Reviewerly/exHarmony huggingface dataset:
Improvement of Exposition Text Writing Motivation and Skills Through the Application of the Problem Based Learning Model

This study aims is to improve: (1) student learning motivation; and (2) students' writing skills in writing exposition texts of class VIII on State of Junior High School 3 Surakarta through the application of the Problem Based Learning model. This type of research is Class Action Research (CAR). This research was conducted in three cycles. This research was conducted in State of Junior High School 3 Surakarta in the first semester of the 2018/2019 academic year. This research was conducted in five months, starting from July to November 2018. The subjects of this study were students of class VIII- 9 in the first semester of the 2018/2019 academic year with a total of 32 students. The method of data collection is done by using tests, observations, questionnaires and document techniques. Data analysis techniques are carried out using critical analysis. Based on the analysis, the study concluded that: (1) the application of the Problem Based Learning model was able to increase students' motivation in learning Indonesian. This is indicated by an increase in the average score of learning motivation and the number of students with a learning motivation score of classification A (Very Good) and B (Good) in each cycle performed; and (2) the application of the Problem Based Learning model is able to improve students' writing skills in writing exposition texts of class VIII SMP Negeri 3 Surakarta in the first semester of the academic year 2018/2019. This is indicated by an increase in the average student score and the level of student mastery throughout the cycle. The average score of students' writing skills in exposition text writing, increased from 63.09 in the initial conditions, increased to 73.00 at the end of the first cycle, increased to 76.78 at the end of the second cycle, and then increased to 80.22 at the end from Cycle III. The level of mastery of student learning increased from 12.50% in the initial conditions, increasing to 43.75% at the end of the first cycle, increasing to 68.75% at the end of the second cycle, then increasing to 100.00% at the end of cycle III.
Circumventing Cryptographic Deniability with Remote Attestation

Abstract Deniable messaging protocols allow two parties to have ‘off-the-record’ conversations without leaving any record that can convince external verifiers about what either of them said during the conversation. Recent events like the Podesta email dump underscore the importance of deniable messaging to politicians, whistleblowers, dissidents and many others. Consequently, messaging protocols like Signal and OTR are designed with cryptographic mechanisms to ensure deniable communication, irrespective of whether the communications partner is trusted. Many commodity devices today support hardware-assisted remote attestation which can be used to convince a remote verifier of some property locally observed on the device. We show how an adversary can use remote attestation to undetectably generate a non-repudiable transcript from any deniable protocol (including messaging protocols) providing sender authentication, proving to skeptical verifiers what was said. We describe a concrete implementation of the technique using the Signal messaging protocol. We then show how to design protocols that are deniable even against an adversary capable of attestation, and in particular how attestation itself can be used to restore deniability by thwarting realistic classes of adversary.
On Value Functions and the Agent-Environment Boundary

When function approximation is deployed in reinforcement learning (RL), the same problem may be formulated in different ways, often by treating a pre-processing step as a part of the environment or as part of the agent. As a consequence, fundamental concepts in RL, such as (optimal) value functions, are not uniquely defined as they depend on where we draw this agent-environment boundary, causing problems in theoretical analyses that provide optimality guarantees. We address this issue via a simple and novel boundary-invariant analysis of Fitted Q-Iteration, a representative RL algorithm, where the assumptions and the guarantees are invariant to the choice of boundary. We also discuss closely related issues on state resetting and Monte-Carlo Tree Search, deterministic vs stochastic systems, imitation learning, and the verifiability of theoretical assumptions from data.
Fraud detection models and payment transactions analysis using machine learning

The work’s aim is to research a set of selected mathematical models and algorithms that examine the data of a single payment transaction to classify it as fraud or verified. Described models are implemented in the form of a computer code and algorithms, and therefore can be executed in real-time. The main objective is to apply different methods of machine learning to find the most accurate, in other words, the one in which the cross-validation score is maximal. Thus, the main problem to resolve is the creation of a model that could instantly detect and block a given fraudulent transaction in order to provide better security and user experience. At first, we determine the classification problem: which initial data we have, how we can interpreter it to find the solution. The next part is dedicated to presenting the methods for solving the classification problem. In particular, we describe such approaches as Logistic Regression, Support Vectors Method (SVM), K-Nearest neighbours, Decision Tree Classifier and Artificial Neural Networks; provide the notion of how these methods operate the data and yield the result. At the end, we apply these methods to the provided data using Python programming language and analyze the results.
Model Similarity Mitigates Test Set Overuse

Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds. Nonetheless, recent replication studies give evidence that popular benchmarks continue to support progress despite years of extensive reuse. We proffer a new explanation for the apparent longevity of test data: Many proposed models are similar in their predictions and we prove that this similarity mitigates overfitting. Specifically, we show empirically that models proposed for the ImageNet ILSVRC benchmark agree in their predictions well beyond what we can conclude from their accuracy levels alone. Likewise, models created by large scale hyperparameter search enjoy high levels of similarity. Motivated by these empirical observations, we give a non-asymptotic generalization bound that takes similarity into account, leading to meaningful confidence bounds in practical settings.
An Empirical Study on Hyperparameters and their Interdependence for RL Generalization

Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters include architecture, regularization, and RL-dependent variables such as discount factor and action stochasticity. We provide empirical results that show complex and interdependent relationships between hyperparameters and generalization. We further show that several empirical metrics such as gradient cosine similarity and trajectory-dependent metrics serve to provide intuition towards these results.
Automated Measurement of Technical Debt: A Systematic Literature Review

Background: Technical Debt (TD) is a quite complex concept that includes several aspect of software development. Often, people talk about TD as the amount of postponed work but this is just a basic approximation of the concept that includes many aspects that are technical and managerial. If TD is managed properly, it can provide a huge advantage but it can also make projects unmaintainable, if not. Therefore, being able of measuring TD is a very important aspect for a proper management of the development process. However, due to the complexity of the concept and the different aspects that are involved, such measurement it not easy and there are several different approaches in literature. Goals: This work aims at investigating the existing approaches to the measurement and the analysis of TD focusing on quantitative methods that could also be automated. Method: The Systematic Literature Review (SLR) approach was applied to 331 studies obtained from the three largest digital libraries and databases. Results: After applying all filtering stages, 21 papers out of 331 were selected and deeply analyzed. The majority of them suggested new approaches to measure TD using different criteria not built on top of existing ones. Conclusions: Existing studies related to the measurement of TD were observed and analyzed. The findings have shown that the field is not mature and there are several models that have almost no independent validation. Moreover few tools for helping to automate the evaluation process exist.
Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması

The objective of this study was to develop a comprehensive Sentiment Analysis Lexicon for Turkish language.The aim of this new lexicon is to facilitate effective sentiment analysis on Turkish social media posts.Previously developed lexicons have been examined and a comprehensive lexicon extending previous work has been developed.As an extension to the earlier work, the lexicon consist simple emoji characters and scoring infrastructure.To evaluate the performance of the Turkish Sentiment Analysis Lexicon, the tweets with certain hashtags were collected and analyzed.Analysis studies were carried out on two different topics.The first of these studies was done to measure the effect of weather changes on users.The data collected during the summer and winter were examined.The users who share Turkish post negative tweets in winter and appeared to share more positive posts in spring and summer compared to winter.The other analysis was conducted on Survivor TV Show, which has been aired on Turkish TVs, and placed at the top of the Trend Topic list for days.We observed that the users share negative posts about this program.Additionally, the habit of improper use of Turkish language on social media was noted in their posts.
AdaOja: Adaptive Learning Rates for Streaming PCA

Oja's algorithm has been the cornerstone of streaming methods in Principal Component Analysis (PCA) since it was first proposed in 1982. However, Oja's algorithm does not have a standardized choice of learning rate (step size) that both performs well in practice and truly conforms to the online streaming setting. In this paper, we propose a new learning rate scheme for Oja's method called AdaOja. This new algorithm requires only a single pass over the data and does not depend on knowing properties of the data set a priori. AdaOja is a novel variation of the Adagrad algorithm to Oja's algorithm in the single eigenvector case and extended to the multiple eigenvector case. We demonstrate for dense synthetic data, sparse real-world data and dense real-world data that AdaOja outperforms common learning rate choices for Oja's method. We also show that AdaOja performs comparably to state-of-the-art algorithms (History PCA and Streaming Power Method) in the same streaming PCA setting.
Kinect-Based Motion Recognition Tracking Robotic Arm Platform

The development of artificial intelligence technology has promoted the rapid improvement of human-computer interaction.This system uses the Kinect visual image sensor to identify human bone data and complete the recognition of the operator's movements.Through the filtering process of real-time data by the host computer platform with computer software as the core, the algorithm is programmed to realize the conversion from data to control signals.The system transmits the signal to the lower computer platform with Arduino as the core through the transmission mode of the serial communication, thereby completing the control of the steering gear.In order to verify the feasibility of the theory, the team built a 4-DOF robotic arm control system and completed software development.It can display other functions such as the current bone angle and motion status in real time on the computer operation interface.The experimental data shows that the Kinect-based motion recognition method can effectively complete the tracking of the expected motion and complete the grasping and transfer of the specified objects, which has extremely high operability.
Chinese Movie Comment Sentiment Analysis Based on HowNet and User Likes

Abstract Emotional information in movie comments is critical to sentiment analysis, Sentiment analysis, which focuses on classify the comments into positive class and negative class according to sentiment lexicon, is one of the studies. Most of the existing researches are centered on sentiment words and user rating, while the user’s attitude towards comments are ignored. And, considering that Chinese is the second largest language in the world. In this paper, in order to get this point to be considered, we propose a method for Chinese movie comments sentiment analysis based on HowNet and user likes which we called HAL. Our research consists of four parts. First, we use HowNet sentiment lexicon to get a new lexicon in the field of movies. Second, we use the new lexicon and word segmentation tool named Jieba to segment the movie comments. Third, we use the user likes and sentiment words to get the positive feature and negative feature. Finally, we train the movie comment data using three models (SVC, LinearSVC, LogisticRegression). The experimental results show that our method performs better than HowNet-based method in Chinese movie comment sentiment analysis.
Pupil diameter as a measure of emotion and sickness in VR

Eye tracking is rapidly becoming popular in consumer technology, including virtual and augmented reality. Eye trackers commonly provide an estimate of gaze location, and pupil diameter. Pupil diameter is useful for interactive systems, as it provides means to estimate cognitive load, stress, and emotional state. However, there are several roadblocks that limit the use of pupil diameter. In VR HMDs there are a lack of models that account for stereoscopic viewing and the increased brightness of near eye displays. Existing work has shown correlations between pupil diameter and emotion, but have not been extended to VR environments. The scope of this work is to bridge the gap between existing research on emotion and pupil diameter to VR, while also attempting to use pupillary data to tackle the problem of simulator sickness in VR.
Error Analysis in the Descriptive Text Writing of Vocational High School Students

English is one of the subjects that has been studied at the elementary to secondary level for many years in Indonesia. One area that must be studied is technical writing skills in English. In the technical writing process in English, there are still many mistakes made by students in Indonesia. So it is very necessary to recognize some common mistakes made by students, to help them improve their teaching and learning strategies in English and improve their learning outcomes. Therefore, this study aims to investigate errors in English technical writing of students at secondary vocational level and the factors that cause these errors. This study focused on errors made by the eleventh grade students of State 10 Vocational High School Jakarta, in their written descriptive text. A total of 30 students were the subjects of this study and they were asked to write descriptive texts. Data is analyzed based on Procedure Analysis This descriptive qualitative study explains and investigates student errors in writing the technique and the causative factors. Research shows that the typical mistakes students make in their writing: the highest place is capitalization with 60 errors (14.67%) and the lowest place is sentence 3 error (0.73%). According to a typical error, the author found the source of the error. The highest place is transfer between languages with 172 errors (42.05%) and the lowest place is a communication strategy with 49 errors (11.98%).
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder

Using powerful posterior distributions is a popular approach to achieving better variational inference. However, recent works showed that the aggregated posterior may fail to match unit Gaussian prior, thus learning the prior becomes an alternative way to improve the lower-bound. In this paper, for the first time in the literature, we prove the necessity and effectiveness of learning the prior when aggregated posterior does not match unit Gaussian prior, analyze why this situation may happen, and propose a hypothesis that learning the prior may improve reconstruction loss, all of which are supported by our extensive experiment results. We show that using learned Real NVP prior and just one latent variable in VAE, we can achieve test NLL comparable to very deep state-of-the-art hierarchical VAE, outperforming many previous works with complex hierarchical VAE architectures.
Automatic Detection of Clear-Sky Periods From Irradiance Data

Recent degradation studies have highlighted the importance of considering cloud cover when calculating degradation rates, finding more reliable values when the data are restricted to clear sky periods. Several automated methods of determining clear sky periods have been previously developed, but parameterizing and testing the models has been difficult. In this paper, we use clear sky classifications determined from satellite data to develop an algorithm that determines clear sky periods using only measured irradiance values and modeled clear sky irradiance as inputs. This method is tested on global horizontal irradiance (GHI) data from ground collectors at six sites across the United States and compared against independent satellite-based classifications. First, 30 separate models were optimized on each individual site at GHI data intervals of 1, 5, 10, 15, and 30 min (sampled on the first minute of the interval). The models had an average F0.5 score of 0.949 ± 0.035 on a holdout test set. Next, optimizations were performed by aggregating data from different locations at the same interval, yielding one model per data interval. This paper yielded an average F0.5 of 0.946 ± 0.037. A final, “universal” optimization that was trained on data from all sites at all intervals provided an F0.5 score of 0.943 ± 0.040. The optimizations all provide improvements on a prior, unoptimized clear sky detection algorithm that produces F0.5 scores that average to 0.903 ± 0.067. Our paper indicates that a single algorithm can accurately classify clear sky periods across locations and data sampling intervals.
Towards a Privacy Compliant Cloud Architecture for Natural Language Processing Platforms

Natural language processing in combination with advances in artificial intelligence is on the rise. However, compliance constraints while handling personal data in many types of documents hinder various application scenarios. We describe the challenges of working with personal and particularly sensitive data in practice with three different use cases. We present the anonymization bootstrap challenge in creating a prototype in a cloud environment. Finally, we outline an architecture for privacy compliant AI cloud applications and an anonymization tool. With these preliminary results, we describe future work in bridging privacy and AI.
A Methodology for Engineering Domain Ontology using Entity Relationship Model

Ontology engineering is an important aspect of semantic web vision to attain the meaningful representation of data.Although various techniques exist for the creation of ontology, most of the methods involve the number of complex phases, scenario-dependent ontology development, and poor validation of ontology.This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model.Firstly, a detailed analysis of intended domain is performed to develop the ER model.In the next phase, ER to ontology (EROnt) conversion rules are outlined, and finally the system prototype is developed to construct the ontology.The proposed approach investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology.The experts' evaluation of accurate identification of ontology vocabulary shows that the method performed well on curriculum data with 95.75% average precision and 90.75% average recall.
Methodology of a marketing research of Internet users

Strengthening of a trend of development in Russia of internet marketing and, as a result, increase of relevance of carrying out highly professional marketing researches in the web environment has been considered. The article is aimed at identification of methodological specifics of a marketing research of Internet users, development of its concept. The stated goal is achieved by means of the solution of the following tasks: the analysis and systematization of the goals of the marketing research of the Internet users; identification and systematization of the empirical indicators, which are necessary for the research of the Internet users; the analysis and systematization of methods, techniques, the technician and procedures of a research of empirical indicators, including, special services, corresponding to methods of collecting primary empirical data in the web environment. The analysis of such methods of the marketing empirical research of the Internet users as a usability testing, an online experiment, a game experiment, a usability experiment, online questioning, online interviewing, including an individual deep online interview, a group deep online interview (online focus group), the content analysis of web messages of consumers, the analysis of traces, including analysis of site statistics, has been presented. Meanwhile, the attention is focused on observance of the necessity of full subordination of each of the presented techniques to the methodological principles of the corresponding basic method, which derivative it is, because violation of the specified requirement leads to inevitable vulgarization of scientific methodology of carrying out an empirical research and obtaining incorrect data. From the point of view of the author, an application by the marketing analyst of the concept of a research and research tools, presented in the article, in the course of the organization and carrying out market researches of the Internet users allows to obtain information, necessary for creation of effective marketing web communication and also to find and in due time to prevent potential and real communicative failures.