Reviewerly/exHarmony huggingface dataset:
Bringing Stories Alive: Generating Interactive Fiction Worlds

Interactive fictions—also called text-based games—are games in which a player interacts with a virtual world purely through textual natural language. In this work, we focus on procedurally generating interactive fiction worlds. Generating these worlds requires (a) referencing everyday and thematic commonsense priors in addition to (b) being semantically consistent, (c) interesting, (d) coherent throughout, all while (e) producing fluent natural language descriptions of places, people, and things. Using existing story plots from books as inspiration, we present a method that first extracts a partial knowledge graph encoding basic information regarding world structure such as locations and objects. This knowledge graph is then automatically completed utilizing thematic knowledge and used to guide a neural language generation model that fleshes out the rest of the world. We evaluate generated worlds with human-participant studies, comparing our technique against rule-based and human-made baselines
Model Pembelajaran Kooperatif Tipe Inside-Outside Circle dalam Pembelajaran Bilingual Terhadap Hasil Belajar Siswa

This research aims at finding out the differences in thematic learning achievement between students who learned by using Inside-Outside Circle cooperative learning model in Bilingual learning and direct learning model in class IV of group I of West Golewa Subdistrict, Ngada Regency. The research  method used a quantitative research with a true experimental research of Posttest-only Control Group Design. This research was conducted at class IV students of Group I of West Golewa Subdistrict, with sample totaled 34 students. The data collection technique used post-test in the form of multiple choices test. The result of post-test was then analyzed using the t-test technique. Based on the data of t count = 4.797 and t table with significance level of 5% (ɑ = 0.05) = 2,021. Because of t count 4,797 > t tab le 2,021, then H o was rejected and H 1 was accepted. The result proved that the experimental class using the Inside Outside Circle in Bilingual learning model was higher than the control class (85.12 > 65.47). Therefore, it can be concluded that there are differences in thematic learning achievement between the experimental group and the control group and the Inside Outside Circle learning model based Bilingual learning towards students learning achievement.
Fast Corner Detection Using Approximate Form of Second-Order Gaussian Directional Derivative

High-efficiency image corner detection, one of the most important and critical basic technology in industrial image processing, is to detect point features from an input image in real-time. In this article, we propose a new corner detection method which has both good performance of corner detection and real-time processing abilities. Firstly, the integral image and the box filter are combined to obtain the second-order derivative response in each direction of the image. Secondly, a new coarse screening mechanism for candidate corners is presented to reduce the complexity of the corner metric. Thirdly, a non-maximum suppression operation is utilized to obtain corners. Finally, the performance evaluation on accuracy of corner detection, localization error, average repeatability, region repeatability, different lighting conditions, and execution time are used to assess the proposed method against twelve state-of-the-art methods. The experimental results show that our proposed detector has good corner detection performance and achieves the requirement of real-time processing.
FCM-Based P2P Network Lending Platform Credit Risk Dynamic Assessment

With the rapid development of Internet finance, Peer to Peer(P2P) network lending has also developed as a new type of financing.However, as the P2P network lending platform becomes more and more popular, problems such as running, shutting down, and withdrawing cash are emerging.One of the most common problems is caused by credit.This paper addresses the issues such as credit problems and existing risks of online platforms by proposing a Fuzzy Cognitive Map (FCM) that is used to establish a credit risk assessment model for the P2P network lending platform.It provides a new idea for the credit risk assessment of online lending platforms.The paper uses Lu Jinfu as an example to evaluate its credit risk and selects two other platforms to rank its credit risk.The results show that the fuzzy cognitive map considers the mutual influence and feedback between the indicators.The index weight matrix learned by historical data represents the relationship between the indicators.After inputting the initial data into the inference, the index data reaches a steady state.The steady-state refers to the state that we expect the indicator to reach after a certain period.As an objective evaluating method for the credit risk of the P2P network lending platform, the proposed method has demonstrated its advantage and effectiveness.
Assessment of Notch Effect Based on Finite Element Analysis and Digital Image Correlation Technique

Triaxial state of stress experienced by structural components due to complex geometry, discontinuities, or type of loading can be studied by tensile testing of notched specimens. Herein, assessment of tensile deformation of 316LN austenitic stainless under the uniaxial and triaxial stresses is presented based on finite element (FE) analysis and digital image correlation (DIC). To study their effects, notches of different root radii are incorporated in flat smooth specimens. The strength of the material increases in the presence of notch. Among the various constitutive expressions, the Swift equation provides a better representation of the tensile response for the smooth specimen. The analysis is further extended by incorporating Swift equation parameters as the deformation model in the FE analysis for notched specimens. The tensile response of the notched specimens is predicted well using the FE analysis. The in‐house algorithm is developed for DIC and used to estimate the strain in smooth and notched specimens by speckle‐pattern image analysis. The total displacement of smooth and notched specimens obtained from the FE analysis and DIC technique is comparable to the experimental results. Both the techniques complemented each other in understanding the deformation behavior of the steel.
An End-to-End Approach to Reconstructing 3D Model From Image Set

Large-scale 3D reconstruction from imagery has received much attention from the computer vision community.However, recovering 3D structures from 2D images is a notoriously complex process that requires expertise with often limited results.This paper presents an end-to-end 3D reconstruction system that can produce high-quality 3D models from a set of unordered image collections.Our workflow is a typical 3D reconstruction architecture that consists of structure from motion (SFM), multi-view stereo (MVS), and surface reconstruction, and can automatically recover desirable 3D models without any interactive operations.Finally, a comprehensive experiment is conducted on several benchmark datasets to assess the presented system.Experimental results show that the presented system achieves significant improvements in reconstruction accuracy and completeness over the existing state-of-the-art approach.
Ways to enhance the effectiveness of online education

The article is an analytical review of current recommendations provided in foreign scientific literature for secondary and higher education in terms of online learning enhancement through transformations in course content, platforms, assessments, and ways of teacher-students interaction. Recent recommendations in this regard are described from the position of methodology (educational procedures), psychology (motivation, information perception and processing, and communication), and technical solutions (platforms and interfaces).
Differentially Private Learning Does Not Bound Membership Inference.

Training machine learning models on privacy-sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to a series of new attacks, such as Membership Inference Attacks (MIAs), that exploit vulnerabilities in ML models in order to expose the privacy of individual training samples. A growing body of literature holds up Differential Privacy (DP) as an effective defense against such attacks, and companies like Google and Amazon include this privacy notion in their machine-learning-as-a-service products. However, little scrutiny has been given to how underlying correlations or bias within the datasets used for training these models can impact the privacy guarantees provided by DP. In this work, we challenge prior findings that suggest DP provides a strong defense against MIAs. We provide theoretical and experimental evidence for cases where the theoretical bounds of DP are violated by MIAs using the same attacks described in prior work. We first show this empirically, with real-world datasets carefully split to create a distinction between member and non-member samples, and then we study the reason why the theoretical DP bounds break when members and non-members are not independent and identically distributed. Our findings suggest that certain properties of datasets, such as bias or data correlation, play a critical role in determining the effectiveness of DP as a privacy preserving mechanism against MIAs.
Automated Essay Grading: An Empirical Analysis of Ensemble Learning Techniques

Automated essay grading refers to the application of natural language processing tools for assigning scores to student essays. It is an important research domain as teachers are often required to grade a large amount of student essays in educational settings. Fair grading of essays is a challenging and tedious task. Teachers often consider this as unproductive work. Thus, there is a need for an automated approach for teachers so that they are no longer required to manually grade the student essays. Various automated essay scoring systems using machine learning and information retrieval concepts have been developed in the past studies. In the recent years, ensemble classification techniques have gained popularity. Ensemble techniques use multiple classifiers for making a prediction and have proved to be outperforming classical machine learning. In this paper, we present an empirical study of ensemble learning techniques for classification of student essays. We studied performance of five machine learning and four ensemble learning techniques for conducting experiments. We further utilized feature selection technique to improve the prediction efficiency. The performance results on automated student assessment prize dataset available on Kaggle showed that ensemble techniques outperform the efficiency of traditional machine learning techniques.
A Survey of Image Enhancement and Object Detection Methods

Image enhancement is a classical problem in computer vision. Image enhancement techniques aim to enlarge the size and quality of low-resolution (LR) images to high-resolution (HR) image. Various techniques have been developed over the years, for example, the traditional methods of upscaling to applying neural networks generating output using trained models and datasets. On the other hand, object detection has vast use cases in modern inference systems like face detection to text detection. This paper surveys the various techniques of image enhancement and object detection along with their contributions and methodologies.
Wearable technology and body mapping

The process of body mapping may be viewed as a performative act. Here, an alternative performative act is presented and discussed – the wearing of immersive body maps. The idea of a wearable body map brings together several current trends in technology development, such as wearable computing, smart garments, and augmented reality, as well as blurring the line between computing infrastructures, fashion, art, and performance. Different kinds of wearable body maps may be created, depending on how these incorporate, or not, various interaction modalities. Hence, basic wearable body maps will be merely expressive. A basic body mapping app for such a context is presented. By combining this basic body mapping capability with data from other sensors, wearable body maps can be made more responsive to the environment. Once electronic sensors are incorporated, one can create more sophisticated interaction modalities, including proximal encounters with other persons and eventually remote encounters with larger collectives. Further, these developments may be understood in terms of an ecological framing theory, which not only directs the design process in an ethically aware manner but also offers the possibility of studying potential long-term impacts for mental health, both in terms of positive and negative outcomes.
Application of Computer Music Production Technology and Computer Multimedia System in College Sight-singing and Ear Training

Abstract Although sight singing and ear training have been widely used in teaching, there are still many problems in the actual implementation process and only by solving these problems can teachers’ teaching goals be better achieved and music teaching in education can be achieved Better results. In the process of practice, teachers need to use computer technology to teach students according to their aptitude, pay attention to the dominant position of students, actively create a good learning environment for students, enhance students’ self-awareness in learning and make vocal training and ear training in education majors. It has been continuously developed, so that the trained talents can better adapt to the needs of social development.
DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations.

Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the context of multiple-input and multiple-output (MIMO) receivers. In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. We propose two novel alternatives for the transformation layer: a maximal ratio combining-based transformation, or a fully learned transformation. The former relies more on expert knowledge, while the latter utilizes learned multiplicative layers. Both proposed transformation layers are shown to clearly outperform the conventional baseline receiver, especially with sparse pilot configurations. To the best of our knowledge, these are some of the first results showing such high performance for a fully learned MIMO receiver.
Natural scene text detection based on YOLO V2 network model

Abstract As an important part in the field, the natural scene text detection has been widely applied in visual navigation system, content-based image and video retrieval, instant translation system and so on. In this paper, we introduce several object detection network based on deep learning, and apply the YOLO v2 into natural scene text detection, changing the multi objects detection problems into the two classification problems. The main works in the paper include the following: prepare the datasets; we train the YOLO v2 with the optimum parameters, carry out the regression analysis of the coordinate parameters and categories of bounding boxes, obtain the detection result; according to different detection models, the detection results of different datasets are compared and analyzed, YOLO V2 model detection speed 0.105s/image has certain advantages.
Multi-Domain Dialogue State Tracking - A Purely Transformer-Based Generative Approach.

We investigate the problem of multi-domain Dialogue State Tracking (DST) with open vocabulary. Existing approaches exploit BERT encoder and copy-based RNN decoder, where the encoder first predicts the state operation, and then the decoder generates new slot values. However, in this stacked encoder-decoder structure, the operation prediction objective only affects the BERT encoder and the value generation objective mainly affects the RNN decoder. In this paper, we propose a purely Transformer-based framework that uses BERT as both encoder and decoder. In so doing, the operation prediction objective and the value generation objective can jointly optimize our model for DST. At the decoding step, we re-use the hidden states of the encoder in the self-attention mechanism of the corresponding decoder layer to construct a flat model structure for effective parameter updating. Experimental results show that our approach substantially outperforms the existing state-of-the-art framework, and it also achieves very competitive performance to the best ontology-based approaches.
Conceptualization of a CyberSecurity Awareness Quiz

Recent approaches to raise security awareness have improved a lot in terms of user-friendliness and user engagement. However, since social engineering attacks on employees are evolving fast, new variants arise very rapidly. To deal with recent changes, our serious game CyberSecurity Awareness Quiz provides a quiz on recent variants to make employees aware of new attacks or attack variants in an entertaining way. While the gameplay of a quiz is more or less generic, the core of our contribution is a concept to create questions and answers based on current affairs and attacks observed in the wild.
M-Learning and Student-Centered Design: A Systematic Review of the Literature

Currently, the development of M-Learning applications, with a user-centered approach, has become an emerging area of great importance, since these applications are expected to generate a better user experience (UX). This paper presents a systematic literature review (SLR) on e-learning the characteristics that current M-Learning applications cover to better satisfy the needs of students and also the models or architectures that are followed for their development. The literature review was performed on the most popular digital databases. The review process consisted of defining keywords for the work search; subsequently, was made a selection task based on inclusion and exclusion criteria; and finally, an analysis of the works was carried out. The results of the process review provided the main contributions to the field of study, as well as the areas of opportunity, allowing a discussion on possible future directions of research.
The impact of M-banking quality service on customers satisfaction during Covid-19 lock down: The case of Bank of Abyssinia, Ethiopia

COVID-19 pandemic posed a great challenge for the financial industry in Ethiopia leading to an increase of mobile banking services which becomes a new normal and necessity for users of financial services. The study seeks empirically to test the impact of m-Banking quality service on Bank of Abyssinia customer’s satisfaction during COVID-19. A quantitative study approach and a descriptive research design with survey research method were used. East Addis Ababa district office was selected as a sample using a non-probability design in the form of convenience sampling to collect data. Accordingly, 296 structured questionnaires were randomly distributed where 240 fully filled survey questioners were retrieved for analysis. The findings from the study showed that e-service quality dimensions are significant forerunner to customer satisfaction, and, among the dimensions, security, reliability and ease of use have a great influence on e-service quality and these dimensions are perceived critical by the Abyssinia Bank customers. All the six predictor variables reliability, efficiency, security, responsiveness, empathy, and ease of use were found to be positively influencing Abyssinia banks m-banking customer satisfaction, while the predictors ease of use and reliability were found to have a significant impact and the highest predictor in absolute numbers 0.2444595 and 0.2200381, respectively. Thus, the study recommends that Abyssinia bank management as a service provider should pay attention to the identified dimensions specifically, on ease of use and reliability of m-banking services while devising e-banking strategies to provide high service quality and satisfaction to its customers. Key words: Abyssinia Bank, customer satisfaction, e-service quality, mobile banking.
Learning Speaker Embedding from Text-to-Speech

Zero-shot multi-speaker Text-to-Speech (TTS) generates target speaker voices given an input text and the corresponding speaker embedding. In this work, we investigate the effectiveness of the TTS reconstruction objective to improve representation learning for speaker verification. We jointly trained end-to-end Tacotron 2 TTS and speaker embedding networks in a self-supervised fashion. We hypothesize that the embeddings will contain minimal phonetic information since the TTS decoder will obtain that information from the textual input. TTS reconstruction can also be combined with speaker classification to enhance these embeddings further. Once trained, the speaker encoder computes representations for the speaker verification task, while the rest of the TTS blocks are discarded. We investigated training TTS from either manual or ASR-generated transcripts. The latter allows us to train embeddings on datasets without manual transcripts. We compared ASR transcripts and Kaldi phone alignments as TTS inputs, showing that the latter performed better due to their finer resolution. Unsupervised TTS embeddings improved EER by 2.06\% absolute with regard to i-vectors for the LibriTTS dataset. TTS with speaker classification loss improved EER by 0.28\% and 0.73\% absolutely from a model using only speaker classification loss in LibriTTS and Voxceleb1 respectively.
A Novel Sentence Embedding Based Topic Detection Method for Microblogs

Topic detection is a difficult challenging task, especially when the exact number of topics is unknown. In this article, we present a novel topic detection approach based on neural computing to detect topics in a microblogging dataset. We use an unsupervised neural sentence embedding model to map blogs to an embedding space. The proposed model is a weighted power mean sentence embedding model in which weights are calculated by a targeted attention mechanism. The experimental results show that our embedding model performs better than baseline in sentence clustering. In addition, we propose a clustering algorithm, referred to as Relationship-Aware DBSCAN (RADBSCAN), to discover topics from a microblogging dataset in which the number of topics is automatically determined by the characteristics of the dataset. Moreover, to provide parameter insensibility, we use the forwarding relationship in the blogs as a bridge of two independent clusters. Finally, we validate the proposed method on a dataset from the Sina microblog. The results show that our approach can detect all topics successfully and can extract the keywords of each topic.