Reviewerly/exHarmony huggingface dataset:
Adler 개인심리학에 근거한 청소년의 학습동기향상 프로그램 개발 및 효과검증

The purpose of the study is to develop learning motivation enhancing program and to verify the effects on self-determination motivation, academic self efficacy, and social interest based on the Adler’s individual psychology. The subjects of this study were 30 third grade students. For statistical methods, repeated measurement analysis and multiple comparison analysis were used.The results of the study are as follows. First, the result of the development of the learning motivation enhancing program based on the Adler’s individual psychology improved and maintained self-determinate motivation. Second, the result of the development of the learning motivation enhancing program based on the Adler’s individual psychology improved and maintained academic self-efficacy. Third, the result of the development of the learning motivation enhancing program based on the Adler’s individual psychology improved and maintained social interest. Finally, discussion and suggestion of the learning motivation enhancing program based on the results of the study were proposed.
Anomaly Detection of Hard Disk Drives Based on Multi-scale Feature

Hard disk drives (HDDs) as a cheap and relatively stable storage tool are widely used by enterprises. However, there is also a risk of fault to the hard disk. Early warning of the HDDs can avoid the data loss caused by the hard disk damage. This paper describes our submission to the PAKDD2020 Alibaba AI Ops Competition, we proposed an anomaly detection method of HDDs based on multi-scale feature. In our method, the original data are classified according to the characteristics of different attributes and proposed a multi-scale feature extraction framework. In order to solve the problem of different data distribution and sample imbalance, the health samples were sampled in time. Finally, we use Lightgbm model to regress and predict the hard disk that will break in the next 30 days. On the real dataset get the 0.5155 precision and 0.2564 recall. Final rank is 24.
Comfortable library space in the Information Society. (On the occasion of Lev Z. Amlinsky’s 90&lt;sup&gt;th&lt;/sup&gt; Anniversary)

The paper coincides with the 90-th anniversary of Lev Zakharovich Amlinsky, professor and doctor of science in pedagogy. The findings of Lev Amlinsky’s studies represented in his recent publications in «Scientific and Technical Libraries» journal for the period of 2011–2016 are analyzed. The subject scope of the works comprises: design of remote library and information services and library resources, architectural and constructional design, provision of psychological comfort for patrons and librarians. Lev Amlinsky’s concept of library design is an anthropocentric one, i. e. the interests of humans, patrons and librarians, must primarily come to the fore when designing libraries. The necessity for apprehending traditional libraries as the space for patrons and librarians, rather than the physical space for books is substantiated. Technological dependence of interior library space has to be replaced with psychological and ergonomic expediency. The scholar has validated a fundamentally new concept of library space arrangement: the libraries are pieces of architecture, however, their interior architecture should meet their functional purpose. The role of efficient architect – librarian cooperation between an under librarian’s leadership is emphasized.
Data Encryption Based on Exponential Numbers

Abstract A novel data encryption scheme using a set of exponential numbers is proposed in this paper which aims at achieving secure communications over public networks. The study begins with the definition of pattern of exponential number, where the decimal pattern of an exponential number g e is the result of taking e − 1 times multiplication the base element g by itself. The multiplication operation of two large integers involves the complicated carry conditions in each step, which is digit-dependence and can only be analyzed case by case. This implies that the pattern of a large exponential number is unpredictable. Next, we prove that nature number can be spanned by a set of exponential numbers, and there exists numerous spanning sets for one nature number, demonstrating that the expression of nature number using a set of exponential numbers is not unique. The unpredictable nature of exponential patterns and the abundant available sets of exponential numbers can contribute the concealment of information when data streams are encrypted by the exponential numbers. In addition, we prove that the base elements of a set of exponential numbers applied for spanning a nature number can be arbitrarily assigned. With this property, we can distribute a secret key sequence in advance as the base elements for data encryption, or this secret key can be shared using the Diffie-Hellman Key-Exchange system over public network. Finally, the encrypted message consists of a sequence of exponents and the index data, which indicates the relationship between the base elements and the secret key for decryption. The ciphertext is transmitted via the public channel to the receiver end; however, it can only be decrypted by those who own the secret key. Some examples are shown for demonstration the feasibility of the proposed encryption scheme.
Exact <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>k</mml:mi></mml:math>-Component Graph Learning for Image Clustering

The performance of graph-based clustering methods highly depends on the quality of the data affinity graph as a good affinity graph can approximate well the pairwise similarity between data samples. To a large extent, existing graph-based clustering methods construct the affinity graph based on a fixed distance metric, which is often not an accurate representation of the underlying data structure. Also, they require postprocessing on the affinity graph to obtain clustering results. Thus, the results are sensitive to the particular graph construction methods. To address these two drawbacks, we propose a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>k</mml:mi></mml:math>-component graph clustering (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>k</mml:mi></mml:math>-GC) approach to learn an intrinsic affinity graph and to obtain clustering results simultaneously. Specifically, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>k</mml:mi></mml:math>-GC learns the data affinity graph by assigning the adaptive and optimal neighbors for each data point based on the local distances. Efficient iterative updating algorithms are derived for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>k</mml:mi></mml:math>-GC, along with proofs of convergence. Experiments on several benchmark datasets have demonstrated the effectiveness of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mi>k</mml:mi></mml:math>-GC.
Research on object recognition and manipulator grasping strategy based on binocular vision

Machine vision has been successfully applied in a series of challenging environments, but at the same time, it has encountered problems[1]. In order to solve this problem, this paper presents an object recognition and manipulator grabbing strategies based on binocular vision. Our method first uses the feature point matching algorithm based on binocular vision to recognize the target, then through the detection of the target object in the image, through the extraction and recognition of the target point, the pose estimation and location of the grab point, determines which grab strategy to operate.
Sepsis Prediction using Continuous and Categorical Features on Sporadic Data

Sepsis is one of the most prevalent causes of mortality in Intensive Care Units (ICUs) and also one of the most expensive health-care problems. Delayed treatment is associated with increase in death and financial burden. This work proposes an early prediction of sepsis validated on Physionet Challenge 2019 dataset. The challenge is to extract continuous, categorical and domain-specific discriminating features from highly sporadic lab data and vital signals. We find that the imputation of extremely isolated data lower the prediction performance. In order to mitigate this, we use a sliding window on sporadic data to generate continuous features which capture the trend. We also devise a binning approach to generate categorical features from the aperiodic data in order to discriminate the deviation from normalcy. Lastly, we observe that a logical fusion of Random Forest and Logit Boost provides optimal performance. Normalized Utility Score (NUS) is used to benchmark the performance of the proposed baselines. Five-fold cross-validation of the best preforming pipeline across the data reveals high median NUS of 0.401.
CUDA Accelerating of Fractal Texture Features for a Neuro-morphological Image Segmentation Approach

Image segmentation is one of the main tasks for many computer vision problems. In this paper, a GPU acceleration for a Fractal features extraction method is proposed, followed by our neuro-morphological approach that will allow to segment the images based on the Fractal texture features. In the first step, we use the CUDA environment on an NVIDIA GPU to compute the Fractal features in parallel for each pixel of our image, this makes it possible to optimize the extraction phase before starting the image segmentation by using our approach which is divided into two main steps. Firstly, we train a Kohonen self-organized Map (KSOM) using the extracted features. In the final step, we use our watershed method to extract the modals regions from the KSOM, these regions define the final regions found in the segmented image. To highlight the effectiveness of our parallel implementation, the performance results of the GPU extraction method are compared to his sequential counterpart based on CPU. In addition, the segmentation rate of the proposed approach is compared to the K-means results.
Analysis of Segmentation Techniques for Synthetic Aperture RADAR Image Processing Applications

Synthetic aperture RADAR (SAR) is an emerging technology primarily used for high resolution ground mapping. SAR's capability to perform under adverse weather conditions, day or night makes it advantageous for many civilian and military applications. It is an active imaging system and inherently suffers from speckle noise, which is multiplicative in nature. Speckle noise originates from the interferen'ce of the coherent wave fronts associated with the reflected signal. The quality of SAR images is significantly degraded by this interference. Conventional intensity-based image processing techniques do not perform well for SAR images because of multiplicative nature of speckle. A variety of suggested image segmentation techniques suitable for SAR images have been analysed in this paper. The paper performs comparative analysis of image segmentation techniques that are based on different features of the images and are specialized to perform on SAR images. Experiments are performed on synthetically generated images and real SAR images obtained from Moving and Stationary Target Acquisition and Recognition (MSTAR) public database.
Reading Aid Machine for Elderly and Visually Impaired Using Single-Board Computer

This paper presents a prototype machine for the elderly and visually impaired by using BPI-M3 single board computer. The program can perform a complete test without any crashing. In term of accuracy for TH SarabunPSK font translation sampling from 10 trials with various font sizes appear as follows: Font Size 10, 12, 14, 16, 18, 20 and 22. An average accuracy of 0.9%, 10.5%, 24.6%, 35%, 32.5%, 32.5%, 44%and 47.7% respectively. Require process time in English language as same as Thai translation but can give a result very high accuracy of nearly 100% in all experiment.
A Systematic Way to Implement Private Tor Network with Trusted Middle Node

Initially, legitimate users were working under a normal web browser to do all activities over the internet [1]. To get more secure service and to get protection against Bot activity, the legitimate users switched their activity from Normal web browser to low latency anonymous communication such as Tor Browser. The Traffic monitoring in Tor Network is difficult as the packets are traveling from source to destination in an encrypted fashion and the Tor network hides its identity from destination. But lately, even the illegitimate users such as attackers/criminals started their activity on the Tor browser. The secured Tor network makes the detection of Botnet more difficult. The existing tools for botnet detection became inefficient against Tor-based bots because of the features of the Tor browser. As the Tor Browser is highly secure and because of the ethical issues, doing practical experiments on it is not advisable which could affect the performance and functionality of the Tor browser. It may also affect the endanger users in situations where the failure of Tor's anonymity has severe consequences. So, in the proposed research work, Private Tor Networks (PTN) on physical or virtual machines with dedicated resources have been created along with Trusted Middle Node. The motivation behind the trusted middle node is to make the Private Tor network more efficient and to increase its performance.
The (2, m, 2, ∞, r)-Wizard of Houses

Abstract The ( n, m, l, t, r )-Wizard of houses is a game played by n players p 1 , p 2 , p 3 , …, p n on a board with m houses. There are l colors of token, and t tokens for each color. Each player chooses one color from the l colors, so that each has a different color. Player p 1 starts the game by choosing one token of any color and putting it into any house. Players p 2 , p 3 , p 4 , …, p n follow suit, until one round is completed. The game is over when the r th round ends or after the tokens are all played, whichever comes first. In each house, each player who has the highest number of his own colored tokens receives one point. The winner is the player who has the highest score. In this paper, we determine the (2, m , 2, ∞, r )-Wizard of houses. We provide non-losing strategies for both players.
Estimates of certain paraxial diffraction integral operator and its generalized properties

Abstract This paper aims to discuss a generalization of certain paraxial diffraction integral operator in a class of generalized functions. At the start of this paper, we propose a convolution formula and establish certain convolution theorem. Then, with the addition to the convolution theorem, we consider a set of approximating identities and substantially employ our results in generating sets of integrable and locally integrable Boehmians. The said generalized integral operator is tested and declared to be one-to-one and onto mapping. Continuity of the generalized operator with respect to the convergence of the Boehmian spaces is obtained. Over and above, an inversion formula and consistency results are also counted.
GPU based Re-trainable Pruned CNN design for Camera Trapping at the Edge

The advent of distributed computing topologies like edge computing has led to an inadvertent paradigm shift in the world of IoT (Internet of Things). Besides overcoming the latency and bandwidth challenges posed by a conventional cloud-based infrastructure, edge computing has brought an obtrusive decline in energy consumption. In this era of proliferating rate of data generation, usage of Deep learning networks has left an astounding impact on the predictive accuracy and processing time of real-time data. This paper proposes a combination of these emergent technologies to solve the challenges faced in camera traps used for wildlife and marine life monitoring. We, further, devise a pruned neural network architecture that uses a Graphical Processing Unit (GPU) to facilitate computation and optimize the hardware. This paper aims at creating a re-trainable deep learning design that brings the processing of captured images closer to the edge and supports relatively higher frame rates.
Real Time Bayer Raw Video Projective Transformation System Using FPGA

This paper describes a mesh-based real-time video stream projective transformation processing system. Projective transformation requires both matrix multiplication and division, which brings great challenges to hardware implementation. To reduce the complexity of hardware, the division in projective transformation is converted into multiplication. The processing system is performed on the Bayer raw picture before demosaicing right after the camera sensor. By buffering a fixed number of lines, the delay between input and output is fixed and predictable, and the system is more easily solidified on the chip. This design is implemented on Xilinx Zynq-7000 XC7Z045 SOC as a real-time system with the capability of processing 4k Bayer raw video streams.
Single Image Haze Removal Using Iterative Ambient Light Estimation with Region Segmentation

We propose an iterative single-image haze-removal method that first divides images with haze into regions in which haze-removal processing is difficult and then estimates the ambient light. The existing method has a problem wherein it often overestimates the amount of haze in regions where there is a large distance between the location the photograph was taken and the subject of the photograph; this problem prevents the ambient light from being estimated accurately. In particular, it is often difficult to accurately estimate the ambient light of images containing white and sky regions. Processing those regions in the same way as other regions has detrimental results, such as darkness or unnecessary color change. The proposed method divides such regions in advance into multiple small regions, and then, the ambient light is estimated from the small regions in which haze removal is easy to process. We evaluated the proposed method through some simulations, and found that the method achieves better haze reduction accuracy even than the state-of-the art methods based on deep learning.
Build Strategic Alliance Model for Playing Empire Kingdom Video Game

The objective of this paper is to introduce a novel methodology of playing a strategic video game by applying several statistical modeling techniques to meet the required troop quality metric. The focus is on how to build a powerful troop which can both attack the remote, stronger castles and could also defend the main castles when attacking troops were far away. K-Means Clustering computing was utilized to cluster the nearby castles to form a joint alliance which was able to communicate and support each other against any mighty attacking from nearby Kingdoms. Principal Component Analysis (PCA) was utilized to study 7 troop characteristics among 40+ troop types available. The PCA Eigenvalue and Eigenvector analysis could effectively provide the insights of troop improvement strategy based on the PCA patterns and each principal component. Established the Transfer Function Sensitivity Analysis through Custom Profile to conduct the troop design optimization. Based on provided Kingdom's expansion strategy, the optimal troop design was identified. The design optimization algorithm and the optimal design were fully aligned or predicted by PCA eigen analysis. This PCA-based algorithm could maximize the Return of Investment (RDI) on playing strategic video game. The same PCA modeling technique could be extended to most modern Business Management World.
Pedagogical Challenges and Ethical Considerations in Developing Critical Thinking in Cybersecurity

The online world of today offers both possibilities and challenges, increasingly requiring critical thinking to distinguish between truth and lies. One has to tell the real news and videos from the fake ones at election campaigns, to distinguish fake online shops from the real ones, and recognize catfishing attempts in budding relationships. The aim of our paper is to discuss pedagogical and ethical challenges online, using the deliberately created fake accounts as a tool to teach students critical thinking and other digital survival skills. In 2018 and 2019 we carried out an exercise for M.Sc. students (a 50 in each year) involving the creation of fake accounts based on prescribed data. We mapped the developed skills and knowledge, analyzing the legal and ethical aspects of the process (also involving external experts, e.g. physical and cybersecurity specialists, lawyers, educational technologists). We conclude that fake accounts as a learning tool can be a powerful measure to activate new ways of `out of the box' thinking necessary for both cybersecurity experts and common citizens in an information society.
Analysis of Pre-processing Techniques for Odia Character Recognition

OCR is the recognition of characters from digitized documents and the OCR systems tries to eliminate human interactions with computer and now days for better result and effectiveness; OCR systems are completely involved in our daily life. The OCR component becomes an important component of document scanners and is used in numerous fields such as postal address processing, script recognition, cheque processing in banking, passport authentication etc. Research in this field has been underway for more than half a century and the results have been astonishing with effective recognition rates for printed characters exceeding 95%, but not like human eye accuracy and with important efficiency improvements for handwritten cursive character recognition where recognition levels surpassed 90%. The primary job in the pre-processing of the recorded images as input is to increase the identification rate and decrease the complexities. The better the pre-processing of a character document image, a good set of features can be obtained that results a better classification as well as recognition result. This paper describes a full pre-processing method that informs various researchers in this area of research.
Modified Public Key Cryptosystem Based On Circulant Matrix

Abstract Experts believe that public key cryptosystems on non-commutative algebraic structures are resistant to the attack of quantum algorithms. In recent years, public key cryptosystems based on Polynomial Symmetrical Decomposition (PSD) on the non-commutative group have been developed. However, they are vulnerable to direct attack, linearization equations attack, and overdefined systems of multivariate polynomial equations attack. This cryptosystem has also been improved by experts. However, the operation of the proposed PSD Improvement still uses complex computing and untested. Therefore in this paper, we replace PSD on a non-commutative group into a non-commutative matrix group. We chose the circulant matrix on the key agreement protocol and the key distribution. The results show that the cryptosystem proposed on the circulant matrix is resistant to direct attack, linearization equations attack, and overdefined systems of multivariate polynomial equations attack.
Reading Text for School Literacy Movement in Mathematics Learning

This article discusses the results of the reading text development on the small group stage for reading activities on the school literacy movements in mathematics learning with the futsal game context, which is currently being enjoyed by the people, especially among students. This research used a design research method whose type is development studies. The evaluation flow at the prototyping stage uses the formative study flow with the stages of self-evaluation, expert review and one-to-one, small group, and field test. The research subjects were 7th-grade students in one of the junior high schools in Kabupaten Karawang. Documentation, questionnaires, and interviews are ways to collect data in this study. This study has produced the reading texts that are used for the school literacy movement program with the futsal context on practical mathematics learning.