Reviewerly/exHarmony huggingface dataset:
Evolutionary Procedural Content Generation for an Endless Platform Game

Making innovative, cohesive and appealing games has become inherently more difficult given the ever increasing competition in the digital games' market. Manually creating game content is expensive and time-consuming. Therefore, alternative approaches for game content creation are relevant for increasing the efficiency of the game development process. This is where procedural techniques step in. Even though they have been used by commercial games since the 1980s, it was only in recent years that this kind of approach has been given the righteous attention in the academic context. In this work, we propose a procedural content generation approach for creating infinite environments for a 2D platform runner game. The approach consists of a Genetic Algorithm that innovatively takes into account environment aesthetics as well as game's physics and rules in its fitness function. Therefore, the created environments should be pleasant and possible to be overcome by the player. An instantiation of the approach was developed using the Godot Game Engine. Time viability for in-game real-time generation and convergence to high/stable fitness values were experimentally evaluated. Our tests indicated parameter ranges that performed best in terms of environment quality and processing time were mutation rates between 0.5 % and 1 % aligned with a population ranging from 50 to 100 individuals. This approach is expandable to other games that have a tileman-based environments.
A Systematic Literature Review on Story-Telling for Kids using Image Captioning - Deep Learning

Image captioning is characterized as the cycle of producing inscriptions or printed depictions for pictures dependent on the substance of the picture. It is an AI task that includes both normal language proceedings furthermore, PC vision (for understanding picture substance). Auto picture inscribing is an ongoing and developing exploration issue these days. Step by step different new strategies are being presented to accomplish acceptable outcomes in this field. Be that as it may, there are still loads of consideration needed to accomplish results comparable to a human. This examination plans to discover in a deliberate manner that what unique and ongoing techniques and models are utilized for picture inscribing utilizing profound learning? What strategies are actualized to utilize those models? Furthermore, what strategies are bound to give great outcomes. For doing so, done an efficient writing audit on ongoing examinations from 2015 to 2020 from well-known information bases.
A Prediction Method Based on Improved Echo State Network for COVID-19 Nonlinear Time Series

This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters.Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.
Feature based Methods for Eye Gaze Tracking

Eye gaze has been depicted as one of the means for interaction with computer. The eye tracking systems perform feature extraction and determination on gaze direction. The feature based methods will estimate gaze direction based on the extracted features. The parameters of pupil, iris and eye corners will determine gaze direction. In this paper, two feature based gaze tracking methods are proposed. The first method is based on Fast Fourier Transform (FFT) and the second method is based on number of zeros in Z-domain. The search for template image in successive video frames tracks the pupil. The optical flow extracts the change in position. The velocity component determines displacement of template region, which helps in gaze direction estimation. The Fourier transform is computed by using the magnitude of velocity components, gives the frequency representation for each gaze direction. The number of zeros inside a specific range of circle in Z-domain aids in determining the gaze direction. The recognition rate of 98.5% and 99% are obtained for the methods based on FFT and zeros, respectively.
Capabilities of digital gamification resources to form the basis of information security

Abstract The research problem is determined from the need to implement the didactic capabilities of gamification tools to support the study of various aspects of information security and information protection. The study purpose is to identify opportunities for the effective use of digital gamification resources to form learners’ information security competencies that will allow to successfully socialize in a modern digital society in the future. The work methodology includes an analysis of the literature of foreign and domestic authors on the use of gamification resources in cognition, training; the method of systematization and generalization of facts about the problems of individual information security, the concepts of the formation of special digital literacy in the space of new software tools and devices. The potential of digital technologies regarding the development of free orientation skills in the modern information environment, the organization of search activities, the use of various cognition strategies is explored. The revealed opportunities are shown on the example of the organization of information interaction of the didactic process participants in the Class Craft gaming environment. The conclusion summarizes the possibilities for the effective use of digital gamification resources for solving cognitive, communicative and organizational tasks in compliance with information security standards.
Age Progression using Generative Adversarial Networks

This study presents a technique to generate face age progression by adopting a conditional generative adversarial network based approach. The best model resulting from a five-fold cross validation has an accuracy of 91.93%, False Omission Rate of 0.45% and Negative Prediction Value of 99.55%. Building on prior work, this paper has three contributions. First, the use of uneven age clusters is presented to account for more rapid and drastic ageing in babies and toddlers than older individuals. Second, perceptual losses rather than per-pixel losses are considered to enable identity preservation. Third, a facial recognition system is applied to verify the identity of individuals upon ageing. Identity preservation was achieved and confirmed, with a facial recognition accuracy of 92.4%. Visual fidelity was also confirmed, with 95.2% subjects identifying ageing in the conducted survey.
Framework With An Approach To The User As An Evaluation For The Recommender Systems

This research is a framework with an approach to the user as an evaluation for the recommendation system. Prediction algorithm can provide accuracy to the recommendation system. The recommendation system strongly influences user experience in the recommendation system. The relationship between objective system aspects and user behavior is carried out with a framework based on a collection of perceptions and evaluations with various aspects of subjective experience through personal and situational characteristics of user experiences. This research is also supported by related literature in mapping the framework. In this way, the framework can be validated. Analysis of Field trials and experiments with structural equation modeling. The results showed that the subjective system aspects and user experience could provide an explanation of why and how the user experience emerges from the recommendation system. Perceived quality and variation of recommendations are important mediators in predicting objective system aspects of user experience components such as perceived processes or difficulties, systems in the form of perceived system effectiveness, results in the form of choice of satisfaction. This study also found that there was a correlation of behavior from subjective aspects such as a lack of search results, this shows the results of the effectiveness of the system. There is a relationship between aspects of the system with personal and situational characteristics indicated by the number of feedback preferences from users in exchange for system usability and user privacy.
Open Data in Higher Education - A Systematic Literature Review

The open data (OD) initiative fosters open access to public information in an organization. It is a relatively new but very significant and potentially powerful approach that starts being adopted in different organizations. In the context of higher education, the adoption of this initiative starts being reported in the literature. In order to get a better understanding of open data (OD) in higher education, we conducted a systematic literature review. From a sample of 595 papers, we identified and selected 25 relevant papers for the review. Our findings suggest that open science, open government, and open university are main reasons to adopt OD. Models, frameworks, methods, and standards about OD have been adopted in higher education context. Different technological tools have been used for: data modeling, RDF data generation, data publishing, linking, and exploitation. However, some issues have been reported in the moment of adopting OD initiatives, mainly related to intellectual property, standardization, legal and operation aspects. Finally, the largest number of OD initiatives reported in the context of higher educations have been applied in European countries.
Planning and Scheduling for Large-Scale Robot Networks: An Efficient and Comprehensive Approach

Mobile robot groups incorporated with intelligence control are known to be efficient and useful in logistic, industry and public transportation implementations, serving for the field information acquisition, industrial material delivery, and passenger transportation needs. In these applications, the environments are typically dynamic and contains large number of uncertainties which will definitely affect the motion of the robot. A comprehensive solution for the task allocating and motion scheduling is of great importance. In addition, most of the existing approaches assume that the group level task allocating and the low level robot scheduling are fully independent of each other. Aiming to address these issues, in this paper, we investigate the integrative strategy which provides the synthetic key to connect task allocating with motion scheduling in an efficient and comprehensive manner. Numerical simulations with hundreds of unmanned logistic vehicles validate the effectiveness of the proposed integrative algorithms.
Digitalizing the Full Documents of Deck Log Book of Sail Training Ship “Kaiwo Maru I” and a Study for the Usage of the Data

The Sail Training Ship "Kaiwo Maru I" was built in 1930 and retired in 1989. After her retiring, she was moored at Fushiki-Toyama port and permanent preservation of her was decided. Afterward, events such as full-sail exhibition or maritime educational program have been held. And, many ship's documents, such as Deck log book, Engine log book, Radio log book, some construction documents and so on, have been preserved in the ship. These documents are historical heritage for the field of navigation, shipbuilding, maritime education and so on. So, we tried to create the digital archives of Deck log book. First, we took pictures of the log Book, which had been written at every day for about sixty years and one booklet for one month, by the digital camera. Secondary, we made a list of the booklet of the log Book. Next, we had actual-finding investigation of "Maiden oceangoing voyage to Truck islands", "Challenging voyage to Bering sea" and "Training voyage at Pacific War". These investigations find out the possibilities of some maritime education effects.
Error estimation of the multi-angle phase triangulation method for measuring the three-dimensional geometry of convex and extended objects

Abstract The work describes the estimation of the error of the multi-angle phase triangulation method for measuring the three-dimensional geometry of convex and extended objects. Theoretical estimates of the measurement error are made and the results of mathematical modeling are presented. It is shown that if the measurement error by the phase triangulation method has normal distribution, the results of stitching a three-dimensional surface will also have a normal distribution with similar characteristics.The multi-angle phase triangulation method for measuring the three-dimensional geometry of convex and extended objects provides the necessary level of measurement accuracy and can be used to obtain the three-dimensional geometry of complex-shaped convex and extended objects in industrial conditions.
Segmentation of Javanese Character in Ancient Manuscript using Connected Component Labeling

Indonesia is known as an archipelago country consisting of various tribes with their cultures and languages. Ancient manuscripts from these various tribes are widely found and need to be preserve. There has been much research on the Javanese script in the Manuscript, such as Javanese script segmentation, Javanese script introduction, and improvement of Javanese characters whose writing has been damage due to the storage process and the length of storage time. This research is focused only on localizing Javanese characters into characters that can be used in recognition of Javanese characters in future research. This study conducted a process of character segmentation of Javanese characters in the ancient Manuscript by using the connected component labeling method, which is applied as an algorithm used to detect interconnected blob regions in a binary image. The accuracy of the results of the segmentation process is quite good at 80.9%.
Extension of Rule-based SLICE Platform With Deep Learning Model

The meaning of intelligent systems has changed over time. Traditionally, it refers to an intelligent ruled-based context aware system, but nowadays, it is more often referred to as a deep learning intelligent system. Since these two intelligent systems are still insufficient to be used separately, this paper try to integrate the intelligent module of deep learning on SLICE, which was introduced as a rule-based intelligent IoT platform that easily provides intelligent services. First, in this paper, the pros and cons of the existing rule-based engine and the deep learning-based intelligence model are examined. After that, how to combine the advantages of each is discussed. Finally, this paper shows that the proposed intelligent system integration can be used to design and implement a service more easily and quickly through a feasibility test and simple performance check with a low-cost general-purpose Raspberry Pi.
Honest Majority MPC with Abort with Minimal Online Communication

In this work we focus on improving the communication complexity of the online phase of honest majority MPC protocols. To this end, we present a general and simple method to compile arbitrary secret-sharing-based passively secure protocols defined over an arbitrary ring that are secure up to additive attacks in a malicious setting, to actively secure protocols with abort. The resulting protocol has a total communication complexity in the online phase of \(1.5(n-1)\) shares, which amounts to 1.5 shares per party asymptotically. An important aspect of our techniques is that they can be seen as generalization of ideas that have been used in other works in a rather ad-hoc manner for different secret-sharing protocols. Thus, our work serves as a way of unifying key ideas in recent honest majority protocols, to understand better the core techniques and similarities among these works. Furthermore, for \(n=3\), when instantiated with replicated secret-sharing-based protocols (Araki et al. CCS 2016), the communication complexity in the online phase amounts to only 1 ring element per party, matching the communication complexity of the BLAZE protocol (Patra & Suresh, NDSS 2020), while having a much simpler design.
Latent Group Recommendation based on Double Fuzzy Clustering and Matrix Tri-factorization

Group recommendation has received great attention owing to its practical value in real applications. However, group members are implicit and groups are formed occasionally in some scenarios. Existing solutions for latent group recommendation assumes a user belongs to a specific group, and totally ignore the possible correlation between the user' s preferences and other groups' preferences. In addition, existing methods cannot deal with new items cold-start problem effectively because they only focus on which items are favored by the group without considering the hidden related information between items. These weaknesses usually lead to poor performance of latent group recommendation. To address the problems above, this paper proposes a latent group recommendation method based on double fuzzy clustering and matrix tri-factorization (DFCMTF -LGR). Firstly, this method utilizes unsupervised learning to implement potential feature extraction and double fuzzy clustering for users and items. Secondly, a novel matrix tri-factorization method is presented to adjust the membership of user-to-group, item-to-item category, and the incidence of group-to-item category is obtained. Finally, latent groups are detected according to user-to-group membership, and group rating can be generated in accordance with group-to-item category incidence matrix and item-to-item category membership. Experimental results on real datasets demonstrate that our proposed DFCMTF-LGR has better performance compared with state-of-the art methods.
An Investigation of End-to-End Speech Recognition Using Model Adaptation for Dysarthric Speakers

In this paper, we present an end-to-end automatic speech recognition (ASR) system for dysarthric speech. Because the speaking style of a person suffering from an articulation disorder is quite different from that of a physically unimpaired person, speech recognition systems for such persons need to be constructed in such a way that they specialize in meeting the needs of such dysarthric people. However, the amount of training data that can be collected from dysarthric people is limited because of their large burden. Therefore, it is a challenge to effectively train an ASR model for dysarthric people. In this paper, we introduce a model adaptation approach to train a more accurate model with limited training data, which adapts an ASR model trained by non-dysarthric speech samples for dysarthric speech recognition. From our experiments on an ASR task with two dysarthric subjects, the model adaptation approach with non-dysarthric speech showed better performance than training from scratch.
An integrated approach to renew software contract using machine learning.

Contract renewal is critical to maintaining a company’s recurring revenue source. Therefore, there is a significant emphasis on setting up an efficient process for renewal. In this study, a machine learning technique was followed to improve contract renewal rates. In addition to this, key factors affecting renewal rates were also studied in detail. The solution presented in this study used an unsupervised machine learning technique to segment high-risk resellers with relatively lower probability of renewal, which was further actioned upon by a proactive contact strategy soliciting a contract renewal. This solution was tested and monitored for a period of three quarters. It resulted in an incremental improvement in the renewal rate for the company. As part of the implementation, a user interface application was also developed, which enabled the sales specialist to list and contact high-risk (or underperformer) resellers quarter-on-quarter.
Sampling from a Mixture of Different Groups of Coupons

A collector samples coupons with replacement from a pool containing g uniform groups of coupons, where “uniform group ” means that all coupons in the group are equally likely to occur (while coupons of different groups have different probabilities to occur). For each j = 1,…, g, let Tj be the number of trials needed to detect Group j, namely to collect all Mj coupons belonging to it at least once. We first derive formulas for the probabilities P{T1 < … < Tg} and $$P\left\{{{T_1} = \wedge _{j = 1}^g\,{T_j}} \right\}$$ . After that, without severe loss of generality, we restrict ourselves to the case g = 2 and compute the asymptotics of P{T1 < T2} as the number of coupons grows to infinity in a certain manner. Then, we focus on T:= T1 ∨ T2, i.e. the number of trials needed to collect all coupons of the pool (at least once), and determine the asymptotics of E[T]and V [T], as well as the limiting distribution of T (appropriately normalized) as the number of coupons becomes large.
Partner Selection for Agents: A Utility Theory Approach

This paper proposes an idea for modeling of matching between agents based on utility theory. The basic idea comes from the stable matching problem, and this focuses on how an agent makes an ultimate decision when it comes to finding a matching agent based on utilities that other agents have as well as the agent's preferences for each utility option. This paper begins with introductions of agent organization, partner selection in Agent- Based Model, and utility theories. The proposed approach of this paper includes aspects of directions and applications. In terms of the aspect of directions, pre-defined concepts such as utility options, perceptual and actual utility, and deal-breakers are introduced to understand applications better. Also, two possible scenarios with basic and high complexity were handled as applications for partner matching between agents based on utilities. There are three merits of the proposed approach, which are: (1) both the agent's benefit and its potential partner agent's benefits are considered for mate selection; (2) by employing Congregation Organizational Paradigm, an agent's partner selection approach can be applied in dynamic environments by seeking its optimal partner in another networking cluster; and (3) by employing threshold and deal-breakers, a rational agent can avoid making a decision which lowers its level of happiness or benefits.
Learning Enriched Features for Video Denoising with Convolutional Neural Network

Video denoising is of great significance in video processing when shooting conditions are complex such as dynamic scenes and low light. Although existing algorithms have already achieved remarkable denoising performance, the inference time of them is usually impractical for real-time applications. In this paper, we propose a convolutional neural network architecture for video denoising. In contrast to other existing CNN-based methods, our approach utilizes different proportion convolutional kernel numbers in a block for extracting enriched features. Channel attention mechanism is integrated in the network to enhance the denoising performance. The network only needs three contiguous frames and noise map as inputs, which leads to a similar excellent running time to the state-of-the-art. We compare our method with different conventional algorithms VBM4D, VNLB and the state-of-the-art CNN-based method FastDVDnet. Experiment results indicate that our method outputs more convincing results in visual and more robustness than others in both peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) indexes.
A Statistical Analysis Method for Reliability Data of Aerospace Components Based on Association Rules

In this paper, as the aerospace components reliability test data resources are rich, randomness, characteristics of discrete, draw lessons from the analysis of large data, in this paper, a method based on association rule mining data statistical analysis is proposed, using this method, easily neglected, useful data model or rules can be excavated, this method provide data support for quality assurance model components.