Reviewerly/exHarmony huggingface dataset:
A Suggestion to Activate Middle School Chinese Online Classes : The Current State from a Survey

On April 2020, all of the schools in South Korea has started the new semester online owing to the outbreak of COVID-19. Focusing on the Chinese language online classes, this study has surveyed 273 students attending middle school in Suwon city and 30 teachers who teach Chinese language in middle school. In specific, the survey has looked into the satisfactions and demands regarding the current online classes and also investigated whether adopting Robert M. Gagne’s 9 events of instruction as an online educational model is suitable. According to the survey, this study has come to the conclusion that Gagne’s 9 events of instuction theory is an efficient model to for the students to be motivated and to do self-directed learning online. While Robert M. Gagne’s 9 events of instruction may be one of them, constant researches are required to find out more efficient and effective online educational models.
FPGA Acceleration of Tree-based Learning Algorithms

Machine learning classifiers provide many promising solutions for data classification in different disciplines.However, data classification at run time is still a very challenging task for real-time applications.Acceleration of machine-learning hardware solutions is needed to meet the requirements of real-time applications.This paper proposes a new implementation of a machine learning classifier on Field Programmable Gate Arrays (FPGA).The proposed implementation utilizes the MicroBlaze soft-core processor on FPGA and uses the Advanced eXtensible Interface (AXI) bus to integrate the MicroBlaze with hardware peripherals.Experimental results shows that hardware-software co-design is a promising solution as it saves silicon area and provides a flexible configuration of decision tree algorithms at run time.
A comparison of learning the digestion process with comic strips and endoscopic video: the effects on student’s achievement and its contributing factors

Comic strips and videos are two popular media used in today's science education.The objectives of this study were to examine the learning outcomes and to explore the contributing factors that drive students to achieve the learning outcomes.A quasi-experimental design was employed.The participants were university students in the science education major from batch 2015 and 2016.Batch 2015 was assigned as an endoscopic video class while batch 2016 was assigned as comic strips class.The results showed that there was no significant difference in students learning outcomes and its highest contributing factor for the learning outcomes was personal motivation followed by the lecturer and the media.We conclude that both media were able to facilitate students learning outcomes but motivating students' personal motives was also essential due to the nature of university students as an independent learner.
Towards Reference-Aware FrameNet Annotation

In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events. We show that frames made relevant by the knowledge of the real-world event can be captured by complementing standard lexicon-driven FrameNet annotations with frame annotations derived through pragmatic inference. We propose a two-layered annotation scheme with a ‘strict’ FrameNet-compatible lexical layer and a ‘loose’ layer capturing frames that are inferred from referential data.
Adversarial Generation of Handwritten Text Images Conditioned on Sequences

State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images. We obtain realistic images on both French and Arabic datasets, and we show that integrating these synthetic images into the existing training data of a text recognition system can slightly enhance its performance.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games

We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for irrelevant token removal in observation text for improved generalization. Our method first trains a base model using Q-learning, which typically overfits the training games. The base model's action token distribution is used to perform observation pruning that removes irrelevant tokens. A second bootstrapped model is then retrained on the pruned observation text. Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.
Abstract Concept Instantiation with Context Relevance Measurement

In different contexts, one abstract concept (e.g., fruit) may be mapped into different concrete instance sets, which is called abstract concept instantiation. It has been widely applied in many applications, such as web search, intelligent recommendation, etc. However, in most abstract concept instantiation models have the following problems: (1) the neglect of incorrect label and label incompleteness in the category structure on which instance selection relies; (2) the subjective design of instance profile for calculating the relevance between instance and contextual constraint. The above problems lead to false prediction in terms of abstract concept instantiation. To tackle these problems, we proposed a novel model to instantiate the abstract concept. Firstly, to alleviate the incorrect label and remedy label incompleteness in the category structure, an improved random-walk algorithm is proposed, called InstanceRank, which not only utilize the category information, but it also exploits the association information to infer the right instances of an abstract concept. Secondly, for better measuring the relevance between instances and contextual constraint, we learn the proper instance profile from different granularity ones. They are designed based on the surrounding text of the instance. Finally, noise reduction and instance filtering are introduced to further enhance the model performance. Experiments on Chinese food abstract concept set show that the proposed model can effectively reduce false positive and false negative of instantiation results.
Adanmış ve Gömülü GPU Sistemlerinin Kapsamlı Performans Karşılaştırması

General purpose usage of graphics processing units (GPGPU) is becoming increasingly important as graphics processing units (GPUs) get more powerful and their widespread usage in performance-oriented computing.GPGPUs are mainstream performance hardware in workstation and cluster environments and their behavior in such setups are highly analyzed.Recently, NVIDIA, the leader hardware and software vendor in GPGPU computing, started to produce more energy efficient embedded GPGPU systems, Jetson series GPUs, to make GPGPU computing more applicable in domains where energy and space are limited.Although, the architecture of the GPUs in Jetson systems is the same as the traditional dedicated desktop graphic cards, the interaction between the GPU and the other components of the system such as main memory, central processing unit (CPU), and hard disk, is a lot different than traditional desktop solutions.To fully understand the capabilities of the Jetson series embedded solutions, in this paper we run several applications from many different domains and compare the performance characteristics of these applications on both embedded and dedicated desktop GPUs.After analyzing the collected data, we have identified certain application domains and program behaviors that Jetson series can deliver performance comparable to dedicated GPU performance.
Accelerated reactive transport simulations in heterogeneous porous medium using Reaktoro and Firedrake.

This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (2020) when applied to different reactive transport problems in heterogeneous porous media. ODML was devised to accelerate the computationally expensive geochemical reaction calculations in reactive transport simulations. We demonstrate that the ODML algorithm speeds up these calculations by one to three orders of magnitude. Such acceleration, in turn, significantly accelerates the entire reactive transport simulation. The numerical experiments are performed by implementing the coupling of two open-source software packages: Reaktoro (Leal, 2015) and Firedrake (Rathgeber et al., 2016).
Dynamic Directional NxN Chart Based Text Based Substitution Cipher

The need for simple secure substitution encryption algorithms has risen due to complexities derived from current encryption algorithms, which over-complicate the process through many sub-processes and consume more space than necessary. This also creates the need for a secure string encryption approach that can also be applied using a simple pen and paper to encrypt and decrypt messages between each other. In this paper we present a technique based on a Non-Deterministic directional NxN chart-based encryption cipher as a solution to this problem. This will be achieved by using a 2-dimensional N by N chart as the key while using a dynamically selected directional approach to generate both the cipher and plain-text.
The Effect of Education Earthquakes and Tsunami Preparadness Emergency Planning

Background: Bantul regency.is one of the areas in which there is the danger of earthquakes and tsunamis. To minimize the impact disasters need to have knowledge on communities including elementary school students who are part of communities that are vulnerable to disasters. Objective: The purpose of this study was to understand the effects of earthquake and tsunami education on elementary school emergency planning Jigudan Pandak Bantul Yogyakarta. Research Methods: Quantitative precision using pre experimental methods. Approach used in one group pretest-posttest design. The population of this study was 46 students in 5 of grade of 10-12 years old. Sample taking used the total sample. The instrument used is a questionnaire. The statistical test for Wilcoxon. Emergency planning research before they gave education to earthquake and tsunami disasters total 15 responders (32.6%) ready, 22 reponders (47.8%) almost ready, 9 responders (19.6%) less ready. Research Results: Emergency planning after giving them earthquake and tsunami aducation total 35 responders (76.1%) completely ready, 11 responders (23.9%) ready. Statistical test using the Wilcoxon score (P: 0,00) was smaller than or equal to degrees of prospering α: 0.05 (p : 0,000 &lt; 0,05 ). Conclusion: There was a significant impact on the readiness of emergency planning students before and after being given the education of earthquake and tsunami disasters. Keywords: Education Disaster Earthquake, Emergency Planning, Tsunami
Effects of Voice-Based Artificial Intelligence (AI) in Customer Service: Evidence from a Natural Experiment

Voice-based artificial intelligence (AI) systems have been deployed gradually to replace traditional interactive voice response (IVR) systems in call center customer service, but little evidence exists on how the implementation of AI systems impacts customer behavior, as well as AI systemsâ€™ effects on call center customer service performance. Leveraging the proprietary data from a natural field experiment, we examine how the introduction of voice-based AI affects call length, customersâ€™ demand for human service, and customer complaints in the call center customer service of a large telecommunication service firm. We find that the implementation of the AI system significantly increases call length and decreases customer complaints. Although the AI-based service system presumably reduces usersâ€™ efforts to transfer to human agents, we do not find any significant increase in customersâ€™ demand for human service. Furthermore, our results show interesting heterogeneity in the effectiveness of the AI-based service system. For simple service requests, the AI-based service system reduces customer complaints for both experienced and inexperienced customers. For relatively complex quests, customers learn from prior experience of interacting with the AI system, and this learning effect leads to fewer complaints. Moreover, the AI-based system exerts a significantly larger effect on reducing customer complaints for older and female customers, as well as for customers who are experienced in using the IVR system. Finally, in examining details in customer-AI conversations, we find that speech-recognition failures in customer-AI interactions result in an increase in customersâ€™ demand for human service and customer complaints.
Adaptation of the Theoretical Approaches to Control the Process of Training Crews of MSVs on the Technical Training Facilities with regard to Lunar Expeditions.

Further development of national manned cosmonautics stipulates designing and building a new manned transport spacecraft with regard to lunar and deep space missions. Accordingly, the elaboration of the programs of training cosmonauts on the simulators of lunar manned vehicles and habitable constructions (manned transport spacecraft, lunar takeoff-and-landing complex, lunar modules, stations, and bases) under construction should be scientifically reasoned and relied on a store of experience in training crews of national MSVs.
Low Complexity Illumination-Invariant Motion Vector Detection Based on Logarithmic Edge Detection and Edge Difference

This paper describes a low complexity illumination-invariant motion vector detection algorithm based on logarithmic edge detection and edge difference without periodic threshold adjustment. A logarithmic edge detector is employed to achieve accurate object movement over a wide illumination range. The threshold for edge detection is determined by one-time background logarithmic gradient extraction. Finally, the difference of the logarithmic edge of 3 consecutive frames is employed in a frame difference-based motion template model to obtain the motion vector. Experimental results show that the proposed algorithm achieves a motion vector detection accuracy of ~90% over an illumination level change of 80%.
Design of an automatic optical system to measure anthropometric hand parameters

Abstract Personalized medicine is an effective tool to improve the quality of rehabilitation and treatment for patients with disabilities. This study deals with the development of a low-cost hand scanner for the acquisition of anthropometric measures. The data acquired by the scanner is used, thanks to the developed procedure, to tailor the dimensions of a hand exoskeleton. The exoskeleton is used for assistive and rehabilitation purposes.
The Variational Deficiency Bottleneck

We introduce a bottleneck method for learning data representations based on information deficiency, rather than the more traditional information sufficiency. A variational upper bound allows us to implement this method efficiently. The bound itself is bounded above by the variational information bottleneck objective, and the two methods coincide in the regime of single-shot Monte Carlo approximations. The notion of deficiency provides a principled way of approximating complicated channels by relatively simpler ones. We show that the deficiency of one channel with respect to another has an operational interpretation in terms of the optimal risk gap of decision problems, capturing classification as a special case. Experiments demonstrate that the deficiency bottleneck can provide advantages in terms of minimal sufficiency as measured by information bottleneck curves, while retaining robust test performance in classification tasks.
Model of student in the reports of children finishing the third grade of primary school

The article is a report of research aimed at identifying and describing the educational functions of descriptive assessment, especially in the field of supporting the role of the student as the "creator of actions", the role of a colleague and the role of a person. This was related to an attempt to reach the role model of a student, colleague and person adopted by the teacher, because according to the perspective of symbolic interactionism, the models of perceiving the child in these roles have an impact on the teacher's educational activities. Discourse analysis was used as the main research method. Understanding discourse as a tool for analysing social space allowed for the interpretation of descriptive assessment through a specifically understood language and allowed to look at the assessment as a way of the method of communication between the teacher and the student. The research technique used was the analysis of documents, i.e. the analysis of classification descriptive assessment, which were treated as tools for talking to children and parents. The results of the research lead to the conclusion that descriptive assessment does not have to be a disciplinary tool for the student, a form of control or authority over the student, and the student does not have to be perceived by the teacher as a "performer of activities". Descriptive assessment may become a helpful diagnostic tool in education based on constructivist ideas if teachers are original and pay attention to such features of the student's role that help to develop reflection, independence, creative thinking, intrinsic motivation. Such teachers are able to combine the expectations directed to the "student" with the expectations directed to the "person" and "colleague". In the descriptive assessments they construct, the model of the student as the "creator of actions" is clearly distinguished. For them, the assessment is a helpful work tool, and gives students the opportunity to enter the reflective learning process.
Artificially Intelligent Chatbots to Resolve Manageable and Frequently Asked Hardware/Software Queries

S-BOT, an acronym for Service BOT, is meant to provide services to end users in a multinational company.It serves the purposes of all the employees who might be in the need of technical enhancements without any fuss.This functionality provides redirection of service requests to the vendors, who will thereby be able to process it appropriately.All the tangible requests are accepted by the BOT and carried forward to the task manager.It is a user friendly application, in a messenger-like service that helps you access task manager and his services.After acquiring the services of the BOT, a constructive feedback is given by the user that helps the administrators expand their technological advancements.S-BOT is a well trained, advanced BOT that has assisted an MNC to empower its customer service, by providing an advanced and adequate version of help to its employees.This highly technical BOT will serve as the baseline and contribute and enlighten the industry about the existence of BOTs and therefore machine learning.Hence, S-BOT paves the way to machine learning and its applications in various fields of the real world.
THE APPLICATION OF « NEMO » ARTIFICIAL INTELEGENCE IN ARABIC LANGUAGE LEARNING IN THE 4.0 REVOLUTION ERA AT AL- KHALIFAH ISLAMIC BOARDING SCHOOL CIBUBUR

Abstract&#x0D; &#x0D; Android application-based learning media is an effective way, because the students motivation have increased in learning and practicing learning activities. Learning media which utilizes Android which emphasizes on several applications that are easily owned by all students and instructors, so that they are easy and affordable. Gagne and Briggs in the Book (Azhar, 2002) provide a limitation on the scope of the media including physical devices with the aim of conveying subject matter such as books, tape recorders, video recorders, films, slides (picture frames), photos, graphics, television and computer. Nemo is an android application that is designed as a medium of learning and language learning by mastering a number of useful words. In the android application, Nemo is like a simple pocket dictionary so that it helps in improving the language level through various learning materials. This study examines the effectiveness of the use of Android-based Arabic Nemo teaching materials for students at Al- Khalifah Islamic boarding school Cibubur. Beginner level students at Al- Khalifah Islamic boarding school Cibubur in learning Arabic. Many relevant studies prove that the teaching and learning activities using android made learning outcomes better than teaching and learning activities without using it. This study aims to test the effectiveness of instructional media on learning outcomes and whether it is more effective than teaching and learning activities that do not use instructional media in Arabic learning, the average value obtained indicates the benefits and effectiveness of using NEMO media on success in teaching and learning activities.
Ridge-axis and Off-axis volcanic massive sulphide, Cyprus-type, in the Mawat Ophiolites, Kurdistan-Iraq, Reconnaissance survey

Zagros orogenic belt is a part of the a-Himalayan orogenic system which streatches in continuity between the Turkish Taurus and the Iranian Zagros ophiolites, complementary with those in Balkans in Europe and to Pakistan and Tibet in Asia. Tectonic analysis of the Mawat ophiolit complex registered system of rupture elements of variable intensity and strike directions, marking the thrust-fault boundaries of the Mawat ophiolite complex. The first order E-W elements delineate the boundaries among three major tectono-stratigraphic units: i) Northern Mawat Group (NMG), (ii) Median Intrusive complex (MIC), (iii) Southern Waraz Group (SWG). SWG has fully developed sheeted dyke complex, while its existence in the NMG and MIC is ambiguous. In addition, NMG and MIC have significantly different mélange units. Sulphide mineralization in the Mawat ophiolite is a part of the sub-sea floor alteration process. The mineralization events in the Waraz meta-basalts are diachronous and not formed from the single fluid and under the same P/T conditions. Its polyphase character could be constrained, at least, into two major episodes: 1) The low temperature hydrothermal (LTH) and 2) The high temperature hydrothermal episode (HTH). They differ by contrasting alteration assemblages. The HTH is related to intrusion of plagiogranites which play active role in the ore forming process. It implies a new regional magmatic impulse as a source of heat and energy for hydrothermal circulation. While the low temperature episode (LTH) is related to magmatism along the ridge-axis, HTH is an off-axis event. The major VMS deposit prospects and Cu-ore showings are situated within the „Shear Zone“, gently arcuated tecto-magmatic belt trending NW-SE, along the western side of the Median Intrusive complex from Mawat village toward the Waraz area. It encountered basic and acidic intrusions, diorites, quartz dolerites and plagiogranites, with pervasive epidotisation, as a dominant alteration process. Extrusive rocks in the Northern Mawat Group bear elements of boninitic magmatism. The Shear Zone ore occurences are parallel with the possible palaeo-spreading ridge-axis, but coincide with a newly established off-axis ridge, displaced temporally and spatially during supra-subduction initiation.