Reviewerly/exHarmony huggingface dataset:
An Online Authoring Tool for Interactive Fiction

Interactive fiction is a part of digital literature that promotes the active role of the reader in the reading process of an electronic book (e-book). For this purpose, multimedia resources are included within the content of the e-book, with which the reader has to interact. As a consequence, a richer reading experience is promoted, according to which the reader must decide how to continue the story he/she is reading, and, in turn, these decisions condition the future course of the story. However, the main problem faced by writers of interactive fiction is the lack of editors that make it easier for them, who are not necessarily experts in interactive content design, to use these strategies in the contents of their e-books, and which in turn offer them enough narrative flexibility. For this purpose, we have developed IFDBMaker, an interactive fiction authoring tool oriented to writers with no prior knowledge in digital content production. This tool is equipped with a web-based interface and lets non-technical writers use sophisticated narrative resources while authoring interactive fiction e-books with the sole support of a simple web browser.
Methods for Assessing Theme Adherence in Student Thesis

In this paper we study approaches to assessing the quality of student theses in pedagogics. We consider a specific subtask in thesis scoring of estimating its adherence to the thesis's theme. The special document (theme header) comprising the theme, aim, object, tasks of the thesis is formed. The theme adherence is calculated as the similarity value between the theme header and thesis segments. For evaluation we order theses in the increased value of the calculated theme adherence and compare the ordering with expert grades using the NDCG measure. We explore different methods, including probabilistic topic modeling, word embeddings and ontologies. The best configuration for theses ranking is based on the weighted averaged sum of word embeddings (word2vec) and keywords extracted from the theme header.
Study of Machine Learning Techniques for Sentiment Analysis

Sentiment Analysis, a field of Machine learning is used to describe what a user feels about a particular product or issues. With this advantage, we can acquire information that might prove helpful in the decision-making processes. It is based on Opinion mining concept that extracts opinion and analyses them. In this scenario, sentiment analysis plays a role on identifying the emotion behind these opinions. Source of these opinions can be from blogs, forums, reviews, twitter feeds, social media comments etc. This paper intends mainly to explore the related works in this field and it revolves around algorithms used and then compares them to find an appropriate solution for a particular problem.
An Associate-Rule-Aware Multidimensional Data Visualization Technique and Its Application to Painting Image Collections

This paper presents a visualization technique for multidimensional datasets containing real and categorical variables. Supposing multidimensional datasets containing real and categorical values, this technique displays a set of axes corresponding to the dimensions of real values. The technique evenly divides the axes into several ranges and displays component bar charts there. It brightly draws the component bar charts if association rules are applied at the corresponding ranges of the dimensions of real values. As a result, this technique highlights association rules so that users can discover important relationships between real and categorical variables in multidimensional datasets. This paper introduces an application of the presented technique to painting image collections. This application visualizes image features and categorical information of painting images and provides a user interface to browse the painting images associated with the multidimensional values. This paper also introduces user evaluation results of the user interfaces for painting image collections.
On Robust Markov Analysis for Reliability Assessment of Complex Systems using Imprecise Markov Chains

Markov analysis is a wide-spread tool for modelling interactions among components in complex systems. It is based on modelling the evolution of system's component states by Markov Chains. But, as in many other uncertainty models, it might often be overly optimistic to assume that we can construct a precise stochastic model which properly captures the uncertainties present in the investigated system. This issue is addressed by the theory of Imprecise Probabilities and, specifically for stochastic processes, by the theory of Imprecise Markov Chains. In this paper, we will demonstrate how Imprecise Markov Chains can not only serve as a robust alternative to classical stochastic models, but also how they can facilitate analyses by the means of problem dimension reduction and also, by die means of deliberate model construction, enable analyses which would not be possible by using solely precise probability models.
On the Question of the Estimation of Metasubject Result of the Educational Activity of Students of the Pedagogical Higher Education Institution Under the Competence of the Training Model

The transition of the higher education system to new educational standards imposes relevant requirements on the design, content and implementation of the procedure for evaluating students’ educational results. The article analyzes the metasubject approach to assessing the formation of competencies among students of a pedagogical university during the current and fi nal control. The authors describe the forms of control, criteria and indicators for evaluating the results of educational activities. The problem of point-rating approach in the assessment system in terms of the competence-based learning model is discussed.
Pattern Recognition in Sequences Using Multistate Sequence Autoencoding Neural Networks

In the paper, we describe the Multistate Sequence Autoencoding Neural Networks (MSANNs) that enable us to deal with some problems concerning pattern recognition in sequential data. The properties of the MSANN networks proposed by us make these networks suitable for use in the following situations: patterns appear only partially, patterns are in different orders, patterns are stretched over time, etc. The training and testing procedures for the MSANN networks are presented in the paper.
Finding the maximal adversary structure from any given access structure

Secure multi-party computation is an important research area in cryptography, and the secret sharing scheme (SSS) is one of the main tools for constructing multi-party computation protocols. The access structure and the adversary structure are two important subsets of participants in an SSS. In general, the collection of all qualified subsets that can reconstruct the secret s, is known as an access structure, while no information regarding this secret is available to any unqualified subsets, and the collection of unqualified subsets is described as an adversary structure. The maximal adversary, which will become a qualified subset if any one participant not in this unqualified subset is added. At present, there is no effective algorithm to determine the maximal adversary structure for any given access structure. In this paper, we propose two algorithms to determine the maximal adversary structure from any given access structure, in which a binary tree is introduced to construct such algorithms. Moreover, a special type of access structure is established, from which the maximal adversary structure can be directly characterized, and the maximal adversary structure in this case is shown to be the largest when the number of participants of each qualified polynomial in the access structure is three.
Adaptively Tuning a Convolutional Neural Network by Gating Process for Image Denoising

This paper presents a new framework that controls feature maps of a convolutional neural network (CNN) according to the noise level such that the network can have different properties to different levels. Unlike the conventional non-blind approach which reloads all the parameters of CNN or switches to other CNNs for different noise levels, we adjust the CNN activation feature maps without changing the parameters at the test phase. For this, we additionally construct a noise level indicator network, which gives appropriate weighting values to the feature maps for the given situation. The noise level indicator network is so simple that it can be implemented as a low-dimensional look-up table at the test phase and thus does not increase the overall complexity. From the experiments on noise reduction, we can observe that the proposed method achieves better performance compared to the baseline network.
Sampling/Importance Resampling for Semantically Consistent Synthetic to Real Image Domain Adaptation in Urban Traffic Scenes

Synthetic data find application in computer vision tasks for a long time. Limited performance of algorithms trained solely on synthetic data has been approached with domain adaptation techniques such as the ones based on generative adversarial framework [1]. In this work we demonstrate how using adversarial training alone can introduce semantic inconsistencies in refined images. We suggest leveraging available semantic labels from target domain using naive re-sampling approach alongside with adversarial loss. We also show that aforementioned strategy improves quality of translated images of underlying method and their usability for the semantic segmentation task in the context of autonomous driving. This method will be also put in comparison with existing state-of-the-art synthetic to real domain adaptation methods.
Reference Path Correction for Autonomous Ground Vehicles Driving Over Rough Terrain

This paper proposes an online reference path correction algorithm for Autonomous Ground Vehicles (AGVs) driving in unstructured environments. Because the reference path which is not in accord with the road geometry cannot be directly executed by AGV, we use a correction baseline, which represents the road geometry, as the guidance to correct reference path. As the road geometry varies according to the road environment, we use the centerline of drivable area extracted with General Regression Neural Network (GRNN) or optimal path generated by multi-resolution local trajectory planner as the correction baseline to correct the distance and curvature errors of the reference path. The proposed algorithm is tested on our experimental autonomous vehicle in the realistic off-road scenarios. Experimental results demonstrated its capability and efficiency to reduce the search space of the local trajectory planner and improve the robustness of tracking the reference path.
A Self-organizing Feature Map for Arabic Word Extraction

Arabic word spotting is a key step for Arabic NLP and the text recognition task. Many recent studies have addressed segmentation problems in the Arabic language. However, many issues still have to be overcome. In this paper, we propose a new approach for segmenting an image Arabic text into its constituent words. Our approach consists of two main steps. In the first step, a set of features is extracted from connected components using the Run-length smoothing algorithm (RLSA). In the second step, spatially close connected components that are likely to belong to the same word component are grouped together. This is done via a learning technique called the self-organizing feature map (Kohonen map). We evaluated our approach on 300 images with different sizes and fonts for handwritten text using AHDB. Our results suggest that our approach can efficiently segments lines. Moreover, as our approach is based on a straightforward machine learning model, it should be possible to adapt it to other languages as well.
When Spatially-Variant Filtering Meets Low-Rank Regularization: Exploiting Non-Local Similarity for Single Image Interpolation

This paper combines spatially-variant filtering and non-local low-rank regularization (NLR) to exploit non-local similarity in natural images in addressing the problem of image interpolation. We propose to build a carefully designed spatially-variant, non-local filtering scheme to generate a reliable estimate of the interpolated image and utilize NLR to refine the estimation. Our work uses a simple, parallelizable algorithm without the need to solve complicated optimization problems. Experiment results demonstrate that our algorithm significantly improves PSNR and SSIM of the interpolated images compared with state-of-the-art algorithms.
Similar Meaning Analysis for Original Documents Identification in Arabic Language

The progressive advancement in technology has become easy to present the language expression of someone else as one’s own with similar words semantically. This phenomenon increased the potential source of plagiarism. Its detection is a challenge especially in the case of Arabic paraphrase because of the semantic ambiguity of this language. In recent decades, researches have been hindered by the very limited availability of well-structured datasets. In this context, our main objectives are focused on constructing a corpus for Arabic and presenting thereafter its impact for identifying paraphrase. Indeed, we generated the suspect documents from the Open Source Arabic Corpora (OSAC). Distributed word representation (word2vec) and part-of-speech methods were useful for replacing each original word by its most similar one that had the same grammatical class. Moreover, we captured the structure of Arabic sentences with different window sizes and vector dimensions. Then, we studied how this corpus could be used efficiently in the evaluation of Natural Language Processing (NLP) methods (i.e. Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), word2vec, Global Vector Representation (GloVe), and Convolutional Neural Network (CNN)) for paraphrase detection. Experiments revealed which one could outperformed significantly for preserving semantic properties of Arabic words with various linear regularities, alleviating data sparseness and increasing the degree of semantic similarity, in terms of precision and recall.
Program Modeling in the Investigation of Crimes Against Cybersecurity in Russia

In a series of recently conducted forensic studies related to methods of a criminal investigation in the sphere of computer information, scientifically interesting data about the constancy of connections of particular characteristics of these criminal actions have been obtained. On their basis, a researcher is supposed to build typical and then individual models of an event being investigated. As practice shows, such a problem is quite complicated for officials of preliminary investigation agencies since they have to choose necessary data from legal regulations and a wide range of reference materials (the Criminal Code, the Code of Criminal Procedure, federal laws, and other statutory acts regulating public relations in the sphere of information, information technologies, and protection of information, forensic scientific products, etc.), most of which don’t apply to a particular case under investigation. It appears that in present-day conditions of science and technology development this problem should be solved on the basis of forensic computer modeling.
Blind Image Blur Assessment Based on Markov-Constrained FCM and Blur Entropy

The image blur assessment is of various practical use such as feedback of microscope dynamic focusing and assessment of the quality of pictures in social media. However, the problem of providing a fast and sensitive assessment toward image blur is not easy to deal with. In this paper, we provide a new effective way to evaluate the blur level of the image. We first introduce Markov Constraints to the Fuzzy-C-Means (MC-FCM) clustering algorithm to improve the robustness to noise, then obtain the fuzzy membership of pixels via the MC-FCM, finally, to leverage fuzzy membership from MC-FCM, the blur assessment toward pixels in the edge zone is provided by modifying Shannon's entropy. Comparisons are made on two public blur image database over five recent image blur assessment algorithms. The results demonstrate that the proposed algorithm has better resolutions for mildly blurred images and lower computation complexity compared with existing approaches.
Single Image Super-Resolution via Cascaded Parallel Multisize Receptive Field

Recovering a High-Resolution (HR) image from a Low-Resolution (LR) image is the main concept of image Super-Resolution(SR). These days Convolution Neural Networks(CNN) have become very popular and efficient in generating HR image from a LR image. Although CNNs are widely used with great performance improvements, there is still much room for improvement. There has always been a trade-off between the number of parameters and performance enhancement. Inspired by the Inception architecture of GoogleNet, we have developed a novel approach with an efficiently increased number of receptive field by cascading different kernel sizes to reconstruct the HR image. Experimental results shows that the proposed model outperforms the state-of-art methods.
Learning Chan-Vese

Chan-Vese is a level set method that simultaneously evolves a level set surface and fits locally constant intensity models for the interior and exterior regions to minimize a Mumford-Shah integral. However, the length-based contour regularization in the Chan-Vese formulation is quite simple and too weak for many applications. In this paper we introduce a generalization of the Chan-Vese method to evolve a curve where the regularization is based on a Fully Convolutional Neural Network. We also show how to learn the curve model as a Recurrent Neural Network (RNN) using training examples. Our RNN differs from the standard ones because it has the Chan-Vese locally constant intensity model, which gives it better interpretability and flexibility.
Adaptive Partition Migration for Irregular Graph Algorithms on Elastic Resources

Component-centric graph programming models allow distributed graph algorithms to be composed, and executed in an iterative manner on commodity clusters and Clouds. Graphs are partitioned and statically placed on a fixed number of machines before execution. However, many graph algorithms have an irregular execution behavior across partitions in different iterations, which causes resource under-utilization. We propose wo strategies, First Fit Decreasing with Migration Planning (FFDMP) and MinMax, for adaptive partition placement onto an elastic number of Cloud resources for such irregular algorithms. For each iteration, our strategies decide the number of hosts and the placement of partitions on them to balance the compute load, and enact this by migrating partitions between hosts at iteration boundaries. Unlike others, our strategies actively consider the time and cost penalties for moving partitions between hosts, and reduce the overall cost of execution while mitigating any increase in makespan. We implement these strategies on our GoFFish subgraph-centric graph processing platform, and evaluate them for performing Breadth First Search (BFS) on large real-world graphs with 10^7-10^9 edges. Our results show that the proposed strategies reduce the median resource cost by 13-38% when compared to a static placement, increase the median makespan by 1-33%, which is strictly bound by a given time budget, and also out-perform existing baseline scheduling algorithms from literature.
Outlier-Robust Neural Aggregation Network for Video Face Identification

Current approaches for video face recognition rely on image sets containing faces of exclusively one identity. However, as image sets are created by unsupervised methods, it is necessary to consider outlier-afflicted sets for real-life applications. In this paper, we propose an Outlier-Robust Neural Aggregation Network (ORNAN). First, we embed each image into a feature space using a Convolutional Neural Network (CNN). With the help of two cascaded attention blocks, we predict outliers within the image set. By integrating this knowledge into our aggregation network, we adaptively aggregate all feature vectors to form a single feature, mitigating the influence of outliers and noisy features. We show that our network is robust against outliers using outlier-afflicted IJB-B and IJB-C benchmarks while maintaining similar performance without outliers.
Specialized Cyclist Detection Dataset: Challenging Real-World Computer Vision Dataset for Cyclist Detection Using a Monocular RGB Camera

Recent accidents on roads involving cyclists and autonomous vehicles have raised an alarm in the industry to focus more on cyclist safety. Although there are plenty of datasets publicly available in the industry, they don't include enough instances of cyclists in different road conditions to run comprehensive tests. Therefore, in this work, we present a new publicly available Specialized Cyclist Dataset, which focuses solely on cyclist detection. Our dataset was recorded using a monocular RGB camera in various scenarios experienced by cyclists on roads in Autumn and Winter (with snow) for enabling researchers to run rigorous tests in various conditions. There are 62297 total images, about 18200 cyclists instances, and 30 different cyclists. Additionally in the dataset, we present the Specialized cyclist jersey with a diamond pattern designed specifically for improving detection accuracy compared to street clothes. For convenience, we utilized the popular KITTI labeling format and resolution in addition to Full HD resolution.
A Performance Analysis of Invariant Feature Descriptors in Eye Tracking based Human Robot Collaboration

For eye tracking applications in Human Robot Collaboration (HRC), it is essential for the robot to be aware of where the human gaze is located in the scene. Using feature detectors and feature descriptors, the human gaze can be projected to the image from which robot could know where a human is looking at. The motion that occurs during the collaboration may affect the performance of the descriptor. In this paper, we analyse the performance of SIFT, SURF, AKAZE, BRISK and ORB feature descriptor in a real scene for eye tracking in HRC where different variances co-exist. We use a robotic arm and two cameras to test the descriptors instead of directly testing on eye tracking glasses in order that different accelerations can be tested quantitatively. Results show that BRISK, AKAZE and SURF are more favourable considering accuracy, stability and computation time.
The Development of Single-Document Abstractive Text Summarizer During the Last Decade

As the number of electronic text documents is increasing so is the need for an automatic text summarizer. The summary can be extractive, compression, or abstractive. In the former, the more important sentences are retained, more or less in their original structure, while the second one involves reducing the length of each sentence. For the latter, it requires a fusion of multiple sentences and/or paraphrasing. This chapter focuses on the abstractive text summarization (ATS) of a single text document. The study explores what ATS is. Additionally, the literature of the field of ATS is investigated. Different datasets and evaluation techniques used in assessing the summarizers are discussed. The fact is that ATS is much more challenging than its extractive counterpart, and as such, there are a few works in this area for all the languages.
Create Digital Transformation Using Chatbots

In this chapter, we create digital transformation using Azure chatbots with a focus on customer satisfaction. Digital transformation using Azure can be accomplished in various ways by using Azure products and services such chatbots, cloud storage, identity and access management, IoT, databases, DevOps, AI and machine learning, and a plethora of products. Digital transformation is the phase during which Azure products and services help to engage your customers, empower your employees, optimize your operation, and transform the user experience. Let’s explore digital transformation using Azure chatbots.