Reviewerly/exHarmony huggingface dataset:
Academic Whatsapp Group: Exploring Students’ Experiences in Writing Class

The use of social-networking application such as WhatsApp in learning writing enables students to foster individual learning experiences during the class and after the class. WhatsApp itself has a special feature to create the specific group in order to bound the members and intimate the interaction among lecturer and students relating to the course matters. This current study generally explores the students’ perceptions in experiencing WhatsApp Group Chat in learning writing. This qualitative research was conducted in one of universities in Lampung province, Indonesia. The subjects were 28 second year undergraduate students who enrolled writing course. The data used were questionnaire, interview, and observations. The findings present the multitude of views that most students significantly showed positive result to use WhatsApp Group in their writing class. By experiencing WhatsApp Group Chat in their learning writing, the students admitted that it is user-friendly and easy to use because it can intensify the interaction with the other group members during the class and after the class, be effective in submitting the writing assignment by uploading the file in the group, get the lecturer’s feedback directly after submitting the writing assignment, and freely ask and share the writing materials. However, some students also revealed that there were some technical problems found when they used the WhatsApp Group Chat such as abundant chats, poor signal, and junks notification. Nevertheless, learning writing through these cross-platform instant messaging might broaden the student’s self-learning experiences.Keywords: Students’ experience, WhatsApp Group, writing class
Flood Prediction with Ensemble Machine Learning using BP-NN and SVM

This study aims to examine the prediction of rainfall and river water debit using the Back Propagation Neural Network (BP-NN) method. Prediction results are classified using the Support Vector Machine (SVM) method to predict flooding. The parameters used to predict rainfall with BP-NN are minimum, maximum and average temperature, average relative humidity, sunshine duration, and average wind speed. The debit of Ular Pulau Tagor river is predicted by BP-NN. BPNN and SVM modeling using software R. Daily climate data from 2015-2017 were taken from three stations, namely Sampali climatology station, Kualanamu meteorological station, and Tuntung geophysics station. Prediction of river water debit is for 6 days and 30 days in the future. The best dataset is a 6 day prediction with a combination of 60% training and 40% testing. Flood prediction accuracy with SVM was 100% in predicting flood events for the next 6 days.
Handling Class Imbalance in High-Dimensional Biomedical Datasets

When dealing with biomedical data, the first and most challenging issue is often the huge dimensionality, i.e. the presence of a very high number of features for each of the problem instances at hand. A vast literature is available on different dimensionality reduction techniques that can be suitable for handling such kind of data, with a special focus on feature selection algorithms that allow to discard uninformative/useless features. In most cases, however, the dimensionality issue is addressed without a joint consideration of other potential problems in the data, including an imbalanced class distribution that may hinder the construction of effective classification models. Class imbalance, in turn, has been mostly treated in literature as an independent problem, especially in application fields where the number of features is not so critical. But several biomedical datasets are both high-dimensional and class-imbalanced, so there is a strong need for designing and evaluating learning strategies that can properly deal with both the issues simultaneously. In this work, we experiment with using feature selection techniques in conjunction with sampling-based class balancing methods and cost-sensitive classification, in order to gain insight into the most effective strategies to use when dealing with such complex data.
Application of Fuzzy Sets to the Expert Estimation of Scrum-Based Projects

The paper is basically dedicated to the problem of effort estimation for the Product Backlog items of IT projects led accordingly to the Scrum framework. The effort estimation issue is important, because low quality estimation decreases the efficiency of project implementation. The paper proposes an estimation method for the Product Backlog items of Scrum-based IT projects (which can be adapted also to other projects), which has two original elements with respect to the state of art in Scrum estimation: the usage of fuzzy numbers and strict rules for consensus forming, combined with a space for human interaction. The assumptions of the method should be complied with and were formulated on the basis of literature and authors experience. Two case studies were used for an initial method validation. The case studies confirmed a high potential of the method to increase estimation quality in Scrum-based projects, as well as in other project types. The case studies were conducted using research methods fulfilling the symmetry principle. The paper is thus an example of symmetry in management research.
ETHICAL LEADERSHIP TRANFORMS INTO ETHNIC: EXPLORING NEW LEADER’S STYLE OF INDONESIA

Although numerous styles have been utilized to study leadership focusing on task, role, work, managerial, spiritual and ethical, however there is no agreement on the ethnic style. The debate over this issue as related to the study of ethical leadership transforms into ethnic, especially natural roles of Indonesian’s leaders. In this paper two major approaches phenomenology and ethnography are examined to gaining the theory and the latter is presented the natural roles of ethnic leaders. This study proofs that (1) the concept of ethnic leadership is the ability to spread the belief, value, culture, and norm of ancestry. Also, (2) natural roles of ethnic leaders are transcendent, endogen and reflection of roles.
Research Methodologies to Supportthe Development of Blockchain Standards

This paper dissects the research methodologies implemented by the Research Team for the Smart Contracts Working Group (TC-307/IT-041 Blockchain and Distributed Ledger Technologies) in developing standards to inform best practice in the design and use of blockchain and distributed ledger technologies. In doing so, it explores the origins of blockchain standardisation and outlines the high-level methodology for conducting and delivering research in this rapidly evolving space.Using three case studies, this paper shows how different tasks give rise to, and benefit from, specialised methodologies. These case studies also highlight a number of challenges and opportunities, and demonstrate how a flexible research approach can increase efficiency and generate findings that are likely to influence blockchain standardisation at a global level.Case Study 1 describes our research into the legal status of smart contracts and the challenge of delivering clear findings about unclear subject matter. Case Study 2, which involved examining the sufficiency of existing regulatory frameworks in the supply chain and trade facilitation context, illustrates how rigid formatting specifications can impact the content of findings. In contrast, Case Study 3 concerns a project to incorporate the United Nations Sustainable Development Goals into new and existing Standards and shows how researchers’ knowledge can be harnessed as a valuable source of information in its own right.Finally, this paper extrapolates practical lessons for researchers who are interested in developing standards for emerging technologies, or simply undertaking much needed research into this area.
Low-Memory Attacks Against Two-Round Even-Mansour Using the 3-XOR Problem

The iterated Even-Mansour construction is an elegant construction that idealizes block cipher designs such as the AES. In this work we focus on the simplest variant, the 2-round Even-Mansour construction with a single key. This is the most minimal construction that offers security beyond the birthday bound: there is a security proof up to $$2^{2n/3}$$ evaluations of the underlying permutations and encryption, and the best known attacks have a complexity of roughly $$2^n/n$$ operations. We show that attacking this scheme with block size n is related to the 3-XOR problem with element size $$\ell = 2n$$ , an important algorithmic problem that has been studied since the nineties. In particular the 3-XOR problem is known to require at least $$2^{\ell /3}$$ queries, and the best known algorithms require around $$2^{\ell /2}/\ell $$ operations: this roughly matches the known bounds for the 2-round Even-Mansour scheme. Using this link we describe new attacks against the 2-round Even-Mansour scheme. In particular, we obtain the first algorithms where both the data and the memory complexity are significantly lower than $$2^{n}$$ . From a practical standpoint, previous works with a data and/or memory complexity close to $$2^n$$ are unlikely to be more efficient than a simple brute-force search over the key. Our best algorithm requires just $$\lambda n$$ known plaintext/ciphertext pairs, for some constant $$0< \lambda < 1$$ , $$2^n/\lambda n$$ time, and $$2^{\lambda n}$$ memory. For instance, with $$n=64$$ and $$\lambda = 1/2$$ , the memory requirement is practical, and we gain a factor 32 over brute-force search. We also describe an algorithm with asymptotic complexity $$\mathcal {O}(2^{n} \ln ^2 n/n^2)$$ , improving the previous asymptotic complexity of $$\mathcal {O}(2^n/n)$$ , using a variant of the 3-SUM algorithm of Baran, Demaine, and Pǎtraşcu.
Designing Visualizations for Workplace Stress Management: Results of a Pilot Study at a Swiss Municipality

Job absenteeism and health problems are frequently caused by elevated exposure to work-related stress. The public sector is particularly affected by this development. Nevertheless, public sector organizations seem to have issues to reliably detect stress or to discuss about this topic in an objective and factual manner. Data visualizations have been found to be a powerful boundary object for sense-making and for unraveling issues that lie under the surface. Based on a pilot study at a medium-sized municipality in Switzerland, we thus developed, tested, and discussed various alternative visual representations for creating awareness about occupational stress. The results of this study showcase the hidden potential and perils of analyzing physiolytics data on aggregate level.
Assessment of Online Learning Application for Health Education

&lt;p&gt;Online learning has become a model, learning strategies, and the preferred channel in education around the world because it is not restricted by time and place. The development of online learning in supporting the success of health education programs for early detection of cervical cancer is the right solution to improve health workers knowledge. To measure user acceptance of the online learning applications, the study used Technology Acceptance Model (TAM) approach. Data collected from questionnaires then analyzed by calculating the frequency (proportion) of the total value, the interpretation of a score by looking for index% and interval. The final result of the calculation shows that all respondents agreed and strongly agreed with all four factors measured. The conclusions are the users believe that the use of learning online applications can reduce the task effort, improve job performance, give positive idea to use the technology, and consciously and expressed desire to run the online learning process in the future.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
KEEFEKTIFAN PENGAJARAN KOSAKATA BAHASA INGGRIS PADA ANAK SEKOLAH DASAR DENGAN MENGGUNAKAN FLASH CARD

Giving the right stimulus in early stage of age gives a very great impact to the next human development process. There are many methods that we can apply for balancing the right brain and the left brain capability. One of them is using a flash card as a media which uses a card game. Flash card is an effective media to teach children in reading a text and knowing their surrounding in an early age. Trough flash card we can introduce colours,animals, letters, numbers, and other things. In principle, introducing the learning materials trough flash card can be delivered in a simple way and the most important is a pleasure atmosphere both for the children (the learner) and the teacher. Therefore, we can conclude that eventhough the learning process only takes a few minutes but it gives a big benefit for our children.&#x0D; Key words: Effectiveness, Vocabulary, Flash Card
Practical BRDF reconstruction using reliable geometric regions from multi-view stereo

In this paper, we present a practical method for reconstructing the bidirectional reflectance distribution function (BRDF) from multiple images of a real object composed of a homogeneous material. The key idea is that the BRDF can be sampled after geometry estimation using multi-view stereo (MVS) techniques. Our contribution is selection of reliable samples of lighting, surface normal, and viewing directions for robustness against estimation errors of MVS. Our method is quantitatively evaluated using synthesized images and its effectiveness is shown via real-world experiments.
A Path Planning Approach Based on Q-learning for Robot Arm

A path planning method based on Q-learning is proposed for robot arm. As reinforcement learning, Q-learning is widely used in the field of mobile robot navigation due to its simple and well-developed theory. However, most of researchers avoid using it for solving the robot arm path planning problem because it has to take each joints motion into account. In this study, approximate regions instead of accurate measurements are used to define new state space and joint actions. Moreover, the reward function takes the distance from current position of robots' end-effector to the goal position into account. The experimental simulation shows that the Q-learning approach is efficient and has ability to plan a collision free path for robot arm when the order of magnitude of state space is below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> . Furthermore, the experimental results indicate that the number of obstacles affects the calculation time for the same iterations. The more obstacles, the less calculation time the algorithm needs. The weight coefficient in the reward function affects the convergence speed and the quality of the solutions.
ScaffoMapping: Assisting Concept Mapping for Video Learners

Previous research has shown that having learners construct concept maps can bring better learning outcome. However, in video learning scenario, there is not sufficient support for learners to create concept maps from educational videos. Through a preliminary study, we identified two main difficulties video learners face in creating concept maps: navigation difficulty and learning difficulty. To help users to overcome such difficulties, we design scaffolds to assist learners in concept mapping. We present ScaffoMapping, a system aiming for scaffolded concept map creation on educational videos through automatic concept extraction and timestamp generation. Our study, which compares ScaffoMapping with the baseline approach, shows that (1) Learners can create higher quality concept maps with ScaffoMapping. (2) ScaffoMapping enables better learning outcomes in video learning scenario.
BotFlowMon: Learning-based, Content-Agnostic Identification of Social Bot Traffic Flows

With the fast-growing popularity of online social networks (OSN), maintaining the security of OSN ecosystems becomes essential for the public. Among all the security threats facing OSN, malicious social bots have become the most common and detrimental. These bot programs are often employed to violate users' privacy, distribute spam, and disturb the financial market, posing a compelling need for effective social bot detection solutions. Unlike traditional bot detection approaches that have strict requirements on data sources (e.g., private payload information, social relationships, or activity histories), this paper proposes a detection method called BotFlowMon that relies only on NetFlow data as input to identify OSN bot traffic, where every NetFlow record is a summary of a traffic flow on the Internet and contains no payload content. BotFlowMon introduces several new algorithms and techniques to help use machine learning to classify the social bot traffic from the real OSN user traffic, including aggregating NetFlow records to obtain transaction data, fusing transaction data to extract features and visualize flows, as well as subdividing transactions into basic actions. Our evaluation shows that with 535GB raw NetFlow records as input, BotFlowMon can efficiently classify the traffic from social bots, including chatbot, amplification bot, post bot, crawler bot, and hybrid bot, with 92.33-93.61 % accuracy.
Welcome, Computer! How Do Participants Introduce a Collaborative Application During Face-to-Face Interaction?

During cooperative interaction, participants introduce materials, artefacts, and other individuals into the ongoing interaction. Depending on how this introduction unfolds, the participants may embrace the new element in an easy way or not. If the new element is a collaborative application of interactive software designed to support the interaction, it may or may not improve the collaboration because of how it was introduced. Therefore, understanding and designing the initial interaction is key for unleashing the positive impact of collaborative systems. The literature has identified the fact that humans employ a specific range of behaviors when introducing an element into an ongoing interaction. Those introduction rituals are determined by whether the new element is a human or a material artefact. Introduction rituals involving interactive elements are still underexplored: How do participants introduce and initiate interaction with them? This manuscript explores the introduction behaviors emerging when an augmented-reality collaborative application is being introduced into a financial advisory service. It shows that the participants employ a wider range of introduction rituals during the introduction of this application than they do when they introduce a brochure. Notably, many of the observed behaviors resemble familiar opening rituals typically used when introducing and greeting humans. This supports the computers-are-social-actors argument and provides evidence that introducing a collaborative application has a social rather than a material character.
Comic book for improving the elementary school students' mathematical problem solving skills and self-confidence

The study aims at generating a product in the form of comic book and also displaying the feasibility and the effectiveness of the comic book in improving the mathematical problem-solving skills and the self-confidence of the Grade IV students in the Muhammadiyah Elementary School Condongcatur Yogyakarta. The study is a Research and Development initiative (R&amp;) with reference to 10 stages that have been developed by Borg &amp; Gall. During the conduct of the study, the subjects were the Grade IV students of Muhammadiyah Elementary School Condongcatur Yogyakarta. The results of the study show that: (1) a comic book-based learning media that meet the criteria of feasibility based on the validation results by the material expert, the media expert, the teacher response and the students response have been generated with “Good” category; and (2) the comic book-based learning media that have been generated are effective for improving the students’ mathematical problem-solving skills and also the students’ self-confidence.
A high-speed key management method for quantum key distribution network

Quantum Key Distribution (QKD) is a technique for sharing encryption keys between two adjacent nodes. It provides unconditional secure communication based on the laws of physics. From the viewpoint of network research, QKD is considered to be a component for providing secure communication in network systems. A QKD network enables each node to exchange encryption keys with arbitrary nodes. However previous research did not focus on the processing speed of the key management method essential for a QKD network. This paper focuses on the key management method assuming a high-speed QKD system for which we clarify the design, propose a high-speed method, and evaluate the throughput. The proposed method consists of four modules: (1) local key manager handling the keys generated by QKD, (2) one-time pad tunnel manager establishing the transparent encryption link, (3) global key manager generating the keys for application communication, and (4) web API providing keys to the application. The proposed method was implemented in software and evaluated by emulating QKD key generation and application key consumption. The evaluation result reveals that it is capable of handling the encryption keys at a speed of 414 Mb/s, 185 Mb/s, 85 Mb/s and 971 Mb/s, for local key manager, one-time pad tunnel manager, global key manager and web API, respectively. These are sufficient for integration with a high-speed QKD system. Furthermore, the method allows the high-speed QKD system consisting of two nodes to expand corresponding to the size of the QKD network without losing the speed advantage.
SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation

Quality estimation (QE) of machine translation (MT) systems is a task of growing importance.It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions.In this work, we describe our submission system in WMT 2019 sentence-level QE task.We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy.The strategy is similar to ELMo, but additionally conditions on source sentences.Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE.In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.
The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction

In recent years, sequence-to-sequence models have been very effective for end-to-end grammatical error correction (GEC).As creating human-annotated parallel corpus for GEC is expensive and time-consuming, there has been work on artificial corpus generation with the aim of creating sentences that contain realistic grammatical errors from grammatically correct sentences.In this paper, we investigate the impact of using recent neural models for generating errors to help neural models to correct errors.We conduct a battery of experiments on the effect of data size, models, and comparison with a rule-based approach.
Minimally Supervised Learning of Affective Events Using Discourse Relations

Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words.In this paper, we propose to propagate affective polarity using discourse relations.Our method is simple and only requires a very small seed lexicon and a large raw corpus.Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data.It also improves supervised learning results when labeled data are small.