Reviewerly/exHarmony huggingface dataset:
Analysis of Characteristics of the Finnish High School Curriculum focusing on Students’Course Selection

[연구목적] 본 연구의 목적은 핀란드 일반계 고등학교의 선택적 교육과정 운영 사례 분석을 통하여 우리나라 고교학점제 운영에 대한 시사점을 도출하는 것이다. [연구방법] 이러한 목적을 달성하기 위해 핀란드의 국가교육과정을 살펴보고 핀란드 수도인 헬싱키시와 인근 도시 에스푸시의 학교교육과정을 분석하여 핀란드의 선택형 교육과정이 어떻게 운영되고 있는지를 살펴보았다. [연구결과] 주요 연구 결과는 다음과 같다. 첫째, 핀란드 고등학교 최소 졸업 요건은 3년간 최소 75개 과정을 이수하고 합격 등급을 부여받는 것이다. 국가교육 과정은 필수 과정과 심화 과정으로 구성되어 있고, 학교교육과정인 응용 과정은 학교별 특색에 맞게 개설&#8231;운영되고 있다. 무학년제를 기본으로 하는 핀란드 고등 학교 교육과정은 학교교육과정 자율성이 높은 반면 필수 과정 비중이 상당히 높은 것이 특징으로 필수 과정의 비율이 최소 47개로 졸업 이수 기준의 60% 이상을 차지한다. 둘째, 헬싱키시의 경우 학생들의 교과선택권을 확보하면서 단위 학교의 부담을 낮추어주기 위해 시가 운영하는 11개 공립고등학교를 3개의 캠퍼스로 묶어 공동교육과정을 운영하며, 헬싱키시 전체가 시간표의 일부를 공통된 시간에 운영하여 단위 학교에서 개설하지 않는 과정은 인근 학교 또는 시내 다른 학교에서 이수할 수 있는 인프라와 사전 수강 신청과 사후 수정&#8231;보완을 통해 학생이 자신이 듣고 싶은 과정을 선택할 수 있는 환경을 구축하고 있다. 셋째, 이수/ 재이수 절차를 법적으로 규정하여 불합격이 된 경우나 성적을 향상시키고자 하는 경우 성적에 대한 이의신청 및 재시험 절차를 거쳐, 판별보다는 성장에 중점을 두고 있다. [결론] 연구 결과에 근거하여 우리나라 고교학점제 성공적 운영을 위해 ‘필수 이수 단위의 확보’, ‘공동 교육과정의 운영’, ‘이수/재이수 제도의 유연성’ 등을 제안 하였다.[Purpose] The purpose of this study was to analyze the characteristics of the Finnish high school curriculum and to find out its implication for Korean high school credit system. [Methods] For this goal, Finland’s curriculum documents, policy documents, and information on web-sites operated by the Finnish National Board of Education and schools of Helsinki and Espoo were analyzed. [Results] This study found that Finland’s high school curriculum requires students to complete minimum 75 courses while making sure students to take 47 or 51 compulsory courses. The compulsory courses account for more than 60%, which secures students to focus on major subjects such as mathematics and mother language. Finnish high schools are given freedom of school curriculum, which made each school’s curriculum different from each other. Helsinki city government helped to build up cooperative environment for high schools with relating public schools into 3 campuses. The city government also helped to build up on-line co-curriculum for all the students of Helsinki city. The Finnish national curriculum gives students chances to recover their failed grades, focusing growth than screening. [Conclusion] Some implications are suggested for Korea’s high school credit system including
Optimized Altitude Control for Quadrotor UAV in Virtual Environment

Quadrotors offer many interesting features and have great potential to be used in many social applications. Unfortunately, due to its nature as an underactuated system, it can be made stable by itself. Hence, such a control strategy is required. This paper presents the optimized PID controller with Firefly Algorithm (FA) for quadrotor in virtual environment. The results verify that the optimized PID controller succeeds to regulate the altitude of quadrotor in the real physics-like of virtual environment.
A Fast Image Enhancement Algorithm of Historical Documents Based on Improved Sauvola

Historical documents are important sources of information. With the information revolution and the advancement of science and technology, the digitalization of historical documents will change the protection, collection, transmission, and utilization of historical documents. However, historical documents often suffer from varying degrees of degradation, such as stains, creases, uneven illumination, and seepage of ink. Compared with the general image, the document image has many characteristics. For example, the background pixels are more concentrated than the foreground pixels, and the foreground and background pixels are relatively concentrated. Based on these problems and characteristics, this paper introduces three commonly used historical document image enhancement algorithms, and improves the Sauvola algorithm for a new historical document image enhancement algorithm that does not have to calculate a special threshold for each pixel, but uses the same threshold within a fixed area to reduce the cost of time while maintaining image quality. Experimental results prove that the improved algorithm can significantly reduce the time required while ensuring the quality of the image.
An accurate and efficient image registration algorithm in the aerial infrared images

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle (UAV) is more and more utilized to capture the aerial images. Therein, the registration of the aerial infrared images play a vital role in the infrared image processing, such as object tracking, information collecting, and behavior analysis. However, due to the low contrast, low-resolution, and few texture features of the infrared images, it is really difficult to implement the image registration in the aerial infrared images. In our work, an accurate and efficient image registration algorithm in the aerial infrared images is put forward. To improve the registration accuracy and efficiency, we construct multiple screening mechanisms to screen out the incorrect and redundant feature points. Besides, to further improve the registration accuracy, the improved SIFT and convolution neural network (CNN) descriptors are combined for the feature description. Finally, the coarse-to-fine feature matching strategy is put forward via FLANN and RANSAC algorithms. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive registration results.
Optimized Object Detection Technique in Video Surveillance System Using Depth Images

In real-timeShahzad Alam, Md. surveillance and intrusion detection, it is difficult to relyAshwin, T. S. only on RGB image-based videos as the accuracyRam Mohana Reddy, G. of detected object is low in the low light condition and if the video surveillance area is completely dark then the object will not be detected. Hence, in this paper, we propose a method which can increase the accuracy of object detection even in low light conditions. This paper also shows how the light intensity affects the probability of object detection in RGB, depth, and infrared images. The depth information is obtained from Kinect sensor and YOLO architecture is used to detect the object in real-time. We experimented the proposed method using real-time surveillance system which gave very promising results when applied on depth images which were taken in low light conditions. Further, in real-time object detection, we cannot apply object detection technique before applying any image preprocessing. So we investigated the depth image by which the accuracy of object detection can be improved without applying any image preprocessing. Experimental results demonstrated that depth image (96%) outperforms RGB image (48%) and infrared image (54%) in extreme low light conditions.
Complexity and Expressive Power of Disjunction and Negation in Limit Datalog

Limit Datalog is a fragment of Datalogℤ—the extension of Datalog with arithmetic functions over the integers—which has been proposed as a declarative language suitable for capturing data analysis tasks. In limit Datalog programs, all intensional predicates with a numeric argument are limit predicates that keep maximal (or minimal) bounds on numeric values. Furthermore, to ensure decidability of reasoning, limit Datalog imposes a linearity condition restricting the use of multiplication in rules. In this paper, we study the complexity and expressive power of limit Datalog programs extended with disjunction in the heads of rules and non-monotonic negation under the stable model semantics. We show that allowing for unrestricted use of negation leads to undecidability of reasoning. Decidability can be restored by stratifying the use of negation over predicates carrying numeric values. We show that the resulting language is Π2EXP -complete in combined complexity and that it captures Π2P over ordered structures in the sense of descriptive complexity.We also provide a study of several fragments of this language: we show that the complexity and expressive power of the full language are already reached for disjunction-free programs; furthermore, we show that semi-positive disjunctive programs are coNEXPcomplete and that they capture coNP.
Spacecraft Anomaly Detection and Relation Visualization via Masked Time Series Modeling

Anomaly detection (AD) refers to find patterns in time series data that do not behave expectedly. Current state-of-the-art anomaly detection method, based on reconstruction error generated by LSTM sequence modeling. Recently, the remarkable improvement achieved by BERT model in language translation demonstrated that transformer is superior to LSTM models, due to its extracting relations ignoring distance. In this paper, we propose a transformer-based architecture, Masked Time Series Modeling, modeling data stream. We compared the performances of our method with state-of-the-art AD methods on challenging public NASA telemetry dataset. The experiment results demonstrated our method saves about 80% time cost because of parallel computing compared with LSTM methods and achieves 0.78 FI point-based score. Moreover, we visualize anthropogenic anomalies through attention score matrix.
Secure Ambient Assisted Living System using Elliptic Curve Cryptography based CPABE

Ambient Assisted Living (AAL) system plays a vital role in the field of Internet of Things (IoT) based healthcare. AAL provides continuous monitoring of the patient and consequently increases the quality of the medical service. One of the major issues in AAL service is the security. In this paper, we have proposed an efficient Elliptic Curve Cryptography (ECC) based Ciphertext-Policy Attribute Based Encryption (CPABE) for the well accepted existing framework of AAL. The proposed CPABE is secured under Elliptic Curve Decisional Diffie-Hellman (ECDDH) assumption. We have evaluated the performance of the proposed CPABE with the existing schemes and result shows that the proposed scheme is more efficient in terms of computation overhead.
An Exploratory Study of University Freshmens Perceptions of Class Choice and Good Teaching

본 연구는 대학 신입생이 수업 선택과 좋은 수업에 대해서 어떻게 인식하고 있는 지를 살펴보고자 한다. 대학 신입생들이 수업선택 시 고려하는 요인과 참고하는 정보에 대해 살펴보고 좋은 수업의 특징에 대한 인식을 조사하였다. 이를 위해 2018년 12월 대학 신입생 300명을 대상으로 설문조사를 실시하였다. 연구결과 수업 선택기준시 수업 시간과 비공식적 정보를 중요하게 인식하는 것으로 나타났다. 그리고 좋은 수업에 대한 요인 중 교수의 태도, 수업결과, 내용 전달이 가장 높은 것으로 나타났다. 이와 같은 연구결과를 바탕으로 시사점은 다음과 같다. 첫째, 대학 신입생의 수업 선택 지원을 위한 충분한 정보 제공과 소통의 창구 마련이 필요하다. 둘째, 대학의 학사 운영을 유연화 하고 강의 모델의 다양화 노력이 필요하다. 셋째, 강의계획, 강의평가, 교육과정 등의 공식적 정보 전달을 확대해야 한다. 넷째, 학생들이 주로 이용하는 서비스나 채널을 대학이 적극 이용하여 정확한 정보를 전달해야 한다. 다섯째, 대학 신입생에게 좋은 수업의 사례를 적극 공유하고 알릴 필요가 있다. 여섯째, 교수는 학생들과 보다 친밀한 관계를 유지할 필요가 있다. 일곱째, 효과적인 수업 내용 전달을 위한 교원의 전문성 강화가 지속적으로 이루어져야 한다. 여덟째, 신입생을 위한 수업을 개설, 운영할 때 사회적 수요나 학생들의 요구를 적극 반영해야 한다. 마지막으로, 수업에서 이루어지는 학생 활동에 대한 교육과 안내가 이루어져야 한다. 본 연구는 대학 신입생이 어떤 기준과 정보를 토대로 수업을 선택하는지 파악하고 좋은 수업에 대한 인식을 살펴봄으로서 대학 신입생의 대학 적응과 대학 교육의 질적 발전을 모색할 수 있는 발판을 마련하였다.The purpose of this study was to examine how university freshman perceive the choice of classes and what constitute good classes. The study examined perceptions of factors considered by college freshmen, the information they refer to, and the characteristics of good classes. To do so, we surveyed 300 freshmen students from December 2018. The results showed that class time and informal information were recognized as important among class selection criteria. Further, the professor’s attitude, lesson results, and subject delivery were highly recognized as factors of a good class. Based on these findings, the implications are as follows. First, it is necessary to provide adequate information and communication channels to support freshmen’s class choices. Second, it is necessary to operate educational management flexibly and diversify the teaching model. Third, formal information delivery such as lecture plans, lecture evaluations, and curricula should be expanded. Fourth, to deliver accurate information, the university should actively employ the main services or channels used by students. Fifth, it is necessary to actively inform college freshmen by sharing examples of good classes. Sixth, professors should maintain closer relationships with their students. Seventh, professionalism should be continuously strengthened to facilitate effective delivery of class contents. Eighth, when opening and operating classes for first-year students, universities should actively reflect social demands and students’ needs. Finally, education and guidance on student activities in class should be provided. This study has implications for the setting of new standards for university students, based on the above-mentioned criteria and information looking at the perception of what constitute good classes.
Pedagogical Trends in Teaching Humanities in the Formation of Future Elementary School Teachers

Introduction: This study presents the results of an investigation, which addressed trends in teaching humanities and ethics when training future elementary school teachers participating in a distance and virtual education program. Humanistic education is understood to be a way to develop human sensitivity towards cultural and social diversity in order to understand the world. Objective: Its purpose, among others, is to foster the development of humanism among students. Current problems focus on the most basic and primitive behaviors of the human being, such as their ability to annihilate, reject, exclude, isolate, dominate and control others. Universities have interpreted these ideological conditions as the educational processes that fall within commercial and industrialized educational frameworks. Method: This study was then conducted as an ethnography, which utilized videos as a non-participant technique for observation and record of virtual activity, collected and analyzed through ATLAS-Ti. Results: The results show that the most common pedagogical trends emerge from teacher-student interactions, which are integrated through socio-constructive, cognitive and behavioral processes. Discussion y Conclusion: In particular, such experiences as the need to persist teaching values, ethical principles, and the teacher’s role in training and transmitting humanistic and ethical knowledge can be shared with the community.
Prototype of Weeding Robot Using Portable Terminal

The purpose of this study is to develop a remote controlled weeding robot using a portable terminal. The developed weeding robots are a wheel type weeding robot and two spherical type weeding robots. The wheel type weeding robot has four-wheel drive controlling device which improves the traveling stability for paddy field. The spherical type weeding robots roll on the surface of water. In this paper, we carried out the experiments using Wi-Fi or Bluetooth communication method from the portable terminal. The effectiveness of the developed weeding robots are verified by the experiments.
Research on Statistical Word Clustering Methods

Statistic language model based on classes of words is an effective method to solve sparse-data problems. Conventional statistical word clustering models usually base on greedy principle. The common Metric for evaluating a clustering model is the perplexity of the corpus or likelihood function. Conventional statistical word clustering algorithms often converge to a local optimum, so global optimum is not guaranteed, and initial choices can influence the final clustering result. Pointing to these problems, this paper presents a definition of word similarity by utilizing mutual information, and gives the definition of word set similarity based on word similarity, and puts forward a bottom-up hierarchical word clustering algorithm which can get global optimum. Experimental results show that the word clustering algorithm is of high executing speed and has good clustering performance. We then interpolated the class-based models with the word-based models and found that it mitigates the remaining sparse-data problems.
Fine-grained Classification Algorithm based on Meta-learning

As a challenging computer vision task, fine-grained visual categorization (FGVC) has received more and more attention. Most fine-grained classification algorithms are data-hungry and time-consuming, on the contrary, humans have the ability to recognize different species with only a small number of samples. For example, a baby can identity different kinds of dogs with only few images. Therefore, in this paper, a fined-grained classification model which is based on meta learning is proposed, and it can be used for more accurate fined-grained classification with only a small number of data. Meta learning uses meta-learners to learn relative tasks so that they can handle with new tasks faster and better when facing new tasks. Specifically, our model uses a task learner to learn various tasks and update the parameters of meta learner at the same time. When facing a new task, meta learner can learn and adapt to new tasks more quickly. The key novelty of our model is an attention module which can enlarge the local area response of image. This model has achieved great results on both CUB-200-2011 and Stanford dogs, and the accuracy of this model has achieved 81.74% with only one image of CUB-200-2011.
An e-cash Scheme with Multiple Denominations and Transferability

E-commerce has developed rapidly in recent years and online transactions and digital services have become popular. However, existing trading systems such as ATMs, credit cards, Paypal, and prepaid systems are potentially unsecure and users’ privacy is not sufficiently protected. Because user information can be easily associated with consumption history in the aforementioned trading systems, consumers’ spending habits and interests are retrieved and analyzed by individuals or businesses or people whose intentions may be suspect and without the consent of the consumers themselves. Therefore, the development of e-cash is critical for future e-commerce. In an e-cash system, blind signatures ensure consumer anonymity and prevent e-cash from being linked to its user. However, the anonymous nature of e-cash makes tracing criminal behavior difficult, so e-cash systems with anonymity revocability have been proposed. To thus expand the applicability of e-cash systems, alternatives have become the goal of subsequent studies (e.g., offline transactions, transferability, and divisible e-cash). This study proposed an e-cash system with multiple denominations that enables e-merchants to give customers change when in an offline environment. The proposed system results in convenient transactions regardless of transaction amount and reduces the amount of e-cash users need to deposit in advance.
Efficient Reconfigurable Architecture for Enhancing Medical Images

The enhancement technique plays a significant role in image process to bring out the knowledge that exists among low dynamic variation of the grey level image. To boost the standard of a picture, it is needed to perform the operations like distinction improvement and reduction or removal of noise. This paper proposes an efficient reconfigurable architecture for enhancement of medical image. The input image is preprocessed by Gaussian filter to remove high frequency edges. The novel architecture for Histogram Generation and Equalization is developed and applied to post processing block to accurately enhance the images. The performance of the architecture is implemented and tested on Digilent ATLYS (Spartan-6, XC6SLX45-3CSG324) FPGA board. It is observed that the areas in terms of slice registers are minimum with high PSNR values.
Spelling Correction To Improve Classification Of Technical Error Reports

This master’s thesis project undertook the investigation of whether spelling correction would improve the performance of the classification of reports. The idea is to use different approaches of sp ...
Speech Recognition and System Controlling using Hindi Language

This paper presents a speech recognition techniques, especially for Hindi origin speakers. It also controls the system applications through the input voice. This paper proposes a method for Hindi speech recognizer which can be extended, its domain from Hindi speech recognition to multiple Indian origin speech recognition like Bangla and Marathi. For this purpose, we used CMU sphinx 4 as the base model of voice recognition and built it over an acoustic language model and language-specific phoneme dictionary. This paper exhibits an efficient way of MLVR (Multi-Linguistic Voice Recognizer) with a considerably low error tolerance.
Error Protected Data Tranmissionon on the Recursive Encryption Base

In this paper proposed to apply the method of recursive data encryption in the transmission systems of flying systems. Defined system properties that justify the effectiveness of the proposed method. The non-inversion method is analyzed and the application of the message encoding method in recursive inverted transmission channels is proposed. Structural diagrams of information encoding devices have been developed and examples of fields of formed code elements are given.
Computer-Aided Design System of Navigation Equipment Test Table

The paper proposes an intelligent design methodology for the automated design and production of the navigation equipment test table, providing optimization functions in the design process and autonomous search for design solution parameters in the system design specifications database. The core of the intelligent part is the multi-agent coordination strategy based on multi-layer neural network. To solve the test table design task of the complex system-level objectives it is divided into subsystems. All test table subsystems are considered as separate set of design variables that must be addressed by virtual design teams called agents. Agents search design solutions and cooperate to meet design task of the complete test table system.
LegalLanguage: A Domain-Specific Language for Legal Contexts

Nowadays legal ontologies have been used in the legal domain, however, being poorly explored in legislative and production processes. This paper analyses the adoption of legal ontologies as a tool to support these processes, in particular, related to activities span from the submission of bills and their subsequent authoring and ratification. This paper introduces the state of the art of legal (or normative) ontologies; and also discusses some application examples. The analysis of this state of the art allows us to identify some problems, namely regarding the activities involving the authoring and validation of laws that tend to be very human-intensive and error-prone. As a consequence of this analysis, we introduce the LegalLanguage, a language particularly suitable for the authoring and specification of law(s) in a more rigorous and systematic way, that would allow to keep track different types of intra and inter-laws relationships (e.g., structural, order or temporal relationships between articles or even between laws). Finally, a simple illustrative example is used and shows the importance of a language like LegalLanguage in the production of normative documents.
A Block Design for Introductory Functional Programming in Haskell

The following topics are dealt with: computer science education; computer aided instruction; programming environments; educational courses; programming; teaching; object-oriented programming; programming languages; handicapped aids; serious games (computing).