Reviewerly/exHarmony huggingface dataset:
Minat Siswa dalam Melanjutkan Study ke Perguruan Tinggi Ditinjau dari Motivasi Belajar dan Pendapatan Orang Tua pada Siswa Kelas XII Akuntansi SMK N 1 Banyudono Tahun Ajaran 2019/2020

The purpose of this study was to determine the effect of learning motivation and parents 'income on students' interest in continuing their study at university. This research is a quantitative research. The population in this study were 71 students of SMK Negeri 1 Banyudono in the academic year 2019/2020, samples taken were 51 students. The data collection technique used a questionnaire. The data analysis technique used multiple regression analysis, t test, F test, R2 test, relative contribution and effective contribution. The results of the regression analysis obtained Y = 1,097 + 0,822 X1 + 0,177 X2. The conclusion drawn is (1) learning motivation has a positive effect on students 'interest in continuing their study at university. (2) Parents' income has a positive effect on students 'interest in continuing their study at university. (3) Learning motivation and parents' income collectively -same has a positive effect on students' interest in continuing their study at university. Based on the simultaneous test (F) it is known that Fcount> Ftable (16.199 > 3.18) and a significant value Fcount of 0.000 <0.05 with a coefficient of determination (R2) of 0.403 indicating that the magnitude of the influence of learning motivation and parents 'income on students' interest in continuing study to higher education by 40.3%.
The study of role of mammography and fine needle aspiration cytology in the diagnosis of early breast carcinoma

Background: Epidemiologically breast cancer is the most common form of cancer among women. Prevalence rate of breast cancer is higher in urban women of middle age group. Early diagnosis of breast carcinoma is required for effective treatment else it may lead to mortality if advanced to stage 3 or 4. At present, there are different diagnosis modalities for early stage breast carcinoma. Currently triple diagnostic assessment including clinical, radiological and pathological examinations with 99% sensitivity is used in diagnosing all palpable breast lumps. The aim of the study was to determine the sensitivity and specificity of mammogram and Fine needle aspiration cytology (FNAC) in diagnosing breast cancer.Methods: A cross sectional study was conducted on total 80 female patients above 30 years of age with a palpable breast lump. Patients were sent for mammography, fine needle aspiration cytology and histopathological examinations. Results of mammography, FNAC and HPR were compared and statistically correlated.Results: Sensitivity of mammogram diagnosis was observed to be 92.857%, specificity as 86.842%, positive and negative predictive values to be 88.636% and 91.667% respectively. Therefore, current study findings revealed accuracy of mammogram to be 90.000%. Sensitivity of FNAC diagnosis was observed to be 92.857% and specificity to be 100%, positive and negative predictive values were observed to be 100% and 92.683% respectively. The study results revealed that FNAC diagnosis exhibited accuracy of 96.250%.Conclusions: Current investigation findings revealed that FNAC has more accuracy in predicting breast cancer than mammogram. Also, it was concluded that there is a significant association between FNAC and mammogram diagnosis.
Emergence of Digitized Gamification as an Educational Tool and the Implications on Digital Literacy and Equity

The phenomenon of gamification has developed into a widely used educational tool over the last sixty years. In that time gamification has evolved from serious games, used to educate military personnel and medical workers, to a tool used to teach a variety of disciplines. The proliferation of gamification is particularly prevalent due to the ease of access and production of games in a digital format. Whether being used in the classroom or being used in daily life as a non-traditional learner, the common man is inundated with information and games that teach. Some games intentionally teach skills, however, even those that aren’t geared towards teaching can teach peripheral skills and values that aren’t easily taught in the classroom. Unfortunately, it is difficult to determine which games and content are going to be positive and helpful to growth and development, and which will not be. One such skill that would be invaluable to teach would be digital literacy, especially in such a digitized world. Thus the question posed here is whether gamification will be a reliable tool to teach digital literacy due to its success being implemented in other disciplines.
Pernik-Pernik Metode dan Pendekatan dalam Penelitian Filologi

This article intends to examine the method and approach in philological research. It should be noted that philology is a difficult study, it is due to the return of the text to a form that is closer to the his original. Even though the text has existed for hundreds or even thousands of years ago, so that the text is prone to corruption, lakuna, interpolation, omission, ditography, haplography, transposition and others. Therefore it is necessary to held the research related to method and approach in philological research. This type of research is classified as qualitative research. The method used is descriptive analysis method. The findings obtained that is: (1) In general, the method of philological studies is divided into two types: a) The method of research on manuscript, b) The method of text criticism. The stages of the method of research on manuscript include: manuscript inventory, manuscript description, manuscript classification, manuscript comparison, manuscript lineage or stema and determination of the basic manuscript. As for related the method of text criticism there are two kinds: uniqus coder and multus coder. If there is only one manuscript, it is pursued in two ways: the diplomatic edition and the standard edition covered by the uniqus coder frame. Meanwhile, if the manuscript is more than one, then there are four methods, that is the intuitive, objective, combined and basic method which are covered in the multus coder frame. The next step is stema drafting and text reconstruction; (2) The approach in philological research according Ronald Tanaka includes a macro approach and a micro approach. Even though researchers use a macro approach, researchers still can’t ignore the micro approach, because both are complementary. With the combination of the two approaches, it is hoped that the maximum level of philological results can be obtained. Another approach that can be used is the linguistic discourse approach and the historical criticism approach. If the linguistic discourse approach is combined with the historical criticism approach, it will have advantages that are very helpful for researchers in the field of philology. One of its advantages, the ability to see the correspondence and parallels of the language used in a text with the language used at the time the text was written.
Channel state information acquisition method of internet of things based on deep learning

As one of the key technologies of Internet of things (IoT), the accurate and efficient acquisition of CSI is an important part to support the high quality communication of IOT system. However, the current IOT system is massive and complex, and the traditional CSI acquisition method is difficult to meet its efficient information communication. To solve this problem, this paper proposes an improved CSI acquisition method based on bidirectional convolutional long and short-term memory networks (Bidirectional Convolutional Long Short-Term Memory, Bi-ConvLSTM) and attention mechanism. This method unifies the Bi-ConvLSTM-Attention network module to improve the encoder and decoder of the CSI-Net network model. Among them, the Bi-ConvLSTM module is mainly used to learn the time correlation between channel matrices. The attention mechanism module is used to calculate the soft probability distribution, so that the Bi-ConvLSTM network has the function of automatic weighting, thereby further improving the characterization ability of characteristic information. In the last part of the thesis, the proposed method is simulated and verified. Experimental results show that the proposed method exhibits excellent network performance in both single-user and multi-user massive MIMO system scenarios. Compared with the comparison method, the bit error rate performance and system capacity are improved by about 0.3–6 dB and 1.3–3.7 bps/Hz in the multi-user complex scene.
THE COMPETENCE PROFILE OF THE EFL TEACHERS AT ISLAMIC BOARDING SCHOOL

This study investigates three main cases. (1) the competence profile of the EFL teachers according to Islamic Boarding School students. (2) the competence profile of the EFL teachers according to Islamic boarding school teachers. (3) the differences and similarities between students’ and teachers’ perceptions on the competence profile of the EFL teachers at the Islamic Boarding School. This study employed mixed-method research in terms of explanatory sequential design. This study was conducted at Pesantren IMMIM Putra Maros, in the academic year of 2019/2020. There were 21 twelfth grade students as the subject of this study by using purposive sampling to obtain the quantitative data, and for qualitative data in selecting six students and two teachers. A number of the subject were 5 teachers by used census sampling in the quantitative phase. The quantitative data were analyzed using descriptive statistics and using the model of Lodico, Spaulding, Voegtle to analyze the qualitative data. The result of this study showed that competent profile EFL teacher has pedagogic, personal, social, and professional competence such as have effective teaching method or strategy, know students’ need, humorous, neat appearance, non-discriminatory attitude, friendly, the ability to prepare learning materials, and have English skills, etc. Further, the result of two perceptions above almost has the same perception between teacher and students as said in teacher perception above except three important points. Among the differences are found in the use of electronic media in learning, teaching method and mastery of the material, and awareness of class cleanliness.
Digital Learning: Exploring the Virtual Personal Social Responsibility Activities

Personal social responsibility (PSR) is a primary responsibility ofany person (both ecological and social) towards the family, job, society, and climate. Social demand has led to PSR courses to be introduced at the university level. It provides learners with opportunities to adapt to the continuous changes required bysociety. However, due to the pandemic Covid-19, PSR projects are done virtually. The issue is whether this virtual PSR, as part of digital learning, serves the purpose of a conventional PSR.Purpose - The purpose of the study is to investigate students' satisfaction with virtual PSR.
Ethnolinguistic approach to university online classes in thefirst quarantine by COVID-19

The measures taken against the disease COVID-19 disease invoked an important challenge for university education, as in only two days back in March 2020 the education had to be arranged to continue remotely. In this time frame, a number of studies have emerged, the conclusions of which tend to oscillate between a) the focus on teachers' e-skills deficit and b) the feeling of achievement in the light of emergency. This work considers the theoretical assumption that electronic devices and the applications that give them meaning are the transmission chain of the neoliberal philosophy, which turns everything into merchandise, including education and its transfiguration into didactic platform. From the participant observation and the guide of the ethnolinguistic model SPEAKING, an exploratory approach will be opened on the main communicative aspects raised during the strict quarantine in spring 2020, while, beyond some exceptional measures, will unravel the main proposed milestones in order to build a critical digital competence, closer to the participants of the didactic action and less focused on the device and its seemingly voluntary servitudes.
A Practical Solution to Handling Randomness and Imperfect Information in Monte Carlo Tree Search

This paper provides practical guidelines for developing strong AI agents based on the Monte Carlo Tree Search algorithm in a game with imperfect information and/or randomness.These guidelines are backed up by series of experiments carried out in the very popular game -Hearthstone.Despite the focus on Hearthstone, the paper is written with reusability and universal applications in mind.For MCTS algorithm, we introduced a few novel ideas such as complete elimination of the so-called nature moves, separation of decision and simulation states as well as a multi-layered transposition table.These have helped to create a strong Hearthstone agent.
Shallow SqueezeNext: Real Time Deployment on Bluebox2.0 with 272KB Model Size

The significant challenges for deploying CNNs/DNNs on ADAS are limited computation and memory resources with very limited efficiency. Design space exploration of CNNs or DNNS, training and testing DNN from scratch, hyper parameter tuning, implementation with different optimizers contributed towards the efficiency and performance improvement of the Shallow SqueezeNext architecture. It is also computationally efficient, inexpensive and requires minimum memory resources. It achieves better model size and speed in comparison to other counterparts such as AlexNet, VGGnet, SqueezeNet, and SqueezeNext, trained and tested from scratch on datasets such as CIFAR-10 and CIFAR-100. It can achieve the least model size of 272KB with a model accuracy of 82%, a model speed of 9 seconds per epoch, and tested on the CIFAR-10 dataset. It achieved the best accuracy of 91.41%, best model size of 0.272 MB, and best model speed of 4 seconds per epoch. Memory resources are of high importance when it comes down to real time system or platforms because usually the memory is quite limited. To verify that the Shallow SqueezeNext can be successfully deployed on a real time platform, bluebox2.0 by NXP was used. Bluebox2.0 deployment of Shallow SqueezeNext architecture achieved a model accuracy of 90.50%, 8.72MB model size and 22 seconds per epoch model speed. There is another version of the Shallow SqueezeNext which performed better that attained a model size of 0.5MB with model accuracy of 87.30% and 11 seconds per epoch model speed trained and tested from scratch on CIFAR-10 dataset.
SISTEM PENDETEKSI BUAH LADA BERBASIS CONVOLUTIONAL NEURAL NETWORK (CNN)

Pepper trees grow vines up to a height of 4 meters supported by other trees or using a pole called an arbor. The current problem is that during the pepper harvesting process, farmers generally pick by hand. In 2020, D3 students made a pepper picker design tool using a remote to control its movement, this tool cannot move automatically, from this problem a convolutional neural network (CNN) based pepper fruit detection system was created. This system uses the Convolutional Neural Network (CNN) method. CNN implementation uses Tensorflow tools with Python programming language. The number of datasets is 100 images of pepper and 100 images of non pepper. Based on the test results, the prediction precision level is 95%, accuracy is 89%, and recall is 90%, so it can be concluded that the detection system using a webcam can predict pepper fruit well.
The Analysis of Students’ Difficulties in Speaking English of Eight Grade Studentsat SMPN 1 Majasari

This research aims to describe and analyse the factors of students' difficulties in speaking English language faced by eighth grade students of SMPN 1 Majasari. This research uses a qualitative approach and descriptive analysis technique. The population in this study were 82 students in the eighth grade; which were divided into three classes (VIII A consisted of 29 students, VIII B consisted of 30 students, and VIII C consisted of 23 students). Therefore, the sample of the population in this research were taken by a simple random sampling technique; six students (two students each class) to answer the interviews, and 33 students (11 students each class) to answer the questionnaires. In collecting data, the instruments used in this study were tests in the form of multiple choice questions and recorded interviews. While to analyze the data, qualitative data analysis procedures were administered through data display, data reduction, and conclusion drawing. The results showed that the factors of students’ difficulty to speak English in class VIII SMPN 1 Majasari were; affective factors (low motivation & anxiety), listening ability, inhibition, focus on grammar, social environment and mother-tongue use. It is recommended for the English teachers to use some strategies to help the students overcome their difficulties in speaking.
The Role of Self-Education in Preservation and Development of Human Capital in the Republic of Karelia: Socio-Cultural Aspect

The problem of preserving human capital in the context of globalization is due to processes in the economic, social and cultural spheres. The search for a solution to this problem in the field of humanities is associated with aspects of personality self-development. Among the most important components of personality self-development, the majority of researchers in the humanities note self-education as a motivator for labor and social activity. Therefore, the identification of socio-cultural factors affecting the level and need for self-education requires a comprehensive interdisciplinary approach with regional specifics taken into account. Objective is to identify the factors influencing the level of self-education of residents in the modern economic and socio-cultural conditions of the Republic of Karelia. The material for cultural analysis was the results of field studies obtained during a scientific expedition to the Karelian Arctic, northern and southern border regions of Karelia. As a research methodology, an integrated cultural approach was used. Among the effective methods were focus groups on thematic cases, standard and non-standard questionnaires, interviews, polls, factor analysis, statistical and normative methods. For the first time in scientific practice possible sources, channels and preferences of the population of Karelia in self-educational activities have been researched. As a result, it was concluded that the combination of economic indicators is the determining socio-cultural factor for self-educational activity of the residents of the investigated areas and geographical indicator, which allows the inhabitants of Karelia to realize their spiritual and material needs at a high level and to preserve human capital as the main value of society in the unstable situation of a globalizing world.
Survey on Recommender System using Hybrid Collaborative Filtering Techniques in Online Social Network

The Recommender system [RS] has now become an inseparable feature of Electronic Commerce (E-Commerce) and online social networking websites. It gives recommendations to the users of social networking thus helping them to connect to their social contacts and buyers (technically call users as buyers on e-commerce sites) thus helping them buying certain items of their interests. RS helps in increasing the market of the commercials thus encouraging other commercial industries to adapt the recommender system. It provides recommendations of the items to the users using certain filtering techniques. The paper provides a survey of the techniques and approaches used in the RS and reviews it using collaborative filtering, and similarity measures, furthermore the performance metrics that are used by the collaborative recommender systems to find the similarity among the users or items and to evaluate the performance of the RS are also discussed. The different approaches adopted by the different social networking and e-commercial sites and the problems and limitations of the existing recommender systems are also examined.
Planning Trajectories Using an Extended Sequential Linearization Algorithm

The development of autonomous driving and driver assistance systems that involve the lateral guidance of the car has the crucial challenge of trajectory calculation. Under the many possible solutions to this problem, the use of a model predictive controller (MPC) is one of the most popular. This article presents a method of calculating a trajectory using an iterative moving time horizon MPC combined with a forwardbackward velocity estimation to guess the initial solution. The discretization in the time domain provides the possibility to easily extend the method for the use with multiple vehicles while the linearization provides very high performance combined with low model complexity. The drawback of the iterative solution which usually would lead to a loss of safety due to possible errors by unfeasible problem formulation is compensated with the efficient guessing of an initial solution. To evaluate the performance of the method it is applied to the problem of global trajectory calculation for autonomous racecars. The presented approach proves to be superior to other methods that only use the geometry of the road while still being able to calculate a global trajectory for a whole circuit. The comparison with other methods like a minimum curvature optimization and the driver of the vehicle simulation program IPG Carmaker shows clear advantages both in terms of lap time and computation time while only using a very simple problem formulation. Also the results show a strong relationship between the driver, the trajectory and the driving performance which is especially relevant to emergency evasive maneuvers where the best use of the potential of the car is crucial for the driver’s safety.
Two-Stage Polishing Network for Camouflaged Object Detection

Due to the needs of medical and military fields, Camouflaged Object Detection (COD) becomes one of important branches of object detection. It has gradually gained people’s attention in recent years. How to correctly locate camouflaged objects accurately segment them are the main problems in this field. The COD task is far different from SOD because of the more complex background with similar colors and textures. Therefore it is more challenging. At present, there are still few existing methods, and lacks targeted method for the edge detection problem. In this paper, we propose a novel Two-Stage Polishing Network (TSPNet). This network consists of Front Feature Fusion Module (FFFM) and Polishing Module (PM). FFFM adopts Cross-modal Feature Aggregation and Global and Local Feature Aggregation to capture global context information and detail local information simultaneously. Meanwhile, PM uses edge truth map as supervision imformation to further study the object edge features. The experiments was conducted on three available datasets and the result shows that the proposed framework outperforms state-of-the-arts. Besides, TSPNet is compact with 50% model size saving than existed COD models.