Reviewerly/exHarmony huggingface dataset:
Modified PPO-RND Method for Solving Sparse Reward Problem in ViZDoom

ViZDoom is an infamous first-person shooter game. Several studies have been conducted to develop agents that can automatically complete game tasks using a reinforcement learning algorithm. Although these studies yielded substantial progress, models proposed by the previous studies when applied to the "my way home" scenario in ViZDoom presented two problems. The first one is that when an agent walks into a specific room, it appears to be immobile and although it does not move until the time ends, the view constantly changes from left to right. The second problem is the slow learning speed of the model. To address these issues, a time penalty method and a modified neural network construction method are proposed in this study. The experimental results demonstrate that the addition of a time penalty improved the learning rate by 40% compared to the methods in which time penalty was not added. Moreover, the models proposed in previous studies could complete only 73% to 85% of the tasks, whereas the method proposed herein can complete 100% of the tasks.
Principles of Learning

Machine learning is, for the most part, sub-divided into three components based on the approach to the learning problem. The three predominant categories of learning are the supervised, unsupervised, and reinforcement learning schemes. In this chapter, we will go over supervised learning schemes in detail and also touch upon unsupervised and reinforcement learning schemes to a lesser extent.
Semi-Finite Length Analysis for Secure Random Number Generation

To discuss secure key generation from imperfect random numbers, we address the secure key generation length. There are several studies for its asymptotic expansion up to the order $\sqrt n $ or log n. However, these expansions have errors of the order $o\left( {\sqrt n } \right)$ or o(logn), which does not go to zero asymptotically. To resolve this problem, we derive the asymptotic expansion up to the constant order for upper and lower bounds of these optimal values. While the expansions of upper and lower bonds do not match, they clarify the ranges of these optimal values, whose errors go to zero asymptotically.
Scalable Information Flow Analysis of Secure Three-Party Affine Computations

Elaborate protocols in Secure Multi-party Computation enable several participants to compute a public function of their own private inputs while ensuring that no undesired information leaks about the private inputs, and without resorting to any trusted third party. However, the public output of the computation inevitably leaks some information about the private inputs. Recent works have introduced a framework and proposed some techniques for quantifying such information flow. Yet, owing to their complexity, those methods do not scale to practical situations that may involve large input spaces. The main contribution of the work reported here is to formally investigate the information flow captured by the min-entropy in the particular case of secure three-party computations of affine functions in order to make its quantification scalable to realistic scenarios. To this end, we mathematically derive an explicit formula for this entropy under uniform prior beliefs about the inputs. We show that this closed-form expression can be computed in time constant in the inputs sizes and logarithmic in the coefficients of the affine function. Finally, we formulate some theoretical bounds for this privacy leak in the presence of non-uniform prior beliefs.
Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models

Prediction is one of the most important tasks in the machine learning field. Data scientists employ various learning methods to find the most appropriate and accurate model for each family of applications or dataset. This study compares the symbolic regression utilizing genetic programming (GP), with conventional machine learning techniques. In cases it is required to model an unknown, poorly understood, and/or complicated system. In these cases, we utilize genetic programming to generate a symbolic model without using any pre-known model. In this paper, the GP is studied as a tool for prediction in different types of datasets and conducted experiments to verify the superiority of GP over conventional models in certain conditions and datasets. The accuracy of GP-based regression results are compared with other machine learning techniques, and are found to be more accurate in certain conditions.
Accurate Single-Stream Action Detection in Real-Time

Analyzing videos of human actions involves understanding the spatial and temporal context of the scenes. State-of-the-art action detection approaches have demonstrated impressive results using Convolutional Neural Networks (CNNs) within a two-stream framework. However, most of them operate in a non-real-time, offline fashion, thus are not well-equipped in many emerging real-world scenarios such as autonomous driving and public surveillance. In addition, they are computationally demanding to be deployed on devices with limited power resources (e.g., embedded systems). To address the above challenges, we propose an efficient single-stream action detection framework by exploiting temporal coherence between successive video frames. This allows CNN appearance features to be cheaply propagated by motions rather than being extracted from every frame. Furthermore, we utilize an implicit motion representation to amplify appearance features. Our method based on motion-guided and motion-aware appearance features is evaluated on the UCF-101-24 dataset. Experiments indicate that the proposed method can achieve real-time action detection up to 32 fps with a comparable accuracy as the two-stream approach.
A New Text Steganography Method Based on Sudoku Puzzle Generation

Text steganography methods follow the principle of embedding secret text message inside an innocent looking cover text and it can be referred as the art of hiding the presence of any ongoing communication. This paper proposes a new puzzle generation based text steganography approach which works on simple Sudoku puzzle generation from the cover text following certain steps that generates a random Sudoku puzzle according to the different secret messages to be hidden at different times. The proposed method finds the position of the secret characters of the secret message in different positions of different words in different sentences. Using those position numbers the proposed hiding method generates a random Sudoku puzzle that is to be sent to the receiver along with the chosen cover text. The extraction algorithm on the other side finds the position of the secret characters from the puzzle and by assembling them in order the hidden message is regenerated at the receiving end accordingly. Experiments show that the proposed method can be considered to be much reliable compared to existing text steganography algorithms, as in this case the secret data bits are not directly hidden inside the chosen cover text and hence it’s not modified in any way, which increases the security level of the method. The proposed method can further be extended by embedding the generated Sudoku puzzle in any set of binary bit string preferably in any text document for its copyright protection.
An Approach to Secure Data Transmission Through the Use of Cryptography and Steganography

In this paper, an approach to transfer data securely is proposed. First, from the sender side, the data is encrypted using Rivest-Shamir-Adleman (RSA) algorithm and then this encrypted data is hidden using Least Significant Bit image steganography concept in a image of the web page and its url is sent to the receiver. Then, at the receiving end, the encrypted data is obtained that is hidden in the image of the web page whose url is sent. This encrypted data is decrypted using the same RSA algorithm. When the sender sends the data to the receiver and incase this data is obtained by the third person or an intruder he will see a URL being sent. Then he will open the URL and he will find a webpage, and he would not find any data being hidden. Then he will try to view the source code of the webpage, and then also he would not find any data being hidden. Thus, the intruder will be assured that no data is been hidden in this and chances are more that he would not go to decode it further. Hence, the chances of doubt an intruder can get that some confidential or private data is hidden in the data being sent is reduced. And hence, the data sent by the sender to the receiver is secured.
Blood lactate concentration prediction in critical care patients: handling missing values.

Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the problem of lactate prediction as a clinically relevant benchmark problem for machine learning community so as to assist clinical decision making in blood lactate testing. Accordingly, we demonstrate the relevant challenges of the problem and its data in addition to the adopted solutions. Also, we evaluate the performance of different prediction algorithms on a large dataset of ICU patients from the multi-centre eICU database. More specifically, we focus on investigating the impact of missing value imputation methods in lactate prediction for each algorithm. The experimental analysis shows promising prediction results that encourages further investigation of this problem.
A Case Study of College Students’ Use of Mobile Applications and their Perception of AI-based Apps

The purpose of the study is to investigate college students" use of smart phone applications for the purpose of English learning and their degree of awareness of current AI-based English learning applications and chatbots. For this purpose, the researcher administered survey questions to 87 college students two times in one semester. The analysis of survey data yielded the following findings: First, majority of the students were participating the autonomous mobile learning outside of the classroom. The most popular applications students used were TOEIC test preparation apps followed by Youtube and other English learning apps. Students" lack of information of proper apps and their disinterest were revealed as the major reasons for the low use of English learning apps. Second, it was found that students were rarely aware of AI based English applications and their actual experience of AI technology is extremely limited. Third, students need English teachers" guidance and advice in selecting appropriate English learning apps for their own mobile learning. The types of apps students favored and the teachers" role in promoting self-directed learners are also discussed.
Kazakh Text Summarization using Fuzzy logic

In this paper we present an extractive summarization method for the Kazakh language based on fuzzy logic. We aimed to extract and concatenate important sentences from the primary text to obtain its shorter form. With the rapid growth of information on the Internet thereis a demand on its efficient and cost-effective summarization. Thereforethe creation of automatic summarization methods is considered as a very important task of natural language processing. Our approach is based on the preprocessing of the sentences by applying morphological analysis and pronoun resolution techniques in order to avoid their early rejections. Afterwards, we determine the features of the processed sentences need for exploiting fuzzy logic methods. Additionally, since there is no available data for the given task, we collected and manually annotated our own dataset from the different Internet resources in the Kazakh language for the experimentation. We also applied our method on CNN/Daily Maildataset. The ROUGE-N indicators were calculated to assess the quality of the proposed method. The ROUGE-L(f-score) score by the proposed method with pronoun resolution for the former dataset is 0.40, whereas for the latter one it is 0.38.
KeyVector: Unsupervised Keyphrase Extraction Using Weighted Topic via Semantic Related

Keyphrase extraction is a task of automatically selecting topical phrases from a document. We present KeyVector, an unsupervised approach with weighted topics via semantic relatedness for keyphrase extraction. Our method relies on various measures of semantic relatedness of documents, topics and keyphrases in the same vector space, which allow us to compute three keyphrase ranking scores: global semantic score, find more important keyphrases for a given document by measuring the semantic relation between documents and keyphrase embeddings; topic weight, pruning/selecting the candidate keyphrases on the topic level; topic inner score, ranking the keyphrases inside each topic. Keyphrases are then generated by ranking the values of combined three scores for each candidate. We conducted experiments on three evaluation data sets of different length documents and domains. Results show that KeyVector out performs state of the art methods on short, medium and long documents.
Are We Having Fun Yet?

Personality of Artificial Intelligence (A.I) is a vast and complex domain. Traits like trustworthiness and humanness have long been evaluated, but less so for traits like fun. In this study we evaluate fun in four conversational agents with a mix of menial tasks, games, and free play in the US and India on voice and text-based platforms. Our research explores user sentiment of fun in an A.I. personality, implications of fun personality on voice-based and text-based agents, and the perception of a fun A.I. personality in two culturally distinct markets. We used a mixed method approach to evaluate these objectives with 44 participants. Our results end with design principles for creating fun in artificial intelligence: 1) balance fun through conversational humor and goal-oriented actions, 2) create fun experiences through playful interactions, 3) convey fun through platform specific expressions like tone of voice or visual gestures and emojis, and 4) be inclusively fun by being culturally relevant so your conversational agent translates across borders.
Penggunaan Aplikasi Google Classroom Dalam Upaya Meningkatkan Hasil Belajar Matematika Pada Siswa Kelas X IPS 3 SMA Batik 2 Surakarta

This study aims to improve learning outcomes on mathematics subjects by using the Google Classroom Application in class X IPS 3 Surakarta Batik 2 High School, amounting to 29 students, consisting of 20 male and 9 female students. The technique of collecting data uses observation, tests, and documentation. Data analysis techniques are carried out through models in order: data reduction, data presentation, and conclusion drawing. The results of this study are increasing student learning outcomes in mathematics learning. This can be seen from the increase in the percentage of student learning outcomes from the pra siklus, siklus I to siklus II. In the pra siklus the number of students who reached the KKM was 48% or 14 students, an increase of 18% in the siklus I to 66% or 19 students. In the siklus II the number of students who achieved KKM increased by 23% to 89% or 25 students. Thus it can be concluded that the use of the Google Classroom application can improve learning outcomes in mathematics subjects in class X IPS 3 SMA Batik 2 Surakarta in 2018/2019.
Low-Complexity Video Quality Assessment Based on Spatio-Temporal Structure

Low-complexity is as important as prediction accuracy for video quality assessment (VQA) metrics to be practically deployable. In this paper, we develop an effective and efficient full-reference VQA algorithm, called Spatio-temporal Structural-based Video Quality Metric (SSVQM). To be more specific, spatio-temporal structural information is sensitive to both spatial distortions and temporal distortions. We calculate spatio-temporal structure based local quality according to spatio-temporal gradient characteristics and chrominance information. Then, these local quality scores are integrated to yield an overall video quality via a spatio-temporal pooling strategy simulating three most important global temporal effects of the human visual system, i.e. the smooth effect, the asymmetric tracking effect. Experiments on VQA databases LIVE and CSIQ demonstrate that our SSVQM achieves highly competitive prediction accuracy and delivers very low computational complexity.
Modelling the Single Word to Multi-Word Transition Using Matrix Completion

Infants acquire language in distinct stages, starting from single gestures and single words, and through utilising gestures, they learn multi-word combinations. To achieve this language development on artificial agents, we propose a multimodal computational model for single to multi-word transition through gesture-word combinations. Our approach relies on advancements in deep models for feature extraction and on casting the supplementary word generation problem into a matrix completion task. Experimental evaluation is carried out on a dataset recorded directly from the humanoid iCub's cameras, comprising the deictic gesture of pointing and real-world objects. Illustrated by our results, the proposed architecture further strengthens its potential to model early stage language acquisition.
SATALex: Telecom Domain-specific Sentiment Lexicons for Egyptian and Gulf Arabic Dialects

Given the sacristy of the Arabic sentiment lexicon especially for the Egyptian and Gulf dialects, together with the fact that a word’s sentiment depends mostly on the domain in which it is used, we present SATALex which is a two-part sentiment lexicon covering the telecom domain for the Egyptian and Gulf Arabic dialects. The Egyptian sentiment lexicon contains close to 1.5 thousand Egyptian words and compound phrases, while the Gulf sentiment lexicon contains close to 3.5 thousand Gulf words and compound phrases. The development of the presented lexicons has taken place iteratively, in each iteration manual annotators analyzed tweets for the corresponding dialect to try to extract as many domain specific words as possible and measure their effect on the performance of the classification. The result are lexicons which are more focused and related to the telecom domain more than any translated or general-purpose sentiment lexicon. To demonstrate the effectiveness of these built lexicons and how directly they can impact the task of sentiment analysis, we compared their performance to one of the biggest publicly available sentiment lexicon (WeightedNileULex) using Semantic Orientation (SO) approach on telecom test datasets; one for each dialect. The experiments show that using SATALex lexicons improved the results over the publicly available lexicon.
100% E-Money and its Implications for Central Bank Digital Currency

Many publications on Central Bank Digital Currency (CBDC) point out the possibility of granting the public access to CBDC through non-bank third-parties instead of letting the central bank interact directly with the public. However, little attention has been paid to the relationship between these approaches and 100% e-money, which is already issued in El Salvador and is legally possible in a few more countries. Therefore, this paper wants to investigate this relationship by comparing 100% e-money with two models, where CBDC is provided by third-parties which are known from the literature as the custodian and the intermediary model. The findings indicate that the intermediary model and 100% e-money display strong similarities, which has implications for CBDC research. So, research on third-party CBDC could be more goal-driven and give better policy implications. In addition, this research shows regulatory requirements on third-party CBDC and future research areas on 100% e-money.
English Dataset for Automatic Forum Extraction

This paper describes the process of collecting, maintaining and exploiting an English dataset of web discussions. The dataset consists of many web discussions with hand-annotated posts in the context of a tree structure of a web page. Each post consists of username, date, text, and citations used by its author. The dataset contains 79 different websites with at least 500 pages from each. Each web page consists of a tree structure of HTML tags with texts taken from selected web pages. In the paper, we also describe algorithms trained on the dataset. The algorithms employ basic architectures (such as a bag of words with an SVM classifier and an LSTM network) to set a baseline for the dataset.
A Parallel Implementation of an XDraw Viewshed Algorithm with Spark

Viewshed analysis is an indispensable part of digital terrain analysis and is widely used in many application domains. High-resolution raster DEM data bring significant computational challenges to the existing viewshed analysis algorithms, which are computationally intensive and require a large memory space and massive computing power. The viewshed analysis process can be accelerated through the use of Apache Spark. In this article, we present both a tile-based raster data storing strategy and an equal-volume computing strategy for distributed viewshed computation using Spark. The parallel implementation of the XDraw algorithm mainly consists of three parts: (1) partitioning a raster DEM file into square tile sets and reorganizing these tile sets to prevent tile overlap across data divisions of HDFS, (2) subdividing the DEM into multiple equal-volume data sectors according to the viewpoint position, and (3) retrieving the corresponding tile sets of each sector to perform the XDraw algorithm independently and efficiently. Experiments with realworld datasets show that the two proposed strategies can achieve higher speed-up and efficiency for XDraw viewshed analysis as the raster DEM data volume is dramatically increased.
An Ensemble of Automatic Keyword Extractors: TextRank, RAKE and TAKE

We construct an ensemble method for automatic keyword extraction from single documents. We utilize three different unsupervised automatic keyword extractors in building our ensemble method. These three approaches provide candidate keywords for the ensemble method without using their respective threshold functions. The ensemble method combines these candidate keywords and recomputes their scores after applying pruning heuristics. It then extracts keywords by employing dynamic thres hold functions. Weanalyze the performance of our ensemble method by using all parts of the Inspect data set. Our ensemble method achieved a better overall performance when compared to the automatic keyword extractors that were used in its development as well as to some recent automatic keyword extraction methods.
Towards Reverse-Engineering Black-Box Neural Networks.

Much progress in interpretable AI is built around scenarios where the user, one who interprets the model, has a full ownership of the model to be diagnosed. The user either owns the training data and computing resources to train an interpretable model herself or owns a full access to an already trained model to be interpreted post-hoc. In this chapter, we consider a less investigated scenario of diagnosing black-box neural networks, where the user can only send queries and read off outputs. Black-box access is a common deployment mode for many public and commercial models, since internal details, such as architecture, optimisation procedure, and training data, can be proprietary and aggravate their vulnerability to attacks like adversarial examples. We propose a method for exposing internals of black-box models and show that the method is surprisingly effective at inferring a diverse set of internal information. We further show how the exposed internals can be exploited to strengthen adversarial examples against the model. Our work starts an important discussion on the security implications of diagnosing deployed models with limited accessibility. The code is available at goo.gl/MbYfsv.