Reviewerly/exHarmony huggingface dataset:
Effect of Group Learning with Primary Education Students

The purpose of this research is to examine group learning and its impact on achieving knowledge for primary school students.In this quasi-experimental study, data were collected from 221 students, with 111 students in an experimental group (who were influenced to learn through cooperative learning strategies) and 110 students in a control group (who learned under normal conditions).The total number of teachers was 50 from both groups.SPSS statistical package was used in this research with the following parameters: median, standard deviation, t-test, and correlation.Students achievement tests were conducted in both groups both prior to and following the experimental process in order to see the effects on students' achievements.The results of the analysis have shown that cooperative learning strategies have a significant effect on student achievement in learning.This has been proven through differences seen in the final success of students in the control and experimental groups, which showed significant statistical value in favor of the experimental group.This research also provides information on the effects of the implementation of group work in teaching, which resulted in higher student achievement based on engagement, and the educational and professional background of teachers as a potential factor in attracting students to active cooperative learning.Based on these results, implications point to the insufficient dedication of teachers during the group learning process.
The Value of Paraphrase for Knowledge Base Predicates

Paraphrase, i.e., differing textual realizations of the same meaning, has proven useful for many natural language processing (NLP) applications. Collecting paraphrase for predicates in knowledge bases (KBs) is the key to comprehend the RDF triples in KBs. Existing works have published some paraphrase datasets automatically extracted from large corpora, but have too many redundant pairs or don't cover enough predicates, which cannot be improved by computer only and need the help of human beings. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. Our dataset comprises 2284 distinct predicates in DBpedia and 31130 paraphrase pairs in total, the quality of which is a great leap over previous works. Then it is demonstrated that such good paraphrase dictionaries can do great help to natural language processing tasks such as question answering and language generation. We also publish our own dictionary for further research.
Offline Pashto OCR Using Machine Learning

For a long time, many techniques have been used for Optical Character Recognition in image processing. But Pashto optical character recognition is less developed area. Because of Pashto cursive nature, writing direction from right to left, and its character change its shape when it is placed at the start, middle and end makes the development of Pashto OCR a complex task. The proposed system takes an image with Pashto text input. After that process it and pass through its modules and generate an editable Pashto text file. More than 5000 images in dataset is use to classify the characters using Sequential Minimal Optimization classifier. An accuracy of our proposed system is approximately 92 Percent.
The Use of Cue Card in Cooperative Learning for Teaching Vocabulary

Vocabulary is the basic of English that is very important and difficult to master. Vocabulary will help students to master the four skills of language. Therefore, teacher have to make creative and interesting a way for teaching vocabulary.This study concern about how cue card and cooperative learning is used for teaching vocabulary. This study focused on theuse cue card as learning media and cooperative learning as learning method. The subject of this study was seven grade student of SMP N 41 Semarang. The aim of this study was to describe the implementation of cue card with cooperative learning in teaching vocabulary to the students in the classroom. This study was a qualitative case research. The observation, questionnaire, interview, and document analysis was used to collect the data. The questionnaire was given at the end of the study. The observation sheet used to monitor the students and teacher’s activities during the teaching learning process. The intrview was done at the end of the meeting.Based on the result, the researcher concluded that the use of cue card for teaching English vocabulary is interesting, effective, efficient, and arffordable. In addition, cooperative learning was helpfull to increase the student’s confidence.&#x0D; &#x0D; Keywords: Cue Card, Cooperative Learning, English Vocabulary
Material Cost Prediction for Jewelry Production Using Deep Learning Technique

Production cost management is a key factor to increase industrial competitiveness.The precious metals and the gemstones comprise 65% of the jewelry material cost.Managing raw material cost is a challenging task especially when the price highly fluctuates.In this article, deep learning models were proposed to predict the prices of main raw materials of jewelry which are silver, gold, and diamond.These models are designed for Thai jewelry manufactures which are mostly small businesses.Therefore, our models only consider historical price data.This is because small businesses usually do not have access to other relevant data, i.e. oil prices and other economic data.The proposed precious metal price model can provide prediction with RMSE of 0.00765 which is comparable to other models in literature while requires less data and offers a simpler model.Also, the proposed diamond price model can provide RMSE of 0.0181 which is 42.41% improvement from the model normally used by jewelry manufacturers.In addition to the raw material price prediction model, a quantization method of diamond 4C grade is proposed and validated statistically and visually.This quantization method could be used in a diamond analysis.
Embedded Information Literacy Instruction in the Mobile Environment: A Case Study

Information literacy instruction in various countries has expanded in a variety of ways. At universities, it has mainly taken the form of credit-based courses, lectures, and embedded instruction. While European and American universities carry out more embedded instruction, universities in China focus more on credit-based courses and lectures, leaving embedded instruction in an exploratory stage. This paper is a preliminary study of a research project that aims to investigate the department-based, embedded information literacy instruction among the libraries of 42 "Double-First Class" universities in China. Using East China Normal University Library as an example, this paper discusses the teaching mode, teaching philosophy, teaching design, teaching methods and contents in order to explore how embedded information literacy instruction can make use of the mobile Internet platform. The specific areas to be examined include teaching resources, enhancing classroom interactions, and after-class feed-pushing as part of teaching support. Finally, some recommendations are put forward for the extension and expansion of embedded information literacy instruction.
Solving Sequential Text Classification as Board-Game Playing

Sequential Text Classification (STC) aims to classify a sequence of text fragments (e.g., words in a sentence or sentences in a document) into a sequence of labels. In addition to the intra-fragment text contents, considering the inter-fragment context dependencies is also important for STC. Previous sequence labeling approaches largely generate a sequence of labels in left-to-right reading order. However, the need for context information in making decisions varies across different fragments and is not strictly organized in a left-to-right order. Therefore, it is appealing to label the fragments that need less consideration of context information first before labeling the fragments that need more. In this paper, we propose a novel model that labels a sequence of fragments in jumping order. Specifically, we devise a dedicated board-game to develop a correspondence between solving STC and board-game playing. By defining proper game rules and devising a game state evaluator in which context clues are injected, at each round, each player is effectively pushed to find the optimal move without position restrictions via considering the current game state, which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues. The final game-end state is viewed as the optimal label sequence. Extensive results on three representative datasets show that the proposed approach outperforms the state-of-the-art methods with statistical significance.
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification with K-Means Features

Multi-task learning (MTL) has received considerable attention, and numerous deep learning applications benefit from MTL with multiple objectives. However, constructing multiple related tasks is difficult, and sometimes only a single task is available for training in a dataset. To tackle this problem, we explored the idea of using unsupervised clustering to construct a variety of auxiliary tasks from unlabeled data or existing labeled data. We found that some of these newly constructed tasks could exhibit semantic meanings corresponding to certain human-specific attributes, but some were non-ideal. In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task. Experimental results across five image datasets demonstrated that the proposed method significantly outperformed existing single task learning, semi-supervised learning, and some data augmentation methods, including an improvement of more than 9% on the Omniglot dataset.
Modeling Probabilistic Commitments for Maintenance Is Inherently Harder than for Achievement

Most research on probabilistic commitments focuses on commitments to achieve enabling preconditions for other agents. Our work reveals that probabilistic commitments to instead maintain preconditions for others are surprisingly harder to use well than their achievement counterparts, despite strong semantic similarities. We isolate the key difference as being not in how the commitment provider is constrained, but rather in how the commitment recipient can locally use the commitment specification to approximately model the provider's effects on the preconditions of interest. Our theoretic analyses show that we can more tightly bound the potential suboptimality due to approximate modeling for achievement than for maintenance commitments. We empirically evaluate alternative approximate modeling strategies, confirming that probabilistic maintenance commitments are qualitatively more challenging for the recipient to model well, and indicating the need for more detailed specifications that can sacrifice some of the agents' autonomy.
Generalized and Sub-Optimal Bipartite Constraints for Conflict-Based Search

The main idea of conflict-based search (CBS), a popular, state-of-the-art algorithm for multi-agent pathfinding is to resolve conflicts between agents by systematically adding constraints to agents. Recently, CBS has been adapted for new domains and variants, including non-unit costs and continuous time settings. These adaptations require new types of constraints. This paper introduces a new automatic constraint generation technique called bipartite reduction (BR). BR converts the constraint generation step of CBS to a surrogate bipartite graph problem. The properties of BR guarantee completeness and optimality for CBS. Also, BR's properties may be relaxed to obtain suboptimal solutions. Empirical results show that BR yields significant speedups in 2k connected grids over the previous state-of-the-art for both optimal and suboptimal search.
Performance Evaluation of Task Scheduling using Hybrid Meta-heuristic in Heterogeneous Cloud Environment

Cloud computing is a ubiquitous platform that offers a wide range of online services to clients including but not limited to information and software over the Internet.It is an essential role of cloud computing to enable sharing of resources on-demand over the network including servers, applications, storage, services, and database to the endusers who are remotely connected to the network.Task scheduling is one of the significant function in the cloud computing environment which plays a vital role to sustain the performance of the system and improve its efficiency.Task scheduling is considered as an NP-complete problem in many contexts, however, the heterogeneity of resources in the cloud environment negatively influence on the job scheduling process.Furthermore, on the other side, the heuristic algorithms have satisfying performance but unable to achieve the desired level of the efficiency for optimizing the scheduling in a cloud environment .Thus, this paper aims at evaluating the effectiveness of the hybrid meta-heuristic that incorporate genetic algorithm along with DE algorithm (GA-DE) in terms of make-span.In addition, the paper also intends to enhance the performance of the task scheduling in the heterogeneous cloud environment exploiting the scientific workflows (Cybershake, Montage, and Epigenomics).The proposed algorithm (GA-DE) has been compared against three heuristic algorithms, namely: HEFT-Upward Rank, HEFT -Downward Rank, and HEFT -Level Rank.The simulation results prove that the proposed algorithm (GA-DE) outperforms the other existing algorithms in all cases in terms of make-span.
Voice assistance in 2019

Abstract The end of the calendar year always seems like a good time to pause for breath and reflect on what’s been happening over the last 12 months, and that’s as true in the world of commercial NLP as it is in any other domain. In particular, 2019 has been a busy year for voice assistance, thanks to the focus placed on this area by all the major technology players. So, we take this opportunity to review a number of key themes that have defined recent developments in the commercialization of voice technology.
A Neural Network Model for Efficient Antonymy-Synonymy Classification by Exploiting Co-occurrence Contexts and Word-Structure Patterns

Antonymy and synonymy are basic semantic relations between words.Automatically distinguishing between antonymy and synonymy is an important task in natural language processing.This task is hard because antonyms and synonyms tend to occur in highly similar contexts.Recent studies often focus on exploiting densevector representations of words to deal with this problem.In this paper, we present a study on antonymy-synonymy discrimination for the Vietnamese language.We propose a deep neural network model (DVASNet) that can utilize not only embedding representations of words but also co-occurrence contexts and specific patterns of Vietnamese word structure.Our experimental results showed that the proposed method achieved significant improvements in comparison with a number of state-of-the-art methods by 14% to 17% in terms of F1 score for both benchmark datasets.
Planning and Acting with Non-Deterministic Events: Navigating between Safe States

Automated Planning addresses the problem of finding a sequence of actions, a plan, transforming the environment from its initial state to some goal state. In real-world environments, exogenous events might occur and might modify the environment without agent's consent. Besides disrupting agent's plan, events might hinder agent's pursuit towards its goals and even cause damage (e.g. destroying the robot).In this paper, we leverage the notion of Safe States in dynamic environments under presence of non-deterministic exogenous events that might eventually cause dead-ends (e.g. “damage” the agent) if the agent is not careful while executing its plan. We introduce a technique for generating plans that constrains the number of consecutive “unsafe” actions in a plan and a technique for generating “robust” plans that effectively evade event effects. Combination of both approaches plans and executes robust plans between safe states. We empirically show that such an approach effectively navigates the agent towards its goals in spite of presence of dead-ends.
Multi-Speaker Video Dialog with Frame-Level Temporal Localization

To simulate human interaction in real life, dialog systems are introduced to generate a response to previous chat utterances. There have been several studies for two-speaker video dialogs in the form of question answering. However, more informative semantic cues might be exploited via a multi-rounds chatting or discussing about the video among multiple speakers. So multi-speakers video dialogs are more applicable in real life. Besides, speakers always chat about a sub-segment of the long video fragment for a period of time. Current video dialog systems require to be directly given the relevant video sub-segment which speakers are chatting about. However, it is always hard to accurately spot the corresponding video sub-segment in practical applications. In this paper, we introduce a novel task of Multi-Speaker Video Dialog with frame-level Temporal Localization (MSVD-TL) to make video dialog systems more applicable. Given a long video fragment and a set of chat history utterances, MSVD-TL targets to predict the following response and localize the relevant video sub-segment in frame level, simultaneously. We develop a new multi-task model with a response prediction module and a frame-level temporal localization module. Besides, we focus on the characteristic of the video dialog generation process and exploit the relation among the video fragment, the chat history, and the following response to refine their representations. We evaluate our approach for both the Multi-Speaker Video Dialog without frame-level temporal localization (MSVD w/o TL) task and the MSVD-TL task. The experimental results further demonstrate that MSVD-TL enhances the applicability of video dialog in real life.
Zero Shot Learning with the Isoperimetric Loss

We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting. We use a pre-trained deep neural network model as a visual representation of image data, a Word2Vec embedding of class labels, and linear maps between the visual and semantic embedding spaces. However, the spaces themselves are not linear, and we postulate the sample embedding to be populated by noisy samples near otherwise smooth manifolds. We exploit the graph structure defined by the sample points to regularize the estimates of the manifolds by inferring the graph connectivity using a generalization of the isoperimetric inequalities from Riemannian geometry to graphs. Surprisingly, this regularization alone, paired with the simplest baseline model, outperforms the state-of-the-art among fully automated methods in zero-shot learning benchmarks such as AwA and CUB. This improvement is achieved solely by learning the structure of the underlying spaces by imposing regularity.
Learning process: Multi-Agent Tutoring System

A multi-agent architecture has been developed for tutorial assignation scheduling. It has two main types of agents: the students and the teachers. These two are coordinated by an algorithm which assigns the classes in order of arrival. The architecture will provide the necessary tools to the students, so they get the maximum profit from the tutorials. Students and Lecturers can coordinate their tutorial meeting in an efficient way with the help of the multi-agent system.
A high throughput pass parallel block decoder architecture for JPEG 2000 that prevents stalling in the decoding process

The Block Decoder (BD) which is an indispensable component of the JPEG 2000 image compression standard has the highest computational complexity and determines the speed of the overall decoder system. This paper proposes a high throughput pass parallel BD architecture, which can decode more than one bit per clock cycle. In BD, the dependency between context generation and arithmetic decoding unit incorporates stalling and reduces the throughput of the decoding process. The proposed selective byte input and synchronous sample skipping techniques are used to prevent stalling in the decoding process. The proposed architecture achieves 86% more throughput with 50% increment in the hardware cost than that of the best available serial BD architecture. In comparison with the best available pass parallel architecture, throughput improves almost 8.2 times with 61% increment in the hardware cost. Incorporation of the speed up techniques in the design is the main reason for more hardware consumption. The Figure of Merit of the proposed design, which is the ratio of throughput and hardware cost, is more than that of the available BD architectures for typical code block (CB) size of 32 × 32. The ASIC implementation of the proposed design consumes 66 mW power at maximum operating frequency.
PENERAPAN MODEL PEMBELAJARAN INKUIRI UNTUK MENINGKATKAN KEMAMPUAN MENGANALISIS UNSUR INTRINSIK CERPEN PADA PESERTA DIDIK KELAS XI MIA 10 SMA NEGERI 7 DENPASAR TAHUN PEMBELAJARAN 2018/2019

The purpose of this research is to find out whether the application of inquiry learning models can improve the ability to analyze short story intrinsic elements and can improve students' responses. This study uses a class action research design (PTK), there are four important components in the PTK cycle, namely: (1) the planning stage, (2) the stage of action, (3) the stage of observation and, (4) the stage of reflection. This research was carried out in the odd semester of August to September 2018. The research subjects were students of class XI MIPA 10 in SMA Negeri 7 Denpasar in the academic year 2018/2019 with a total of 36 students. The object of this class action research is the ability to analyze short story intrinsic elements. The results of this study indicate the increased ability to analyze short story texts. The acquisition of the class average value at the initial reflection of 65.64 increased in the first cycle to 73.42 and in the second cycle it increased to 82.03. The increase in value from cycle I to cycle II occurs in 28 students or 77.78%. The percentage of success of students in learning to analyze the intrinsic elements of short stories reaches classical completeness 94.44% of the number of students get 70 and above. The application of the inquiry learning model in learning analyzes the intrinsic elements of the short story gets a positive response from students. The acquisition of the average class response value in the first cycle is 69.12 and in the second cycle it increases to 84.31. Percentage of response of students in learning to analyze the intrinsic elements of short stories with the application of inquiry learning models achieving classical completeness, namely 94.44%.