Reviewerly/exHarmony huggingface dataset:
Learning of Reading Comprehension through Reading Workshop in the Industry 4.0

Reading comprehension is still considered as a part of new literacy required to encounter the Industry 4.0. Reading comprehension in this era is related to reading the data presented in technology. In addition to students’ ability to understand readings, students sort positive and negative information in a reading through reading comprehension. Students can also get used to utilize the internet access to read various information and stories. Therefore, the presence of the Industry 4.0 is considered to not have negative effects on students. This research focuses on examining the online magazine-based reading workshop model towards the improvement of fourth grade students’ reading comprehension skills. The research employed quasi experiment methods with a nonequivalent group design. The research sample was selected purposively from fourth grade students in primary schools in Cimahi City. The data were collected using the test of reading comprehension skills and were analyzed using inferential statistics with the assistance of the SPSS 21.0 program. Through this research, the researchers are expected that the online magazine-based reading workshop model has an effect on the improvement of the fourth-grade students’ reading comprehension skills.
WristUnlock: Secure and Usable Smartphone Unlocking with Wrist Wearables

We propose WristUnlock, a novel technique that uses a wrist wearable to unlock a smartphone in a secure and usable fashion. WristUnlock explores both the physical proximity and secure Bluetooth connection between the smartphone and wrist wearable. There are two modes in WristUnlock with different security and usability features. In the WristRaise mode, the user raises his smartphone in his natural way with the same arm carrying the wrist wearable; the smartphone gets unlocked if the acceleration data on the smartphone and wrist wearable satisfy an anticipated relationship specific to the user himself. In the WristTouch mode, the wrist wearable sends a random number to the smartphone through both the Bluetooth channel and a touch-based physical channel; the smartphone gets unlocked if the numbers received from both channels are equal. We thoroughly analyze the security of WristUnlock and confirm its high efficacy through detailed experiments.
PEMANFAATAN MEDIA PEMBELAJARAN DALAM PROSES BELAJAR UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KOGNITIF SISWA

Bruner argues (in Slameto,2010:11) , it would be nice if the school could provide opportunities for students to progress quickly according to students' abilities in certain subjects. To improve the learning process needs an environment called discovery learning environment. Thus learning media have roles and functions that greatly influence teaching and learning. The instructor of the learning media itself is a teaching aid that also influen ces the climate, conditions and learning environment that is organized and created by the teacher. Hamalik (1986) suggests that the use of instructional media in the learning process can generate new desires and interests, generate motivation and stimulation of learning, and even bring psychological effects to students. In the teaching and learning process, the teacher has the duty to encourage, guide, and provide learning facilities for students to achieve goals. The role of the media is very important to assist teachers in achieving these goals. Especially in cognitive students, because learning is influenced by the frequency of encounters with stimuli or the provision of the same or similar stimulus and response made in the form of learning media. The purpose of this paper provides an understanding of the importance of the role of learning media in the development of students' stimulus abilities in cognitive thinking. So the role of the teacher is needed in the development of appropriate learning media. Keyword : learning, learning media,  cognitive thinking
RESEARCH ON DETERMINING PERCEPTIONS AND ATTITUDES TOWARDS EMOJI USE IN DIGITAL MARKETING CAMPAIGNS

The effect of the digital revolution is spreading through all aspects of life. One of the newest periods of marketing strategy is digital marketing. The most intensive group using digital devices is young students. In addition, young consumers are the group that uses social media the most. Users who want to interact with younger generations use digital marketing technologies. Companies can use many tools for their digital marketing campaigns. The newest digital marketing tool can be considered emojis. The aim of this study is to investigate the attitudes and perceived benefits of young consumers towards emojis in emotional and rational advertising messages used in social media advertising campaigns. In order to realize the aim of the research, a questionnaire study was conducted on the students of the School of Applied Sciences. The research on 580 students revealed that emojis were more effective than traditional methods when they were used at the right time and on the target audience.
Direct Estimation of Differential Functional Graphical Models

We consider the problem of estimating the difference between two functional undirected graphical models with shared structures. In many applications, data are naturally regarded as high-dimensional random function vectors rather than multivariate scalars. For example, electroencephalography (EEG) data are more appropriately treated as functions of time. In these problems, not only can the number of functions measured per sample be large, but each function is itself an infinite dimensional object, making estimation of model parameters challenging. We develop a method that directly estimates the difference of graphs, avoiding separate estimation of each graph, and show it is consistent in certain high-dimensional settings. We illustrate finite sample properties of our method through simulation studies. Finally, we apply our method to EEG data to uncover differences in functional brain connectivity between alcoholics and control subjects.
Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind

In this work we proposed a computational model to extract pedestrian characteristics from video sequences. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. As one of the main contributions, four large dimensions of geometric characteristics (Big4GD) were proposed: I - Physical, II - Personal and Emotional, III - Social and IV - Cultural, which seek to describe the behavior of pedestrians and groups in the crowd. The GeoMind tool was developed for the purpose of detecting the four geometric dimensions from video sequences. In addition, several analyzes were carried out with the purpose of validating the proposed model, from comparing results with the literature, including the comparison of spontaneous multitudes from several countries and controlled experiments involving Fundamental Diagrams.
GazeMotive: A Gaze-Based Motivation-Aware E-Learning Tool for Students with Learning Difficulties

We developed a gaze-based motivation-aware e-learning tool, a Windows desktop learning application, for students with learning difficulties that aims at motivation-enhanced learning by dynamically assessing and responding to students’ motivational states based on the motivation model that we developed previously using rigorous methodologies including domain knowledge and empirical studies with participants with learning difficulties. The learning application uses an eye tracker to monitor a user’s eye movements during the user’s learning process, assesses the user’s motivational states using the prediction models we developed before to output personalised feedback from a pedagogical agent in the system based on both the eye gaze features and user’s self-input data for enhancing users’ motivation and engagement in real-time. Our e-learning tool is an example of applying user modelling and personalisation to an e-learning environment targeting at users’ learning motivation, producing great insight on how eye tracking can assist with students’ learning motivation and engagement in e-learning environments.
GTAA: A Geo-Aware Task Allocation Approach in Cloud Workflow

The cloud computing simplifies application development into the orchestration of virtual-services workflow. However, network latency between geographically distributed hosts would slow down the workflow's makespan time. This paper proposes a geo-aware task allocation approach (GTAA). GTAA partitions the workflow for geo-distributed data centers(DCs) and reduces sub-workflows across DCs. GTAA aims to optimize overall workflow makespan time and improves the efficiency of workflow.
MAssistant: A Personal Knowledge Assistant for MOOC Learners

Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners.In this work, we present MAssistant system, a personal knowledge assistant for MOOC learners.MAssistant helps users to trace the concepts they have learned in MOOCs, and to build their own concept graphs.There are three key components in MAssistant: (i) a large-scale concept graph built from open data sources, which contains concepts in various domains and relations among them; (ii) a browser extension which interacts with learners when they are watching video lectures, and presents important concepts to them; (iii) a web application allowing users to explore their personal concept graphs, which are built based on their learning activities on MOOCs.MAssistant will facilitate the knowledge management task for MOOC learners, and make the learning on MOOCs easier.
PPHE-automatic detection of sensitive attributes in a privacy preserved Hadoop environment using data mining techniques

Online social networks (OSN) has become highly popular, where users are more and more lured to reveal their private information. To balance privacy and utility, many privacy preserving approaches have been proposed which does not meet well users personalized requirements. In this paper, we present a privacy preserved Hadoop environment (PPHE) which automatically detects sensitive attributes using data mining techniques. This work considers Twitter which contains private information such as email addresses, mobile numbers, physical addresses, and date of births. First, we authenticate each Twitter user using the integrated algorithm RSA and Elgamal algorithm. Second, we categorize the tweets into private and non-private attributes based on the type-2 fuzzy logic system. Third, we apply a data suppression technique for private tweets and finally share the user's content based on their similarity information. Content similarity has been evaluated using cosine similarity. Finally we evaluate the system performance in terms of accuracy, precision, recall, and F-measure.
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification

The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data.Most previous work follows a two-step process.They first separate the content from the original sentiment, and then directly generate text with the target sentiment only based on the content produced by the first step.However, the second step bears both the target sentiment addition and content reconstruction, thus resulting in a lack of specific information like proper nouns in the generated text.To remedy this, we propose a specificity-driven cascading approach in this work, which can effectively increase the specificity of the generated text and further improve content preservation.The experiments show that our approach outperforms competitive baselines by a large margin, which achieves 11% and 38% relative improvements of the overall metric on the Yelp and Amazon datasets, respectively.
Analisis Speaker Recognition Menggunakan Metode Dynamic Time Warping (DTW) Berbasis Matlab

Voice is one of way to communicate and express yourself. Speaker recognition is a process carried out by a device to recognize the speaker through the voice. This study designed a speaker recognition system that was able to identify speakers based on what was said by using dynamic time warping (DTW) method based in matlab. To design a speaker recognition system begins with the process of reference data and test data. Both processes have the same process, which starts with sound recording, preprocessing, and feature extraction. In this system, the Fast Fourier Transform (FFT) method is used to extract the features. The results of the feature extraction process from the two data will be compared using the DTW method. Calculations using DTW that produce the smallest value will be determined as the output. The test results show that the system can identify the voice with the best level of recognition accuracy of 90%, and the average recognition accuracy of 80%. The results were obtained from 50 tests, carried out by 5 people consisting of 3 men and 2 women, each speaker said a predetermined word
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

Qiongkai Xu, Chenchen Xu, Lizhen Qu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations. 2019.
Detecting Argumentative Discourse Acts with Linguistic Alignment

We report the results of preliminary investigations into the relationship between linguistic alignment and dialogical argumentation at the level of discourse acts.We annotated a proof of concept dataset with illocutions and transitions at the comment level based on Inference Anchoring Theory.We estimated linguistic alignment across discourse acts and found significant variation.Alignment features calculated at the dyad level are found to be useful for detecting a range of argumentative discourse acts.
The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets

The sequence of documents produced by any given author varies in style and content, but some documents are more typical or representative of the source than others.We quantify the extent to which a given short text is characteristic of a specific person, using a dataset of tweets from fifteen celebrities.Such analysis is useful for generating excerpts of highvolume Twitter profiles, and understanding how representativeness relates to tweet popularity.We first consider the related task of binary author detection (is x the author of text T?), and report a test accuracy of 90.37% for the best of five approaches to this problem.We then use these models to compute characterization scores among all of an author's texts.A user study shows human evaluators agree with our characterization model for all 15 celebrities in our dataset, each with p-value < 0.05.We use these classifiers to show surprisingly strong correlations between characterization scores and the popularity of the associated texts.Indeed, we demonstrate a statistically significant correlation between this score and tweet popularity (likes/replies/retweets) for 13 of the 15 celebrities in our study.
Lingua Custodia at WMT’19: Attempts to Control Terminology

This paper describes Lingua Custodia's submission to the WMT'19 news shared task for German-to-French on the topic of the EU elections.We report experiments on the adaptation of the terminology of a machine translation system to a specific topic, aimed at providing more accurate translations of specific entities like political parties and person names, given that the shared task provided no in-domain training parallel data dealing with the restricted topic.Our primary submission to the shared task uses backtranslation generated with a type of decoding allowing the insertion of constraints in the output in order to guarantee the correct translation of specific terms that are not necessarily observed in the data.
HCI Challenges in Human Movement Analysis

Assessing human bodies’ postures and positions enables to design new interaction techniques, to understand users’ performances and to evaluate ergonomics of devices. In addition to the applications for improving Human-Computer Interaction, human movement analysis is at the heart of other types of usages including sports, rehabilitation, gesture recognition, etc. This workshop aims at providing a platform for researchers and designers to discuss the challenges related to the processing (e.g., data collection, treatment, interpretation, recognition) of human movement (e.g., motor skills, amplitude of movements, limitations). We expect to identify the main challenges to be addressed and come up with a research agenda to give HCI new perspectives and suggest promising directions.
Unified Sample-Optimal Property Estimation in Near-Linear Time

We consider the fundamental learning problem of estimating properties of distributions over large domains. Using a novel piecewise-polynomial approximation technique, we derive the first unified methodology for constructing sample- and time-efficient estimators for all sufficiently smooth, symmetric and non-symmetric, additive properties. This technique yields near-linear-time computable estimators whose approximation values are asymptotically optimal and highly-concentrated, resulting in the first: 1) estimators achieving the $\mathcal{O}(k/(\varepsilon^2\log k))$ min-max $\varepsilon$-error sample complexity for all $k$-symbol Lipschitz properties; 2) unified near-optimal differentially private estimators for a variety of properties; 3) unified estimator achieving optimal bias and near-optimal variance for five important properties; 4) near-optimal sample-complexity estimators for several important symmetric properties over both domain sizes and confidence levels.
Saliency-driven system models for cell analysis with deep learning

Background and objectives: Saliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert’s visual attention, there is little evidence about how these models perform when used to predict the cytopathologist’s eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions. This paper describes how our computational schemes retrieve regions of interest (ROI) of clinical relevance using visual attention models. We also compare the performance of different computed saliency models as part of cell screening tasks, aiming to design a computer-aided diagnosis systems that supports cytopathologists. Method: We record eye fixation maps from cytopathologists at work, and compare with 13 different saliency prediction algorithms, including deep learning. We develop cell-specific convolutional neural networks (CNN) to investigate the impact of bottom-up and top-down factors on saliency prediction from real routine exams. By combining the eye tracking data from pathologists with computed saliency models, we assess algorithms reliability in identifying clinically relevant cells. Results:The proposed cell-specific CNN model outperforms all other saliency prediction methods, particularly regarding the number of false positives. Our algorithm also detects the most clinically relevant cells, which are among the three top salient regions, with accuracy above 98% for all diseases, except carcinoma (87%). Bottom-up methods performed satisfactorily, with saliency maps that enabled ROI detection above 75% for carcinoma and 86% for other pathologies. Conclusions:ROIs extraction using our saliency prediction methods enabled ranking the most relevant clinical areas within the image, a viable data reduction strategy to guide automatic analyses of Pap smear slides. Top-down factors for saliency prediction on cell images increases the accuracy of the estimated maps while bottom-up algorithms proved to be useful for predicting the cytopathologist’s eye fixations depending on parameters, such as the number of false positive and negative. Our contributions are: comparison among 13 state-of-the-art saliency models to cytopathologists’ visual attention and deliver a method that the associate the most conspicuous regions to clinically relevant cells.
ACPNP: an Efficient Solution for Absolute Camera Pose Estimation from Two Affine Correspondences

In this paper, a novel algorithm to estimate the absolute camera pose is proposed using two affine correspondences (ACs). Exploring the relationship between the affine transformation and the projection equation, six linear constraints are derived, and only two ACs are sufficient to recover the pose. Even though perspective cameras are assumed, the constraints can straightforwardly be generalized to other camera models since they describe the relationship between local affinities and projection. Benefiting from the requirement of less correspondences, the proposed algorithm needs less sampling times when robust estimators like RANSAC are applied, and still performs stably with rather limited number of correspondences. For the improvement of robustness, the affine transformation is further optimized via photometric and epipolar constraints. The proposed method was validated on both synthetic and real-world datasets, which demonstrates that the proposed method yields results superior to the state-of-the-art in terms of accuracy.
Design of a Web System of Learning Evaluation of Students with Visual Disability by Voice Recognition

In the actuality, exists a variety of applications of the information and communication technologies for the educational ambit. Nevertheless, is required to attend students with special educative necessities. The objective is to present the design, development and implementation of a web system as an inclusive tool for people with visual disability to support the learning evaluation in courses of high education, achieving a digital transformation of the educative processes and services for in the education 4.0. The methodology utilized was the inclusive design centered in the user and the implementation uses PHP, MySQL, JavaScript, voice libraries. The tests of functionality and usability were realized in a focal group of students with visual disability.