Reviewerly/exHarmony huggingface dataset:
An Analysis of Teacher’s Speech Acts in Teaching and Learning Process

This research focuses on classroom interaction during teaching and learning process that included investigation of types and functions of the speech acts produced by teacher. It was designed as a qualitative research. The subject of this research was an English teacher in SMAN 1 Kalirejo. The data were collected through recording. The data were analyzed through three cyclical steps: data reduction, data display, and conclusion drawing or verification. The results show that directives speech acts is mostly produced by the teacher than the other speech acts. This can be seen from the data that 33 utterances (21.2%) are representative speech acts, 6 utterances (3.8%) as expressive speech acts, 112 utterances (71.8%) as directives speech acts, and 5 utterances (3.2%) as commisive speech acts. The types and functions of directives speech acts proposed are need statement, bald imperative, embedded imperative, permission directive, and question directive.
Fashion Attributes-to-Image Synthesis Using Attention-Based Generative Adversarial Network

In this paper, we present a method to generate fashion product images those are consistent with a given set of fashion attributes. Since distinct fashion attributes are related to different local sub-regions of a product image, we propose to use generative adversarial network with attentional discriminator. The attribute-attended loss signal from discriminator leads generator to generate more consistent images with given attributes. In addition, we present a generator based on Product-of-Gaussian to encode the composition of fashion attributes in effective way. To verify the proposed model whether it generates consistent image, an oracle attribute classifier is trained and judge the consistency of given attributes and the generated images. Our model significantly outperforms the baseline model in terms of correctness measured by the pre-trained oracle classifier. We show not only qualitative performance but also synthesized images with various combinations of attributes, so we can compare them with baseline model.
Detection of squamous cell carcinoma in digitized histological images from the head and neck using convolutional neural networks

Primary management for head and neck squamous cell carcinoma (SCC) involves surgical resection with negative cancer margins. Pathologists guide surgeons during these operations by detecting SCC in histology slides made from the excised tissue. In this study, 192 digitized histological images from 84 head and neck SCC patients were used to train, validate, and test an inception-v4 convolutional neural network. The proposed method performs with an AUC of 0.91 and 0.92 for the validation and testing group. The careful experimental design yields a robust method with potential to help create a tool to increase efficiency and accuracy of pathologists for detecting SCC in histological images.
The Efficacy of Using Liulishuo for Spoken English Ability—Taking the Practice of Liulishuo in North China Electric Power University (Baoding) as an Example

This research aims to verify the effectiveness and practicability of Liulishuo, an English-learning APP, in improving college students’ oral English. The research adopts different student groups according to their different listening levels, and compares the scores of the pre- and post-practice tests of the control groups and the feedback analysis of the two sessions. The research results showed an upward trend, which proved the efficacy and practicability of Liulishuo in improving students’ oral English.
HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS

The problem of high utility sequence mining (HUSM) in quantitative se-quence databases (QSDBs) is more general than that of frequent sequence mining in se-quence databases. An important limitation of HUSM is that a user-predened minimum tility threshold is used commonly to decide if a sequence is high utility. However, this is not convincing in many real-life applications as sequences may have diferent importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, collected data such as by sensor maybe uncertain. Thus, this paper proposes a framework for mining high utility-probability sequences (HUPSs) in uncertain QSDBs (UQS-DBs) with multiple minimum utility thresholds using a minimum utility. Two new width and depth pruning strategies are also introduced to early eliminate low utility or low probability sequences as well as their extensions, and to reduce sets of candidate items for extensions during the mining process. Based on these strategies, a novel ecient algorithm named HUPSMT is designed for discovering HUPSs. Finally, an experimental study conducted in both real-life and synthetic UQSDBs shows the performance of HUPSMT in terms of time and memory consumption.
GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment

mathematical details is covered in Section III.Details about the interactions are covered in Section IV.Implementation with evaluation details is explained in Section V. A comparative analysis is performed in Section VI whereas applicability in games is investigated in Section VII.The last section of the paper is about conclusion and future work.
The Effect of Explicit English Morphology Instruction on EFL Secondary School Students’ Morphological Awareness and Reading Comprehension

The study attempted to investigate the effect of explicit morphology instruction (EMI) on developing secondary school students&amp;rsquo; EFL morphological awareness and reading comprehension. The explicit morphology instruction targeted two morphological skills namely, inflectional and derivational skills. The study used a pre-posttest experimental and control group design. The intact study participants were (98) first year secondary school students. While the first intact group (n=49) was functioned as an experimental group, the second intact group (n=49) represented the control group. To collect the data, a two-unit explicit morphology instruction program (EMIP), a morphological awareness test (MAT) and a reading comprehension test (RCT) were designed, validated and implemented. Before the intervention, the participants&amp;rsquo; morphological awareness and reading comprehension were pre-tested. During the course of intervention, while the experimental group participants were exposed to explicit morphological instruction in addition to their regular English instruction sessions, the participants of the control group only received their regular EFL instruction sessions. Results revealed that the experimental group participants&amp;rsquo; mean scores on the post morphological awareness test and reading comprehension test surpassed that of the control group. Accordingly, explicit morphological instruction was effective in developing EFL secondary school students&amp;rsquo; morphological awareness and reading comprehension. However, the effect size of explicit morphological instruction on developing EFL secondary school students&amp;rsquo; morphological awareness was higher than its effect size on developing their reading comprehension. Therefore, teaching English morphology should be an integral part of EFL secondary school curriculum.
Towards better volcanic risk-assessment systems by applying ensemble classification methods to triaxial seismic-volcanic signals

Analysis of seismic data is the most important method for volcano monitoring. Such data typically consists in digital signals acquired with an arrangement of triaxial seismic sensors which are strategically deployed on the volcano and its surrounding areas. Very rich measurements of the underlying phenomena are obtained from the arrangement because each sensor, through their corresponding three sensing axes, uninterruptedly acquires data at a relatively high sampling rate. Such an uninterrupted acquisition, however, turns manual classification of seismic signals into an inefficient and often error-prone task. As a solution, several systems for automated classification of seismic-volcanic signals have been proposed. All these systems, however, are limited to the usage of only one direction of acquisition; typically the vertical one. In this paper we make a step forward, exploring the potential benefit of using information from the three axes of the signals gathered by a single sensor. Integration is performed by classifier combining techniques, applied at different levels: this permits to take into account in the classification all the three orthogonal orientations —vertical, East-West and North-South— of the phenomenon. Preliminary experimental results on a set of volcanic signals gathered at Nevado del Ruiz volcano in Colombia confirmed the richness of this information.
Efficient Embedded Images in Portable Document Format (PDF)

The use of facsimile machines is gradually declined, because the email slowly but surely takes its function; however, the use of the facsimile format is still supported by the well-known format -Portable Document Format (PDF).In spite of that, PDF commonly prefers to use JPEG over using the facsimile format.This can be suitable for many images; however, when the image is a document, preferring JPEG over the facsimile format will not yield high-quality outcomes.In this paper we explain why the facsimile format can do better when an embedded image of document is enclosed in a PDF file.
Overview of Licensing Platforms based on Distributed Ledger Technology

The licensing of creative work is of broad and current interest.The European Commission proposes that when uploading a licensed digital work, the uploader should be checked by the system that one has the necessary rights.Technically this law is difficult to implement, as images with different intentions are shared, and even small changes like watermarks make it difficult to reveal similarities.The characteristics of distributed ledger technology could provide excellent support for the licensing and management of the rights of use.In this work, non-technical and technical criteria are defined to achieve an overview of the state-of-the-art solutions in the field of blockchain-based licensing platforms.Based on the criteria, different licensing platforms are reviewed, and the results are presented in a comparison matrix.
An Empirical Study of Home User Intentions towards Computer Security

Home computer users are solely responsible for implementing security measures on their devices.Although most computers have security software installed, the potential remains for security breaches, which makes it important for home users to take additional steps, such as not sharing one's password and using strong passwords, to secure their devices further.Drawing on protection motivation theory and findings from prior research, this study evaluates factors that influence individuals to implement additional security measures to protect their home computers.Using SmartPLS and responses from 72 home computer users, the results show that response efficacy, self-efficacy and subjective norms were significant in encouraging persons to implement additional security measures.Maladaptive rewards on the other hand acted as a significant detractor, while neither perceived vulnerability nor perceived severity was significant in relation to willingness to implement additional security measures.
I Understand What You Are Saying: Leveraging Deep Learning Techniques for Aspect Based Sentiment Analysis

Despite widespread use of online reviews in consumer purchase decision making, the potential value of online reviews in facilitating digital collaboration among product/service providers, consumers, and online retailers remains under explored.One of the significant barriers to realizing the above potential lies in the difficulty of understanding online reviews due to their sheer volume and free-text form.To promote digital collaborations, we investigate aspect based sentiment dynamics of online reviews by proposing a semi-supervised, deep learning facilitated analytical pipeline.This method leverages deep learning techniques for text representation and classification.Additionally, building on previous studies that address aspect extraction and sentiment identification in isolation, we address both aspects and sentiments analyses simultaneously.Further, this study presents a novel perspective to understanding the dynamics of aspect based sentiments by analyzing aspect based sentiment in time series.The findings of this study have significant implications with regards to digital collaborations among consumers, product/service providers and other stakeholders of online reviews.
RGB-D action recognition based on discriminative common structure learning model

The emergence of low-cost depth cameras creates potential for RGB-D based human action recognition. However, most of the existing RGB-D based approaches simply concatenate original heterogeneous features without discovering the latent relations among different modalities. We propose a discriminative common structure learning (DCSL) model for human action recognition from RGB-D sequences. Specifically, we extract deep learning-based features and hand-crafted features from multimodal data (skeleton, depth, and RGB). In particular, we propose a deep architecture based on 3-D convolutional neural network to automatically extract deep spatiotemporal features from raw sequences. The proposed DCSL model utilizes a generalized version of collective matrix factorization to learn shared features among different modalities. To perform supervised learning and preserve intermodal similarity, we formulate a graph regularization term by considering both label information and similar geometric structure of multimodal data, which intends to improve the discriminative power of shared features. Moreover, we solve the objective function using an iterative optimization algorithm. Then, an improved collaborative representation classifier is employed to perform computationally efficient action recognition. Experimental results on four action datasets demonstrate the superior performance of the proposed method.
Immersive Ars Memoria: Evaluating the Usefulness of a Virtual Memory Palace

The Method of Loci (also memory palace) is the most powerful mnemonic strategy and was widely analyzed over the last twenty years.Especially, the approach to combine this ancient learning method with modern technology got more and more into the focus of an interdisciplinary research community.Researchers presented their students virtual environments via computer screen or head-mounted displays and instructed them to use these virtual worlds as a template for a memory palace.However, most studies did not investigate the users' attitude to actually use such a tool in everyday situations.This study addresses this research gap by an experiment and a correlation and regression analysis.Results show significant correlations between the learning success and important factors of the users intention to use a virtual memory palace.
A Software Tool Aimed at Automating the Generation, Distribution, and Assessment of Social Media Messages for Health Promotion and Education Research

Background: Social media offers promise for communicating the risks and health effects of harmful products and behaviors to larger and hard-to-reach segments of the population.Nearly 70% of US adults use some social media.However, rigorous research across different social media is vital to establish successful evidence-based health communication strategies that meet the requirements of the evolving digital landscape and the needs of diverse populations. Objective:The aim of this study was to expand and test a software tool (Trial Promoter) to support health promotion and education research by automating aspects of the generation, distribution, and assessment of large numbers of social media health messages and user comments. Methods:The tool supports 6 functions (1) data import, (2) message generation deploying randomization techniques, (3) message distribution, (4) import and analysis of message comments, (5) collection and display of message performance data, and (6) reporting based on a predetermined data dictionary.The tool was built using 3 open-source software products: PostgreSQL, Ruby on Rails, and Semantic UI.To test the tool's utility and reliability, we developed parameterized message templates (N=102) based upon 2 government-sponsored health education campaigns, extracted images from these campaigns and a free stock photo platform (N=315), and topic-related hashtags (N=4) from Twitter.We conducted a functional correctness analysis of the generated social media messages to assess the algorithm's ability to produce the expected output for each input.We defined 100% correctness as use of the message template text and substitution of 3 message parameters (ie, image, hashtag, and destination URL) without any error.The percent correct was calculated to determine the probability with which the tool generates accurate messages. Results:The tool generated, distributed, and assessed 1275 social media health messages over 85 days (April 19 to July 12, 2017).It correctly used the message template text and substituted the message parameters 100% (1275/1275) of the time as verified by human reviewers and a custom algorithm using text search and attribute-matching techniques. Conclusions:A software tool can effectively support the generation, distribution, and assessment of hundreds of health promotion messages and user comments across different social media with the highest degree of functional correctness and minimal human interaction.The tool has the potential to support social media-enabled health promotion research and practice: first, by enabling the assessment of large numbers of messages to develop evidence-based health communication, and second, by providing public
Empirical Assessment of Ensemble based Approaches to Classify Imbalanced Data in Binary Classification

Classifying imbalanced data with traditional classifiers is a huge challenge now-a-days.Imbalance data is a situation wherein the ratio of data within classes is not same.Many real life situations deal with such problems e.g.Web spam detection, Credit card frauds, and fraudulent telephone calls.The problem exists everywhere when our objective is to identify exceptional cases.The problem is handled by researchers either by modifying the existing classifications methods or by developing new methods.This paper review ensemble based approaches (Boosting and Bagging based) designed to address imbalance in classes by focusing on binary classification.We compared 6 Boosting based, 7 Bagging based and 2 hybrid ensembles for their performance in imbalance domain.We use KEEL tool to evaluate the performance of these methods by implementing the methods on seven imbalance data having class imbalance ratio from 1.82 to as high as 129.44.Area Under the curve (AUC) parameter is recorded as the performance metric.We also statistically analyzed the methods using Friedman rank test and Wilcoxon Matched Pair signed rank test to strengthen the visual interpretations.After analysis, it is proved that RusBoost ensemble outperformed every other ensemble in the imbalanced data situations.
Cursive multilingual characters recognition based on hard geometric features

The cursive nature of Arabic script characters segmentation and recognition has attracted researchers from academia and industry. However, despite several decades of research, still Arabic characters classification accuracy is not up to the mark. This paper presents an automated approach for Arabic characters segmentation and recognition. The proposed methodology explores character's boundaries based on their geometric features, prior to their recognition. However, due to uncertainty and without dictionary support few characters are over-divided. To expand the productivity of the proposed methodology a hybrid neural model (HNN) is proposed. The model is composed of trained MLP and RBF networks to assist the character's boundary recognition process. For reasonable examination, only benchmark dataset is utilised.
Improved Hunting Search Algorithm for the Quadratic Assignment Problem

&lt;p&gt;Nowadays, the metaheuristics are the most studied methods used to solve the hard optimization problems. Hunting Search algorithm is a metaheuristic inspired by the method of group hunting of predatory animals like wolves. Created for solving continuous optimization problems, recently, it is adapted and evaluated to solve hard combinatorial optimization problems. This paper proposes an improved hunting search algorithm to solve the quadratic assignment problem. No local search method is used. To evaluate the performances of this work, the improved Hunting Search is checked on a set of 36 instances of QAPLib and it outperforms the results obtained by the well-known metaheuristics.&lt;/p&gt;
LINE Messenger as a Transport Layer to Distribute Messages to Partner Instant Messaging

The rapid development of technology generated a lot of instant messaging applications, as an example is LINE Messenger, Telegram, WhatsApp, Facebook Messenger, BlackBerry Messenger, etc.Many instant messaging applications cause some problems, one of which is the possibility of owning more than one instant messaging applications to meet messaging needs.This is very ineffective because messages will be received from various instant messaging applications and have to open all instant messaging applications one by one to reply to those messages.Application Programming Interface technology of instant messaging can be used to build a transport layer system that allows message exchange with several instant messaging applications using only one instant messaging application.The system built in this research requires a master instant messaging to be able to distribute messages to partner instant messaging, which in this case uses LINE Messenger as the master instant messaging to distribute messages to several partner instant messaging, which is Telegram and Extensible Messaging Presence Protocol.
NEED ANALYSIS OF ENGLISH FOR TOUR AND TRAVEL DEPARTMENT OF VOCATIONAL HIGH SCHOOL IN BENGKULU CITY

This research aimed to investigate the English needs of Tour and Travel Department at Vocational High School Negeri 1 and Vocational High School Negeri 7 in Bengkulu City. The research used mixed method and convergent parallel design. The sample of this research consisted of three groups of respondents. First group was students, consisted of 28 active students and 4 alumni. Second group was English teachers, consisted of 2 English teachers. The third group was stakeholders, consisted of two heads of the Tour and Travel Department, two assistances of curriculum, hotel, and tour staffs. The data of this research were gathered by using adapted questionnaire and interview. The questionnaire was analyzed quantitatively and interview was analyzed qualitatively. The data were combined by using convergent parallel design. Results of this research indicated that listening and speaking are the two prioritized skills needed by the tour and travel department. Topics of speaking which has the greatest importance are flight reservation, prices and payment, tourist destination, tourist attraction, direction, and booking hotels. Topics of listening which has the greatest importance are flight reservation, booking hotels, direction, job interview, complaints, and cultural tourism. Topics of reading which has the greatest importance are memos, letters, cultural tourism, handling a complaint, and tourism terms. Topics of writing which has the greatest importance are rules and regulation, instruction.