Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Media Tiga Dimensi Terhadap Kemampuan Berpikir Analsisis Siswa Pembelajaran Tematik

The background of this research is thematic learning in class V still using learning media that contains images in the book package. The purpose of the study was to understand the use of three-dimensional media on students' analytical skills. This type of research is quantitative research in the form of Pre Experimental Design with the type of Pretest-Posttest One Group. The population of this study was fifth grade students who gathered 27 students. Data collection uses test, observation, and documentation methods. The results of the assessment of students' thinking analysis before and after being given permission to use three-dimensional media need to be improved. Based on the results of the t-test know tcount 21.415&gt; t table 1.706. The media conclusions are three positive and significant dimensions of students' analytical thinking abilities.
Using Evolving Algorithms to Cryptanalysis Nonlinear Cryptosystems

In this paper, new method have been investigated using evolving algorithms (EA's) to cryptanalysis one of the nonlinear stream cipher cryptosystems which depends on the Linear Feedback Shift Register (LFSR) unit by using cipher text-only attack. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) which are used for attacking one of the nonlinear cryptosystems called "shrinking generator" using different lengths of cipher text and different lengths of combined LFSRs. GA and ACO proved their good performance in finding the initial values of the combined LFSRs. This work can be considered as a warning for a stream cipher designer to avoid the weak points, which may be found in the stream cipher, and may be explored by the cryptanalysts. This work can find the optimal solution for text with minimum lengths of 20 characters and 100 iteration were very enough to find the real initial values of key stream.
Constrained Self-Supervised Clustering for Discovering New Intents (Student Abstract)

Discovering new user intents is an emerging task in the dialogue system. In this paper, we propose a self-supervised clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process and does not require intensive feature engineering. Extensive experiments on three benchmark datasets show that our method can yield significant improvements over strong baselines.
Applications of Nature-Inspired Meta-heuristic Algorithms: A Survey

Nature-inspired meta-heuristic algorithms (NIMAs) combine the properties of nature-inspired and mete-heuristic algorithms. Evolutionary inspired and swarm-inspired algorithms are the most researche...
ISTIC’s Neural Machine Translation System for IWSLT’2020

Chinese-to-Japanese MT task.The paper mainly elaborates on the model framework, data preprocessing methods and decoding strategies adopted in our system.In addition, the system performance on the development set are given under different settings.
Bell-inequality and skew-information non-localities in two lossy cavity-qubit systems linked by a waveguide

We explore two open cavity-qubit systems linked via an optical fiber. The generation of non-local correlations (NLCs) between the two qubits, via skew-information and Bell-function, are investigated under the effects of the physical parameters of the initial state and the interaction couplings of the cavity-qubit and the cavity-fiber and the environment. It is found that the robustness and the generation of the NLCs depend on the physical parameters. Under the condition that the cavity-qubit couplings is much smaller than the cavity-fiber coupling, the regularity of the growth and the oscillations of the NLCs are enhanced and are more robust.
Gerakan literasi masyarakat basis media sosial

Introduction. The three main aspects to improve literacy movement are family, school, and society. Using social media could be useful to improve literacy as many reading materials can be accessed and freely available. However, the content should be selected to prevent from unexpected materials. &#x0D; Data Collection Method. Data obtained through observations of 100 participants in the development of community literacy movement. They were religious education community (pesantren) with professions such as, teachers, education staffs and students’ representatives from two levels of education within the reading village frameworks in Tanjungmekar village, Tanjungkerta, Sumedang.&#x0D; Analysis Data. Data were analyzed using descriptive percentage studies, and describing the results of observations.&#x0D; Result and Discussions. The community accesses reading material dominant using printed material, reading assignment and communication through social media have not done as expected. In general, the activities of fostering literacy in the religious education community require patterns that are in accordance with time, motivation, access, technological devices, various social media to foster literacy readiness.&#x0D; Conclusions. Alternative use of social media is expected be able to support community movement to build the literate village.
Task Scoping for Efficient Planning in Open Worlds (Student Abstract)

We propose an abstraction method for open-world environments expressed as Factored Markov Decision Processes (FMDPs) with very large state and action spaces. Our method prunes state and action variables that are irrelevant to the optimal value function on the state subspace the agent would visit when following any optimal policy from the initial state. This method thus enables tractable fast planning within large open-world FMDPs.
Marahel: A Language for Constructive Level Generation

Marahel is a language and framework for constructive gen- eration of 2D tile-based game levels. It is developed with the dual aim of making it easier to build level generators for game developers, and to help solving the general level generation problem by creating a generator space that can be searched using evolution. We describe the different sections of the level generators, and show examples of generated maps from 5 dif- ferent generators. We analyze their expressive range on three dimensions: percentage of empty space, number of isolated elements, and cell-wise entropy of empty space. The results show that generators that have starkly different output from each other can easily be defined in Marahel.
The rocs framework to support the development of autonomous robots

With the expansion of autonomous robotics and the variety of applications found nowadays (e.g. medical, competition, military), the biggest hurdle on the development of mobile robots lies in endowing them with the capacity of interacting with the environment and making correct decisions so that their tasks can be executed successfully. Based on the wellknown IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective.
Learning to Model Opponent Learning (Student Abstract)

Multi-Agent Reinforcement Learning (MARL) considers settings in which a set of coexisting agents interact with one another and their environment. The adaptation and learning of other agents induces non-stationarity in the environment dynamics. This poses a great challenge for value function-based algorithms whose convergence usually relies on the assumption of a stationary environment. Policy search algorithms also struggle in multi-agent settings as the partial observability resulting from an opponent's actions not being known introduces high variance to policy training. Modelling an agent's opponent(s) is often pursued as a means of resolving the issues arising from the coexistence of learning opponents. An opponent model provides an agent with some ability to reason about other agents to aid its own decision making. Most prior works learn an opponent model by assuming the opponent is employing a stationary policy or switching between a set of stationary policies. Such an approach can reduce the variance of training signals for policy search algorithms. However, in the multi-agent setting, agents have an incentive to continually adapt and learn. This means that the assumptions concerning opponent stationarity are unrealistic. In this work, we develop a novel approach to modelling an opponent's learning dynamics which we term Learning to Model Opponent Learning (LeMOL). We show our structured opponent model is more accurate and stable than naive behaviour cloning baselines. We further show that opponent modelling can improve the performance of algorithmic agents in multi-agent settings.
Repragmatization of the ME TOO Speech Act in Social and Communicative Practices of the Internet

The pragmatic nature of the Me too speech act is studied in two different areas of functioning: outside the Internet environment and in cyberspace. It is noted that in normal communication Me too is a response cue that closes an adjacency pair and does not have a persuasive potential. On the Internet, this speech act undergoes radical changes, turning into a stimulus statement that initiates the mass participation of a certain target audience in the movement against sexual harassment. It is shown that the pragmatic transformation of the phrase is due to the action of both linguistic and extralinguistic factors. The transition from a responsive cue to an initial utterance is ensured by a common presupposition for all addressees, while the versatility of intentions acquired by the speech act (affirmation, recognition, accusation, appeal) together with the deictic pronoun of the first person endow it with a high degree of persuasiveness. As the psychological basis for the effective impact of the phrase, the ideas of the American psychological school about the importance of empathy and the therapeutic effect of public actions - confessions are successfully introduced into the consciousness of a globalized community. The novelty of the study is that for the first time the fact of the limited relevance of individual postulates of pragmalinguistic theories for the Internet space is established. The relevance of the work is associated with the need for a comprehensive understanding of the phenomenon of repragmatization of the Me too , which receives the role of a semiotic marker and a cultural symbol of a new wave of the feminist movement.
PENINGKATAN KEMAMPUAN SISWA MENULIS BAHASA INGGRIS NARRATIVE TEXT MELALUI MEDIA GAMBAR BERSERI

The aim of this study is to improve students’ ability in writing narrative text in English by using picture series media. The object of this study is the Eighth grade students of one-roof Junior High School Teratak Baru Kuantan Hilir district. This study used the quantitative classroom action research which was carried out in 2 cycles, in this study the data collection technique used observation and tests (pre-test and post-test). The result of this study showed that there is an increasing in learning by students of eighth grade, consequently it can be concluded that by utilizing picture series media can improve students’ ability in writing English narrative text. It can be seen from the result of the average post-test in cycle 2 of 80 increased to85. The result of the observation showed that the teacher’s activities in teaching increased in both early condition in the cycle I and cycle II.
WANDA - a Higher Order Termination Tool (System Description)

Wanda is a fully automatic termination analysis tool for higher-order term rewriting. In this paper, we will discuss the methodology used in Wanda. Most pertinently, this includes a higher-order dependency pair framework and a variation of the higher-order recursive path ordering, as well as some non-termination analysis techniques and delegation to a first-order tool. Additionally, we will discuss Wanda’s internal rewriting formalism, and how to use Wanda in practice for systems in two different formalisms. We also present experimental results that consider both formalisms.
Assisting Undergraduate Students in Writing Spanish Methodology Sections

In undergraduate theses, a good methodology section should describe the series of steps that were followed in performing the research.To assist students in this task, we develop machine-learning models and an app that uses them to provide feedback while students write.We construct an annotated corpus that identifies sentences representing methodological steps and labels when a methodology contains a logical sequence of such steps.We train machine-learning models based on language modeling and lexical features that can identify sentences representing methodological steps with 0.939 f-measure, and identify methodology sections containing a logical sequence of steps with an accuracy of 87%.We incorporate these models into a Microsoft Office Addin, and show that students who improved their methodologies according to the model feedback received better grades on their methodologies.
Tabouid: a Wikipedia-based word guessing game

We present Tabouid, a word-guessing game automatically generated from Wikipedia.Tabouid contains 10,000 (virtual) cards in English, and as many in French, covering not only words and linguistic expressions but also a variety of topics including artists, historical events or scientific concepts.Each card corresponds to a Wikipedia article, and conversely, any article could be turned into a card.A range of relatively simple NLP and machine-learning techniques are effectively integrated into a two-stage process.First, a large subset of Wikipedia articles are scored -this score estimates the difficulty, or alternatively, the playability of the page.Then, the best articles are turned into cards by selecting, for each of them, a list of banned words based on its content.We believe that the game we present is more than mere entertainment and that, furthermore, this paper has pedagogical potential. 1
AraDIC: Arabic Document Classification Using Image-Based Character Embeddings and Class-Balanced Loss

Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering.These could be eliminated by using character-level features.We propose a novel end-to-end Arabic document classification framework, Arabic document imagebased classifier (AraDIC), inspired by the work on image-based character embeddings.AraDIC consists of an image-based character encoder and a classifier.They are trained in an end-to-end fashion using the class balanced loss to deal with the long-tailed data distribution problem.To evaluate the effectiveness of AraDIC, we created and published two datasets, the Arabic Wikipedia title (AWT) dataset and the Arabic poetry (AraP) dataset.To the best of our knowledge, this is the first image-based character embedding framework addressing the problem of Arabic text classification.We also present the first deep learningbased text classifier widely evaluated on modern standard Arabic, colloquial Arabic and classical Arabic.AraDIC shows performance improvement over classical and deep learning baselines by 12.29% and 23.05% for the micro and macro F-score, respectively.
The CARL Portage Partnership Story

Portage is the research data management initiative of the Canadian Association of Research Libraries and its story has been very much about establishing partnerships in a complex environment to advance research data management services and infrastructure in Canada. Many jurisdictions make up the space in which research data management takes place. A variety of legal, political, cultural, economic, technological, and scientific factors are at play and how they fit together depends on the connections between a number of stakeholders. The levels at which these stakeholders operate and the transient nature of research data itself made the development of partnerships a complex undertaking for Portage. This article describes the building of partnerships in a multi-jurisdictional environment, discusses challenges in operating in Canada's digital research ecosystem, and highlights the importance of working with Canada's regional academic library associations in laying the foundations for digital research infrastructure to support data management.
CASE STUDY: THE ROLE OF LEARNING STRATEGIES, PERSONALITY AND MOTIVATION IN THE SECOND LANGUAGE ACQUISITION: WRITING PROFICIENCY OF A SOMALIAN STUDENT

The study aims to investigate a case study of second language acquisition of a foreign learner studying at Universitas Islam Negeri Maulana Malik Ibrahim, Malang. There are three aspects examined by the researcher such as the learning strategies, personality, and motivation which influence the learner’s second language acquisition of English. The findings of the research show that the subject has good learning strategies such as reading English books, learning via online media, and studying with friends. The subject has a good personality, an introvert who is confident in practicing English with several people in Indonesia. The subject has high motivation in learning English to get the scholarship and to have a good career in the future. After assessing his essay with the rubric score, the students’ writing proficiency is improved.
Teaching wave-particle complementarity using the Virtual Mach-Zehnder Interferometer

We propose the teaching of wave-particle duality mediated by a software called Virtual Mach-Zehnder Interferometer, developed by our research group. We introduce the Dirac’s formalism contextualized on the Mach-Zehnder Interferometer, constructing explicitly the operators that represent the action of each device on the interferometer, calculating probability amplitudes on screens. Wave-particle complementarity is explored both in the qualitative and quantitative perspective, using computational simulations provided by the software. By studying concepts such as visibility, predictability and distinguishability, we explore the intermediate interference patterns aiming to expand what has not traditionally been found in textbooks and most courses. With the purpose of illustrating the application of the software in the classroom, the paper presents some didactical situations experienced by students of a physics teacher-training course during a quantum physics class, discussing students’ discursive interactions. The didactical approach to quantum interference phenomena carried out with the software, whether qualitative or quantitative, can promote rich and interesting discussions among students, exploring recent topics of quantum physics and allowing them to have a deeper and articulated understanding of the theory.
Real-time salient object detection with boundary information guidance

Abstract The low contrast between salient objects and background may result in errors in salient object detection. The low-contrast areas are likely distinguished away from salient objects by saliency information. However, salient objects near the boundary are often detected wrongly based on current methods. In this paper, a novel network with boundary information guidance is proposed to distinguish low-contrast areas near the boundary effectively. We design two separate decoding sub-networks including a saliency sub-network and a boundary sub-network to learn saliency and boundary information respectively based on multi-level feature fusion. We further design a connection module to exchange the information between two sub-networks and a fusion module to fuse information of salient objects and boundaries. The boundary sub-network supervised by boundary maps outputs the error map calculated by ground truth to focus on boundary information. The boundary sub-network is only used in training and the optimal weight of the total loss is obtained by experiments to balance the two sub-network losses. With the guidance of boundary information, salient objects are detected accurately. Extensive evaluations on six benchmark and two video datasets demonstrate that our model outperforms the state-of-the-art methods. Furthermore, the proposed model can run at more than 34 FPS.
Optimizing the Feature Selection Process for Better Accuracy in Datasets with a Large Number of Features (Student Abstract)

Most feature selection methods only perform well on datasets with relatively small set of features. In the case of large feature sets and small number of data points, almost none of the existing feature selection methods help in achieving high accuracy. This paper proposes a novel approach to optimize the feature selection process through Frequent Pattern Growth algorithm to find sets of features that appear frequently among the top features selected by the main feature selection methods. Our experimental evaluation on two datasets containing a small and very large number of features shows that our approach significantly improves the accuracy results of the dataset with a very large number of features.
Selecting and Sharing Multidimensional Projection Algorithms: A Practical View

Multidimensional Projection techniques are often used by data analysts for exploring multivariate datasets, but the task of selecting the best technique for the job is not trivial, as there are many candidates and the reasons for picking one over another are usually unclear. On the other hand, researchers developing new techniques can have a hard time comparing their new technique to existing ones and sharing their code in a way that makes it readily available for the public. In this paper, we try to address those issues systematically by analyzing recent surveys in the area, identifying the methods and tools used, and discussing challenges, limitations, and ideas for further work.
Models for the Calculation of Diffuse Radiation on Solar Collectors

For the design and exploitation of solar energy systems, information of both direct and diffuse component of solar radiation is necessary. However, the measurement data that are available are usually restricted to total horizontal irradiance. Therefore, many models have been developed to determine the diffuse component of the irradiance on solar collectors. This paper discusses the most well-known isotropic and anisotropic models for the calculation of the diffuse component of the solar irradiance. A modification of the isotropic clear day model for determining the diffuse component of solar radiation on a horizontal surface has been presented. The presented model has the same form as a simple clear day model, with the parameters determined using the least squares method and measurement data on horizontal irradiance in the region of Belgrade, Serbia. The applicability of isotropic clear day models was examined. The paper also discusses the applicability of the models for determining the diffuse irradiance on tilted collectors in real weather conditions during the year. The basic expressions are given and the characteristics of individual models are highlighted. Based on the measurement data on horizontal irradiance, the possibilities of applying different models for the calculation of the diffuse component of irradiance on solar collectors located in the region of Belgrade were examined.
ThreatPredict: From Global Social and Technical Big Data to Cyber Threat Forecast

Predicting the next threats that may occurs in the Internet is a multifaceted problem as the predictions must be enough precise and given as most as possible in advance to be exploited efficiently, for example to setup defensive measures. The ThreatPredict project aims at building predictive models by integrating exogenous sources of data using machine learning algorithms. This paper reports the most notable results using technical data from security sensors or contextual information about darkweb cyber-criminal markets and data breaches.