paying much on trust and security.
Block-wise Dynamic Sparseness.

Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a way to reduce the memory and computation footprint of these models has seen significant research attention in recent years. In this paper, we present a new method for \emph{dynamic sparseness}, whereby part of the computations are omitted dynamically, based on the input. For efficiency, we combined the idea of dynamic sparseness with block-wise matrix-vector multiplications. In contrast to static sparseness, which permanently zeroes out selected positions in weight matrices, our method preserves the full network capabilities by potentially accessing any trained weights. Yet, matrix vector multiplications are accelerated by omitting a pre-defined fraction of weight blocks from the matrix, based on the input. Experimental results on the task of language modeling, using recurrent and quasi-recurrent models, show that the proposed method can outperform a magnitude-based static sparseness baseline. In addition, our method achieves similar language modeling perplexities as the dense baseline, at half the computational cost at inference time.
Iterated posterior linearization filters and smoothers with cross-correlated noises

In the article, several concise and efficient iterated posterior linearization filtering and smoothing methodologies are proposed for nonlinear systems with cross-correlated noises. Based on the Gaussian approximation (GA), the presented methods are derived via performing statistical linear regressions (SLRs) of the nonlinear state-space models w.r.t the current posterior distribution in an iterated way. Various posterior linearization methods can be developed by employing different approximation computation approaches for the Gaussian-weighted integrals encountered in SLRs. These new estimation methods enjoy not only the accuracy and robustness of the GA filter but also the lower computational complexity. Estimation performances of the designed methods are illustrated and compared with conventional estimation schemes by two common numerical examples.
Inferring Deterministic Regular Expression with Unorder

Schema inference has been an essential task in database management, and can be reduced to learning regular expressions from sets of positive finite-sample. In this paper, we extend the single-occurrence regular expressions (SOREs) to single-occurrence regular expressions with unorder (uSOREs), and give an inference algorithm for uSOREs. First, we present an unorder-countable finite automaton (uCFA). Then, we construct an uCFA for recognizing the given finite sample. Next, the uCFA runs on the given finite sample to count the number of occurrences of the subexpressions (connectable via unorder) for every possibly repeated matching. Finally we transform the uCFA to an uSORE according to the above results of counting. Experimental results demonstrate that, for larger samples, our algorithm can efficiently infer an uSORE with better generalization ability.
Risks of the Blockchain Technology

This paper comprehensively analyses various risks of the blockchain technology in circulation, including wallet security risks, code vulnerability, risks on the validity of the consensus mechanism, Hash algorithm risk, 51% attack risk and exchange risk. At present, with the vigorous development of the blockchain technology and the absence of government and legal supervision, users can only rely on themselves to fully identify risks to effectively avoid loss.
Establishing an Automotive Cyber Defense Center

As vehicles turn into human-transporting computers, more specific attention to the issue of long-term secure operation is needed. In order to prevent cyber-attacks on the fleet, monitoring the internal state of the individual vehicles’ IT-infrastructure is required.In this paper we provide a suggestion on how vehicles could be managed over the course of their lifetime. Establishment of an Automotive Cyber Defense Center is a key factor of ensuring the secure operation of the vehicle fleet by an OEM. Within this paper we demonstrate why establishing such a center is necessary, what kind of security operations it needs to perform and what stakeholders are involved in ensuring secure operation of public road transport.Since Cyber Defense Centers and the required technology are well-established in classical IT-infrastructure, we propose an architecture for the automotive domain which uses these technologies, highlighting the gaps in transitioning from operating a network to operation of a vehicle fleet.The most important difference being the distributed, inhomogeneous and nomadic nature of a vehicle fleet. In order to overcome this gap we provide an exemplary implementation, which aims to make security relevant information available for usage within a Cyber Defense Center, using IoT-technology.
The Effects of an Eco-Environmental Education Program on the Environmental Literacy and Sociality of Elementary School Students

본 연구는 생태환경교육 프로그램이 초등학교 1학년의 환경소양과 사회성에 미치는 영향을 알아보기 위해서, 2015개정 통합교과와 연계한 생태환경교육 프로그램을 개발한 후 ○○시 S초등학교 1학년 2개 학급을 실험집단과 비교집단으로 선정, 8주간, 40차시의 생태환경교육 프로그램 수업을 실시하였다. 이 연구에서 얻은 결과는 다음과 같다. 첫째, 2015개정 통합교과 교육과정 및 교과서 분석을 통해 교육과정을 재구성하여 생태환경교육을 위한 주제중심 통합 프로그램을 개발하였다. 둘째, 본 연구에서 개발된 생태환경교육 프로그램은, 초등학생의 환경소양을 함양시키는데 유의미한 효과가 있었다(P<.01). 셋째, 본 연구에서 개발된 생태환경교육 프로그램은 초등학생의 사회성 함양을 높이는데 유의미한 효과가 있었다(P<.05). 위의 분석 결과로 볼 때, 초등학교 1학년 대상 생태환경교육 프로그램은, 초등학생의 환경소양 및 사회성 함양에 모두 효과가 있는 것으로 볼 수 있다. 본 연구는 학교 환경교육을 시작하는 초등학교 1학년을 대상으로 생태환경교육 프로그램을 적용하여 환경 감수성을 기르고, 이를 기반으로 환경소양 및 사회성을 증진시키고자 한 것이 큰 의의가 있다고 할 수 있다.The goal of this study is to examine the effects of an eco-environmental education program on the environmental literacy and sociality of elementary school first-grade students. In order to achieve the research goals, the eco-environmental education program was developed in connection with the 2015 revised integrated curriculum, and two first grade elementary school classes from S-Elementary School in the Daegu were selected as the experiment group and the control group. Then the experiment group was targeted for an 8-week period, and a total of 40 environmental education program classes were held. The results of this study are as follows: First, through analysis of the 2015 revised integrated curriculum and the textbook, the course was restructured, and a subject-centered, integrated program for eco-environmental education was developed. Second, the eco-environmental education program developed in this study had a positive effect on environmental literacy of elementary school students(P<.01) Third, the eco-environmental education program developed in this study was effective in fostering the sociability of elementary school students(P<.05). This study applies the eco-environmental education program to elementary school first-graders, who are just starting environmental education, in order to raise environmental sensitivity, and with that as a base, to promote environmental literacy and sociality.
Optimal design of window functions for filter window bank

This paper considers the designs of the periodic window functions in the filter window banks. First, the filter window bank with the constant synthesis periodic window functions is considered. The total number of the nonzero coefficients in the impulse responses of the analysis periodic window functions is minimized subject to the near perfect reconstruction condition. This is an $ L_0 $ norm optimization problem. To find its solution, the $ L_0 $ norm optimization problem is approximated by the $ L_1 $ norm optimization problem. Then, the column of the constraint matrix corresponding to the element in the solution with the smallest magnitude is removed. Next, it is tested whether the feasible set corresponding to the new $ L_0 $ norm optimization problem is empty or not. By repeating the above procedures, a solution of the $ L_0 $ norm optimization problem is obtained. Second, the filter window bank with the time varying synthesis periodic window functions is considered. Likewise, the design of the periodic window functions in both the analysis periodic window functions and the synthesis periodic window functions is formulated as an $ L_0 $ optimization problem. However, this $ L_0 $ norm optimization problem is subject to a quadratic matrix inequality constraint. To find its solution, the set of the synthesis periodic window functions is initialized. Then, the set of the analysis periodic window functions is optimized based on the initialized set of the synthesis periodic window functions. Next, the set of the synthesis periodic window functions is optimized based on the found set of the analysis periodic window functions. Finally, these two procedures are iterated. It is shown that the iterative algorithm converges. A design example of a filter window bank with the constant synthesis periodic window functions and a design example of a filter window bank with the time varying synthesis periodic window functions are illustrated. It is shown that the near perfect reconstruction condition is satisfied, whereas this is not the cases for the nonuniform filter banks with the conventional samplers and the conventional block samplers.