Reviewerly/exHarmony huggingface dataset:
A Study on Effective Instructional Design for IoT Security Education Focusing on Experiential Learning

This paper discusses about effective instructional design for fostering security engineers, especially focusing on experiential learning. The authors prepared two groups, the proposed experiential learning group (experimental group) and non-experiential learning group (control group), and investigated each learning effectiveness by using several evaluation metrics. The evaluation metrics included pre-/post-test scores, delayed-test score and learning motivation score. The result of statistical evaluation wasthat there was no significant difference about pre-/post-test scores and also learning motivation scores between the two group. However, there was significant difference about delayed-test scores between them. This interesting result explains that the learners in the both groups had higher learning motivation and they actually acquired knowledge about cybersecurity in each lecture, nevertheless, almost learners in the non-experiential learning group couldn’t keep to retain the knowledge they acquired several weeks ago, meanwhile almost learners in the experimental learning group could keep it. This result indicates that experiential learning can influence learner’s knowledge retention and has possibility to bring long-term knowledge retention than the case of non-experiential learning. Although the fact was empirically known among most lecturers, this experiment proved it based on statistical quantitative evidence. The contribution of this paper is the quantitative evidence. The result of this paper provides one of the evidences that instructional design including experiential learning brings superior learning effectiveness for cybersecurity education.
Ontological Approach Based on Multi-Agent System for Indexing and Filtering Arabic Documents

In recent years, Automatic Natural Language Processing (ANLP) for Arabic language has received a great amount of attention for the development of several applications such as question answering, information retrieval and translation, etc.However, there are a few automated applications using Semantic Web technologies for retrieving Arabic-language documents despite the high demand and need for this content.In addition, the Arabic language presents serious challenges to researchers and developers of NLP applications.These challenges are due to the complexity of the morphological, syntactic and semantic characteristics specific to the Arabic text, which requires the use of semantic resources such as ontology.In our work, we propose a new approach based on ontology and multi-agent systems to index and filter Arabic documents.Our proposal is composed of five layers, each layer contains several agents: (1) Lexical Layer; (2) Syntactic Layer; (3) Semantic Layer; (4) Indexing Layer; and GUI/Interface Layer.Our Arabic ontology is manually constructed on the basis of schemes and their semantics meanings.We use also combination of Arabic WordNet contents and Arabic VerbNet in the process of constructing the ontology.We use the semantic similarity to find the relevant documents according to the user's queries.The aim of this paper is to study the effect of patterns in solving the problem of the semantic indexing system (SIS).The main objective is to improve the quality of the indexing process to ensure the accuracy of the information search of relevant documents based on us ers' multiword queries, and also to reduce indexing and search time.Indeed, our experiments are conducted on the basis of the combination of two Arab corpus: OSAC and SemEval.We compared our results Ontological Approach Based on Multi-Agent System for Indexing and Filtering Arabic Documentswith Lucene in-dex for the same data and, we found that our approach achieves much better results than the other.
Application of Global Optimization Methods to Increase the Accuracy of Classification in the Data Mining Tasks

The article describes the solving of data mining task using neural-like structures of Successive Geometric Transformations Model (NLS SGTM).The main problems of this task are imbalanced dataset and different weigh of errors.Therefore, to take into account these features, the method of penalties and rewards was used, as well as a piecewise linear approach to classification.The supplement of the methods used by the final optimization procedure is proposed.The procedure of final optimization using simulated annealing.
Text Readability: A Snapshot

Selecting suitable reading materials are taxing and challenging for many English instructors. Text readability analysis can be used to automate the process of reading material selection and also the assessment of reading ability for language learners. Readability formulas have been broadly used in determining text difficulty based on learnersâ€™ grade level. Based on mathematical calculations, a readability formula examines certain features of a text in order to provide best rough approximations as an indication of difficulty. This paper reflects some aspects and issues of readability analysis.
A Study for the Development of Automated Essay Scoring (AES) in Malaysian English Test Environment

Automated Essay Scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational assessment context. It is developed to overcome time, cost, and reliability issues in writing assessment. Most of the contemporary AES are “western” proprietary product, designed for native English speakers, where the source code is not made available to public and the assessment criteria may tend to be associated with the scoring rubrics of a particular English test context. Therefore, such AES may not be appropriate to be directly adopted in Malaysia context. There is no actual software development work found in building an AES for Malaysian English test environment. As such, this work is carried out as the study for formulating the requirement of a local AES, targeted for Malaysia's essay assessment environment. In our work, we assessed a well-known AES called LightSide for determining its suitability in our local context. We use various Machine Learning technique provided by LightSide to predict the score of Malaysian University English Test (MUET) essays; and compare its performance, i.e. the percentage of exact agreement of LightSide with the human score of the essays. Besides, we review and discuss the theoretical aspect of the AES, i.e. its state-of-the-art, reliability and validity requirement. The finding in this paper will be used as the basis of our future work in developing a local AES, namely Intelligent Essay Grader (IEG), for Malaysian English test environment.
Development of the Human Resources Information System (HRIS) Based on Usability Analysis with Usefulness, Satisfaction, Ease to Use (USE) Questionaire and Cognitive Walkthrough Methods

HRIS application is an application that is needed to manage human resources well and integrated the system. Usability in a system is very necessary in the development of a system so that it can affect the use of the application. The author tests the usability user interface on HRIS products using the cognitive walkthrough evaluation method, which works through a series of task scenarios and asks a number of questions from the user's perspective, and the method of usefulness, satisfaction, ease of use (USE) Questionnaire to assess how much usability is done by the user. The cognitive walkthrough testing is done by using the application that has been made by the author, after that the author makes improvements to the system according to the results of the test. Then after being repaired, the system was tested again with the same application, then the results were assessed using the USE Questionnaire and compared with previous results. The result is that the system has increased from previously by 3.67 from a scale of 1-5 to 4.04, up by 0.34.
The Image Recognition Based on Restricted Boltzmann Machine and Deep Learning Framework

For the Unmanned Aerial Vehicle (UAV) has high mobility, it is adopted to reducing the difficulty in the patrolling of electric power line is a hard work by manual way. However, the judgment of damaged poles is still carried out by the patroller which is inefficient and fallible. So the method with artificial intelligence is considered to be introduced that a novel method is proposed in this paper to improve the recognition effect in complex background. Restricted Boltzmann Machine (RBM) is used to instead the full connected layers of faster regions with convolutional neural network (faster RCNN). For RBM has the ability of unsupervised learning, with the RBM and faster RCNN combined, it can reduce the training samples and influence of different background in the images to be identified. The experimental results show that the proposed model takes effects on the recognition of the wire poles in the distribution network which has practical value.
Research on Wide-area Distributed Power Quality Data Fusion Technology of Power Grid

With the advancement of the "big operation" system construction, the online monitoring system for power quality has been integrated, and various power quality data have been incorporated into relevant organizations for unified management. Power quality management has a larger range of data, more types, and higher frequency. It needs to realize the unified storage management and efficient access of massive heterogeneous power quality data for the characteristics of data applications and the collection and aggregation of these effective data. This paper proposes a new type of grid wide-area distributed power quality data integration architecture, which is designed for multi-source, heterogeneous, distributed data integration technology and wide-area distributed data storage technology to solve the big data source problem and realize the sharing of power quality data information of the whole network.
Radial Shearing Interferometer Based on a Cosinusoidal Zone Plate

A simple and compact radial shearing interferometer (RSI) based on a cosinusoidal zone plate (CZP) is proposed. The CZP can modulate both phase and amplitude, it has two foci, so as the nominal plane beam under test passes through the CZP, it is diffracted into two parts, i.e., a converging beam and a diverging beam. Then, the two beams can be considered as the compressed beam and the expanded beam in the RSI, respectively. A charge-coupled device detector located in the vicinity of the CZP records the interference pattern. This RSI needs only a single zone plate and therefore can be made very compact. The experiment validates the correctness of the proposed method. With the compactness and stability, the presented interferometer is expected to be used in the detection of moving objects and dynamic processes.
Unsupervised Learning of Monocular Depth and Large-Ego-Motion With Multiple Loop Consistency Losses

We propose UnLearnerVO, a jointly unsupervised learning framework for monocular depth, camera motion estimation from videos. UnLearnerVO is coupled with the relationships of 3D scene geometry and can estimate the 6-DoF pose of a monocular camera in an end-to-end pattern. There are two significant features of the proposed UnLearnerVO: one is an unsupervised depth learning pipeline based on the consecutive /inconsecutive frames, and the other is robustness in a scenario with large camera motion. Specifically, we deeply excavate the pose loop consistency loss, thus optimizing the camera pose and enforcing consistency of the estimated poses across consecutive and inconsecutive frames. Furthermore, a photometric loop consistency loss is proposed, which reduces the disturbance caused by factors such as the dynamic motion object and the photo inconsistency. The experiments on KITTI datasets show that our UnLearnerVO achieves the state-of-the-art results in large camera motion scenarios and performs better than the currently popular unsupervised approaches.
Geographical Context Influence on Co-design Practice Between Indonesia and the UK Context

(2019). Geographical Context Influence on Co-design Practice Between Indonesia and the UK Context. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 763-776.
A Branch of the Library for Natural Sciences of RAS in Pushchino Research Center as a link of management information system for science

This paper describes main the direction of activities at the Central Library in Pushchino Research Center (PNC) of the Russian Academy of Sciences (a branch of the Library for Natural Sciences of RAS), which provides PNC scientists with the information they need. The most important activities involve the development and implementation of new methods for delivering information support to scientific studies, based on the latest networking technologies. In addition, the library supports the development of information systems in the field of physical and chemical biology, along with bibliometry and scientometrics research.
Pedestrian Proposal and Refining Based on the Shared Pixel Differential Feature

We design a pedestrian proposal and refining system tailored for fast pedestrian detection. The pedestrian proposal is based on pixel differential feature (PDF), which is a light weighted feature with a high recall rate. For the pedestrian refining, we propose an aggregated region feature (ARF) to distill the co-existing dominant pixel differential patterns in a local region to reject hard false positives. Albeit discriminative, ARF largely relies on the size of anchored regions and the scale of the PDF, which hinders its performance in real-world applications. Although multi-scale PDF with spatial pyramid somewhat alleviates this problem, it is computationally expensive and thus infeasible in practice. To address this issue, we further propose a directional radius pooling method to extract discriminative information in each orientation of PDF while reducing the feature dimensionality with a more compact size. The pedestrian proposal and refining framework is built on the shared pixel differential feature map which is very computationally efficient. More specifically, a set of pedestrian proposals generated from the single-scale PDF are first obtained in images. Second, multi-scale ARF in spatial pyramid is used to fuse information from different scales and spatial resolutions for anchor regions. Third, the directional radius pooling method is proposed to extract dominant information of each orientation in the anchor regions. The pedestrian proposal and refining are finally integrated for accurate pedestrian detection. The extensive experimental evaluations on five public benchmarks show that our method achieves state-of-the-art results while running at 18 fps for $480\times640$ images.
The Nexus between Export and Economic Growth in Tanzania

This study explored the causal relationship between exports and economic growth in Tanzania. It analyzed time series data for the period of 1980 to 2015. Economic growth is measured in terms of growth per cent while exports are measured in percentage change of goods and services sold abroad. Econometrics analysis was employed in the due course. Such procedures as testing for the presence of unit root, co-integration and causality were done. Furthermore, the Johansen co-integration and Granger causality tests were employed to examine the long-run relationship among variables. The results of co-integration indicate the existence of one co-integrating equation. The causality test results exhibited causality which runs from economic growth to exports. The results conclude that, in the long run, there is a relationship between exports and economic growth in Tanzania. This study recommends the Government to make efforts to improve exports and eventually, in the long-run, rejuvenating the economy.
Jet Features: Hardware-Friendly, Learned Convolutional Kernels for High-Speed Image Classification

This paper explores a set of learned convolutional kernels which we call Jet Features. Jet Features are efficient to compute in software, easy to implement in hardware and perform well on visual inspection tasks. Because Jet Features can be learned, they can be used in machine learning algorithms. Using Jet Features, we make significant improvements on our previous work, the Evolution Constructed Features (ECO Features) algorithm. Not only do we gain a 3.7× speedup in software without loosing any accuracy on the CIFAR-10 and MNIST datasets, but Jet Features also allow us to implement the algorithm in an FPGA using only a fraction of its resources. We hope to apply the benefits of Jet Features to Convolutional Neural Networks in the future.
Wheelchair Control Based on Facial Gesture Recognition

In this article, an approach for controlling a wheelchair using gestures from the user's face is presented, particularly some commands for the basic control operations required for driving a wheelchair are recognized. In order to recognize the face gestures an Artificial Neural Network which is trained since it is one of the most successful classifiers in Pattern Recognition. In particular, the authors' proposed method is useful for controlling a wheelchair when the user has restricted (or zero) mobility in some parts of the body such as: legs, arms or hands. According to their experimental results, the proposed approach provides a successful tool for controlling a wheelchair through a Natural User Interface based on machine learning.
Students� Writing Ability on English Descriptive Text at Grade VIII in SMPN 33 Padang

There are 5 indicators in writing descriptive texts such as: identification, description, use of language, vocabulary and mechanics. Based on the problems found at the beginning of the research, the purpose of this research was to find the students ability in writing descriptive text on English subject in SMPN 33 Padang. The type of research is descriptive research. Participants of the research were VIIIs grade students at SMPN 33 Padang. Data obtained through writing descriptive text tests. The data is analyzed by combining Heatons theory of assessment in writing and research rubrics about writing. The results of the research concluded that students ability in writing identification was Good to Average, students ability in writing descriptive was Fair to Poor, students ability in language use was Very Poor, students ability in using vocabulary was Fair to poor, and students ability in using mechanics was Fair to Poor. Then, the results of the research also showed that students had difficulty in identifying objects clearly, difficulties in developing related ideas, students lack of understanding of simple present tense structures, limited vocabulary, and errors in mechanics. Keywords: Writing Ability, English Descriptive text
INFORMATION TECHNOLOGY AS A COMPONENT OF IMPROVING THE TRAINING QUALITY FUTURE SPECIALISTS IN HIGHER EDUCATION INSTITUTIONS

The study and solution of the problem of informatization of higher education, which is that as a result, global rationalization of intellectual activity in education through the use of artificial intelligence should be achieved in order to increase the efficiency and quality of training specialists to the level of information culture. Ability to use artificial intelligence systems in the learning process allows you to have an idea of the object of knowledge, and helps to master all the diversity and complexity of relationships characteristic of real processes, trace the dynamics of these relationships when changing external and internal factors. The article analyzes the use of information technologies as a factor for improving the quality of training of future specialists in higher education institutions and the actual problems of professional training of specialists and ways of their solution. The types of innovative teaching methods in higher educational institutions are investigated and the role of information and social technologies in education is considered. The peculiarities of the programmed educational facilities are revealed and the directions of use of information technologies in the educational environment are substantiated. The stated goal set the task: to analyze the possibilities of modern information educational technologies and to investigate the development of the concept of pedagogical innovation as an innovation in the field of pedagogy; to determine the possibilities of using artificial intelligence systems in order to implement a differentiated approach to students with different levels of training; to identify promising directions in the field of application of information technologies in the process of training future specialists in higher education institutions.
Index on Students’ Reading Interest

Reading habit is an important thing for people’s life. Through reading, people are getting information and knowledge that could change their attitude, behavior, or other new things. This study aimed to know the index on students’ reading interest and the factors that influenced the reading interest. Using questionnaire based on Motivation for Reading Questionnaire, the researcher collected the data. The participants were students of 6th, 9th, and 12th grade in Probolinggo. The result showed that students’ reading interest was based on the amount of book they have read. Moreover, the factor was influenced by activity, involvement, and motivation on reading.
Medical Image Denoising Using Bilateral Filter and the K-SVD Algorithm

Medical images are inevitably affected by noise in the acquisition process. This paper describes an image denoising method based on bilateral filter and the K-SVD algorithm. Firstly, the method uses bilateral filter to divide the image into edge layer and residual layer. Then the K-SVD algorithm is used to process the residual layer of the image to avoid damaging the edges of the image. Finally, the image denoising result is obtained by adding residual layer and edge layer. The experimental results for image with different intensity noise show that the proposed method can acquire higher peak signal-to-noise ratio (PSNR) than the K-SVD algorithm. In the denoising experiments of computed tomography (CT) and magnetic resonance (MR) images, the proposed method can obtain clearer soft tissue and bone structure than the K-SVD algorithm.
Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

Image compression is a process based on reducing the redundancy of the image to be stored or transmitted in an efficient form. In this work, a new idea is proposed, where we take advantage of the redundancy that appears in a group of images to be all compressed together, instead of compressing each image by itself. In our proposed technique, a classification process is applied, where the set of the input images are classified into groups based on existing technique like L1 and L2 norms, color histograms. All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook. In the process of extracting the different sub-images, we used the mean squared error for comparison and three blurring methods (simple, middle and majority blurring) to increase the compression ratio. Experiments show that varying blurring values, as well as MSE thresholds, enhanced the compression results in a group of images compared to JPEG and PNG compressors.
Health and Wellbeing. Challenging Co-Design for Difficult Conversations, Successes and Failures of the Leapfrog Approach

(2019). Health and Wellbeing. Challenging Co-Design for Difficult Conversations, Successes and Failures of the Leapfrog Approach. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 575-587.