Reviewerly/exHarmony huggingface dataset:
Structure guided image completion using texture synthesis and region segmentation

Image completion techniques are often used to fill in damaged images. In this study, a novel method is proposed that can handle large hole regions surrounded by different types of texture. This method is based on reconstructing damaged texture structure and texture synthesizing. The images are segmented into several regions and similar regions touching the hole region are linked, thereby resulting in new regions. It is found that the new regions provide sufficient information to generate a guidance map, which serves as a soft constraint in the inpainting process. With the guidance map, the hole is filled by using a global optimization texture-synthesis method. The experimental results illustrate that this novel method is a potential tool for image completion.
An efficient parameter update method of 360-degree VR image model

Recently, with the rapid growth of manufacture and ease of user convenience, technologies utilizing virtual reality images have been increasing. It is very important to estimate the projected direction and position of the image to show the image quality similar to the real world, and the estimation of the direction and the position is solved using the relation that transforms the spheres into the expanded equirectangular. The transformation relationship can be divided into a camera intrinsic parameter and a camera extrinsic parameter, and all the images have respective camera parameters. Also, if several images use the same camera, the camera intrinsic parameters of the images will have the same values. However, it is not the best way to set the camera intrinsic parameter to the same value for all images when matching images. To solve these problems and show images that does not have a sense of heterogeneity, it is needed to create the cost function by modeling the conversion relation and calculate the camera parameter that the residual value becomes the minimum. In this article, we compare and analyze efficient camera parameter update methods. For comparative analysis, we use Levenberg–Marquardt, a parameter optimization algorithm using corresponding points, and propose an efficient camera parameter update method based on the analysis results.
On efficiency of methods and algorithms for solving optimization problems considering objective function specifics

Introduction. The estimation of efficiency of methods and algorithms for solving optimization problems with a vector criterion and a set of nonlinear constraints is considered. The approach that allows proceeding to an optimization problem with a single objective function (i.e., an unconditional optimization problem) after equivalent transformations is described. However, the objective function obtained in this way has properties (nonlinearity, multimodality, ravine, high dimension) that do not allow classical methods to be used to solve it. The presented work objective is to develop hybrid methods, based on combinations of the algorithms inspired by wildlife with other approaches (gravitational and gradient) for the solution to this problem. Materials and Methods. New methods to solve the specified problem are developed. A computer experiment was conducted on a number of test functions; its analysis was performed, showing the efficiency of various combinations on various functions. Research Results. The efficiency of hybrid algorithms that combine the following approaches is evaluated: genetic and immune; methods of swarm intelligence and genetic and immune; immune and swarm with gravity and gradient. Discussion and Conclusions. The hybrid algorithms in optimization problems are studied. In particular, decisions can be made on their basis under the management of compound objects in the military and industrial sectors, in the creation of innovative projects related to the digital economy. It is established that the type of the objective function affects the result much more than the combination of algorithms.
BAB-QA: A New Neural Model for Emotion Detection in Multi-party Dialogue

In this paper, we propose a new neural model BAB-QA to address the task of emotion detection in multi-party dialogues, which aims to detect emotion for each utterance in a dialogue among four label candidates: joy, sadness, anger, and neutral. A variety of models have been proposed to solve this task, but few of them manage to capture contextual information in a dialogue properly. Therefore, we adopt a Bi-directional Long Short-Term Memory network (BiLSTM) and an attention network to obtain representations of sentences and then apply a contextualization network to refine the sentence representations for classification. More importantly, we propose and incorporate a new module called QA network in our model, which is inspired by natural language inference tasks. This QA network enables our model to acquire better sentence encodings by modeling adjacent sentences in a dialogue as question-answer pairs. We evaluate our model in the benchmark EmotionX datasets provided by SocialNLP2018 and our model achieves the state-of-the-art performance.
A Brief Subjective Perspective on the Development of Quantum Technologies 2.0

I trace the early developments in quantum technologies 2.0 using the example of D-Wave Systems and discuss the key problems which must be solved before these technologies can be brought to fruition.
Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing

As one of the typical clustering algorithms, heuristic clustering is characterized by its flexibility in feature integration. This paper proposes a type of heuristic algorithm based on cognitive feature integration. The proposed algorithm employs nonparameter density estimation and maximum likelihood estimation to integrate whole and local cognitive features and finally outputs satisfying clustering results. The new approach possesses great expansibility, which enables priors supplement and misclassification adjusting during clustering process. The advantages of the new approach are as follows: (1) it is effective in recognizing stable clustering results without priors given in advance; (2) it can be applied in complex data sets and is not restricted by density and shape of the clusters; and (3) it is effective in noise and outlier recognition, which does not need elimination of noises and outliers in advance. The experiments on synthetic and real data sets exhibit better performance of the new algorithm.
A new area-based convexity measure with distance weighted area integration for planar shapes

In this paper we propose a new area-based convexity measure. We assume that convexity evaluation of an arbitrary planar shape is related to the total influence of dents of the shape, and discover that those attributes of the dents, such as the position, area, and depth with respect to the Geometric Center of Convex Hull (GCCH) of the shape, determine the dent influence. We consider that the convex hull of the shape consists of infinitely small patches, to each of which we assign a weight showing the patch influence. We can simply integrate all the patch weights in any regions within the convex hull to calculate their total influence. We define this operation as the Distance Weighted Area Integration, if the weight is associated with the Euclidean distance from the patch to the GCCH. Our new measure is a distance weighted generalization of the most commonly used convexity measure, making this conventional measure fully replaceable for the first time. Experiments demonstrate advantages of the new convexity measure against the existing ones.
Knowledge Graph Embedding with Order Information of Triplets

Knowledge graphs (KGs) are large scale multi-relational directed graph, which comprise a large amount of triplets. Embedding knowledge graphs into continuous vector space is an essential problem in knowledge extraction. Many existing knowledge graph embedding methods focus on learning rich features from entities and relations with increasingly complex feature engineering. However, they pay little attention on the order information of triplets. As a result, current methods could not capture the inherent directional property of KGs fully. In this paper, we explore knowledge graphs embedding from an ingenious perspective, viewing a triplet as a fixed length sequence. Based on this idea, we propose a novel recurrent knowledge graph embedding method RKGE. It uses an order keeping concatenate operation and a shared sigmoid layer to capture order information and discriminate fine-grained relation-related information. We evaluate our method on knowledge graph completion on benchmark data sets. Extensive experiments show that our approach outperforms state-of-the-art baselines significantly with relatively much lower space complexity. Especially on sparse KGs, RKGE achieves a 86.5% improvement at Hits@1 on FB15K-237. The outstanding results demonstrate that the order information of triplets is highly beneficial for knowledge graph embedding.
Design and Implementation of Autonomous Ground Vehicle for Constrained Environments

The design, development and validation of the Eklavya 6.0 have been presented in this paper. Eklavya 6.0, a three-wheeled differential drive autonomous robot which can navigate to prespecified GPS coordinates as well as through lanes was developed to participate in the Autonomous Navigation Challenge of the 26th Intelligent Ground Vehicle Competition(IGVC). The mechanical, electronic, software architecture sub-modules including localization, lane detection navigation and motion planning have been discussed.
A Critical Analysis of Topics in Software Architecture and Design

Software architecture and design is an important component in the software engineering field.This aspect of software engineering covers the functional and non-functional requirements of any system being proposed to be developed, while software architecture deals with non-functional requirements, software design entails the functional requirements.The objective of this paper is to critically analyze current topics in Software architecture and design.The method of analysis involved the use of inclusion and exclusion criteria of papers published in journals and conferences.These papers were accessed from digital libraries like ScienceDirect, and IEEE explore, with a quantitative approach of analysis been imbibed.From the analysis, the result showed that, of 35 papers used in analysis, 34.3% discussed stakeholders' involvement and decisions in software design.17.1% for design quality, 20% examined software reuse while 11.4% discussed software evaluation and 8.6% of papers reviewed discussed software management, evolution and software development life cycle each which should be more focused as it is the fundamentals of software design and architecture.From the analysis derived, stakeholder's involvement and decision in software design is an integral part in software building for effective use.Thereby making researchers dwell more on the topic.The least discussed topics was due to the expectations of researchers.Expecting readers to have a fore knowledge of the fundamentals of design which includes software management, evolution and software development life cycle.
Privacy-Preserving Pre-Consensus Protocol for Blockchains

A blockchain is a technology that allows transactions to be processed and committed data to be shared among participants without a central server. To implement applications among restricted parties with permission such as asset sales and trades, a special type of blockchain system called a private blockchain is used. Additionally, the demand for privacy preservation where sensitive information such as the trade amounts and balances is not disclosed is increasing. However, privacy preservation in a typical blockchain setting is difficult because it prevents parties that are not involved in the transaction from checking the correctness of the transaction being processed, which may lead to unintended or invalid transactions. To address this issue, herein we propose a protocol that allows the consistency with regard to the transaction amount and the balance to be checked without disclosing their values. Specifically, we exploit a homomorphic encryption to encrypt the transaction amount and balance. Thus, the correctness of a transaction can be publicly verified by the parties using the zero-knowledge proof without a trusted third party.
A computational model of scientific discovery in a very simple world, aiming at psychological realism

We propose a computational model of human scientific discovery and perception of the world. As a prerequisite for such a model, we simulate dynamic microworlds in which physical events take place, as well as an observer that visually perceives and makes interpretations of events in the microworld. Moreover, we give the observer the ability to actively conduct experiments in order to gain evidence about natural regularities in the world. We have broken up the description of our project into two pieces. The first piece deals with the interpreter constructing relatively simple visual descriptions of objects and collisions within a context. The second phase deals with the interpreter positing relationships among the entities, winding up with elaborated construals and conjectures of mathematical laws governing the world. This paper focuses only on the second phase. As is the case with most human scientific observation, observations are subject to interpretation, and the discoveries are influenced by these interpretations.
Creating Value Added for an Enterprise by Managing Information Security Incidents

Abstract This article presents the structure and analysis of information security incidents in a production company in 2015-2017. The purpose of the analysis is to identify incidental events and their frequencies. The analysis includes the occurrence of notifications, threatening events, employee errors and false alarms. The conducted research includes also the procedure for handling the incident in the enterprise. The enterprises very often avoid informing their contractors about the occurrence of incidents. Thanks to the analysis of incidents and a clearly defined action plan, the examined enterprise tested the incidents and actions taken with them as a method of creating the added value of the enterprise during the period under consideration. The conducted research has shown that contractors who are aware of preventive actions taken, as well as those affecting information security even after the occurrence of an incident, are more willing to provide trust and even support to the surveyed enterprise. The conducted analysis is a pilot study carried out in one large enterprise in the metallurgical industry. The aim of the conducted research is to show that the incident or negative event may have a positive impact on the company's image. The research was carried out with the use of a questionnaire and in-depth interview with representatives of enterprises that are co-operators of the examined company.
Effects of Educational Support Robots using Sympathy Expression Method with Body Movement and Facial Expression on the Learners in Short and Long-term Experiments

Recently, educational-support robots have been attracting increasing attention as studying-support gadgets.Previous studies used the sympathy expression method in which the robot expressed emotions in sympathy with the learners; however, the robots considered in those studies expressed only facial emotions.Presently, there is no study that uses body movements together with facial expressions in the sympathy expression method.Thus, in this paper, we examine the effects of two types of robots that have different method of expressing emotions on learners in two experiments.
Fact, Conjecture, Hearsay and Lies: Issues of Uncertainty in Natural Language Communications

Humans are very important sources of information for intelligence purposes. They are multi-modal: they see, hear, smell, and feel. However, the information which they relay is not simply that which they personally experience. They may pass on hearsay, they form opinions, they analyze and interpret what they hear or see or feel. Sometimes they pass on ambiguous, vague, misleading or even false information, whether intentional or not. However, whether imprecise or vague, when humans communicate information, they often embed clues in the form of lexical elements in that which they pass on that allows the receiver to interpret where the informational content originated, how strongly the speaker herself believes in the veracity of that information. In this chapter, we look at the ways in which human communications are uncertain, both within the content and about the content. We illustrate a methodology which helps us to make an initial evaluation of the evidential quality of information based upon lexical clues.
A Markovian Approach to Evaluate Session-Based IR Systems

We investigate a new approach for evaluating session-based information retrieval systems, based on Markov chains. In particular, we develop a new family of evaluation measures, inspired by random walks, which account for the probability of moving to the next and previous documents in a result list, to the next query in a session, and to the end of the session. We leverage this Markov chain to substitute what in existing measures is a fixed discount linked to the rank of a document or to the position of a query in a session with a stochastic average time to reach a document and the probability of actually reaching a given query. We experimentally compare our new family of measures with existing measures – namely, session DCG, Cube Test, and Expected Utility – over the TREC Dynamic Domain track, showing the flexibility of the proposed measures and the transparency in modeling the user dynamics.
Toward Recognition and Classification of Hindi Handwritten Document Image

With the increased demand of digitization of Indic scripts in today’s world, many Devanagari printed and handwritten text recognition and extraction techniques have been developed and are used in industries, corporate, and institutional domain areas. Because of the script and character structure difficulties, and handwritten content-based criticalities, Hindi handwriting processing is considered as a big bottleneck in recognition systems. This paper introduces NMC handwriting types and complexity evaluators for Hindi language. The inherent challenges of handwriting are discussed further. Although several Hindi-based handwritten character recognizers have been developed, they are limited to the segmentation and identification of character images only. So, this paper proposes a new idea of offline Hindi handwritten document classification, which first recognizes and classifies the character images, and then classifies the document image into the predefined category. In support of this concept, this paper provides a case study using a set of Hindi handwritten documents and shows their segmentation and classification results. The proposed system puts a step ahead in the direction of automatic document image classification.
A compact index for order‐preserving pattern matching

Summary Order‐preserving pattern matching has been introduced recently, but it has already attracted much attention. Given a reference sequence and a pattern, we want to locate all substrings of the reference sequence whose elements have the same relative order as the pattern elements. For this problem, we consider the offline version in which we build an index for the reference sequence so that subsequent searches can be completed very efficiently. We propose a space‐efficient index that works well in practice despite its lack of good worst‐case time bounds. Our solution is based on the new approach of decomposing the indexed sequence into an order component , containing ordering information, and a δ component , containing information on the absolute values. Experiments show that this approach is viable, is faster than the available alternatives, and is the first one offering simultaneously small space usage and fast retrieval .
Developing a General Video Game AI Controller Based on an Evolutionary Approach

The field of general intelligence is one, where humans can still easily outperform machines. In the context of our work we describe it as the ability to learn an activity, like playing a game, without any prior knowledge of goals and rules. The agent has to learn by doing/playing and examining the consequences of its actions. Many traditional techniques in reinforcement learning, such as SARSA and Q-Learning, can provide a good solution to this category of problems. In our paper, however, we propose an alternative method based on evolutionary algorithms to overcome the extensive computing for all state-action pairs needed in traditional approaches. We have evaluated various parent selection algorithms and two different fitness functions. "The General Video Game AI Competition" (GVGAI), where contestants submit a playing agent programmed with some learning algorithm to be tested against unknown games, has been used as a benchmark for the performance of our implementation.
Evaluation of Stratified Validation in Neural Network Training with Imbalanced Data

Validation data is widely used for measuring the generalization performance of models in machine learning such as neural network training. However, because usually it is randomly extracted from training data, imbalanced validation data can be obtained, resulting in an unstable validation for models. To deal with this problem, we have used a stratified validation, which is a conventional method for preparing balanced validation data by making the ratio of the numbers for each class of the validation samples the same as that of the training samples. We have evaluated the stratified validation for a multilayer perceptron and convolutional neural networks with several benchmark datasets.
Detection of Bursty and Significant Keyphrases from Wikipedia edit history

In an online collaboration system such as Wikipedia, edit history is stored as revisions. Topics of articles or categories grow and fade over time, and evolutionary information is retained in its edit history. We consider that a great amount of information that is related to real life events is hidden in such edit history of documents. This paper focuses on a particular temporal text mining task: effectively extracting keyphrases from burst periods in the edit history of Wikipedia articles or category. We first combine the ARIMA model with a decay function to find typical edit burst periods, then do keyphrase extraction on burst periods to reveal topics of bursts. However, keyphrase extraction methods, such as TextRank, do not consider temporal trends in text stream. In this paper, we propose TextRank_nfidf which reflects temporal trends into phrase node weights, by computing smoothed difference of editing frequency between revisions. We confirm that detected bursts and keyphrases are matching well with events along the timeline.