Reviewerly/exHarmony huggingface dataset:
Inclusive Education Through Accessible Mobile Serious Games

Accessibility has been defined as the ability of an object to be used despite the condition or disability of a person. However, it is a feature that often has not been taken into account in the design of products or services, and the mobile serious game market is no exception. Accessibility guidelines were defined through the consolidation of different initiatives and good practices of video game developers as well as groups interested in providing accessibility to video games. Once the guidelines were defined, a method for evaluating the accessibility of mobile serious games was developed and applied in a mobile serious game. The purpose of this document is to propose an evaluation tool for those who wish to develop accessible mobile serious games for people with impairments and improve inclusive education.
Characteristics of Lexical Spectra of Texts in the Problem of Establishing Authorship

The lexical spectrum is a significant characteristic for solving the problem of determining the authorship of texts (for example, when solving the problem of attribution of texts that may belong to F. M. Dostoevsky). However, the application of some data mining methods (for example, decision trees) requires representing the spectrum as a single number that would adequately reflect its structure. We consider the approximation of lexical spectra (at the dictionary level and at the text level) by hyperbolic and exponential curves. Based on the articles from the pre-revolutionary journal “Time” (1861–1863), it is shown that the coefficients of hyperbolic regression approximate the data much better than the coefficients of the exponential curve. In this case, the distance $$\chi ^2$$ was used to determine the difference between the spectra. The research was carried out using the SMALT information system, where automated marking of works was implemented with manual control of specialists-philologists.
Hydra: Pipelineable Interactive Arguments of Knowledge for Verifiable Neural Networks

We present Hydra, a novel verifiable computation system for neural networks. In this environment, a client (verifier) requests a server (prover) to evaluate a computation represented as an arithmetic circuit. The server returns the result of said computation, as well as a proof that attains to its correctness. Hydra introduces an interactive argument scheme protocol geared towards the efficient pipelining of general arithmetic circuit verification, where layers of the circuit can be proven asynchronously. Compared to non-interactive SNARKs which rely on either knowledge type assumptions or the Random Oracle model and theoretical non-interactive arguments based on standard assumptions that are not useful in practice, Hydra achieves a sweet spot with a practical approach. From standard assumptions, we collapse the round complexity to polylogarithmic to the width of the circuit. We demonstrate the effectiveness of this protocol in general cloud computing environments, and also apply this protocol in the specific context of cloud-based deep neural network verification. We propose a new interactive neural network quantization algorithm to convert a neural network into a provable arithmetic circuit representation, and leverage Hydra's pipelined approach to efficiently verify neural networks composed of many layers in both inference and training. We obtain protocol time efficiency improvements of up to 34.8 × for general computation while also reaching state of the art accuracy (99.5%) on MNIST for our neural network framework.
An Analysis of Neural Architecture Search and Hyper Parameter Optimization Methods

The growing interest for Deep Learning has leading the develop of methods for Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO). Generally, architectures are designed in an iterative and expensive process of trial and error, besides being restricted to designer creativity and its specific knowledge about the topic. This paper presents an analysis and discussion about the frequent algorithms used to automate neural networks’ design and tuning of its hyperparameters. Similarly, Floating Point Operations per Second (FLOPS), training time, and network complexity are complementary objectives to accuracy in classification, conceiving the performance as the sum of precision and computational consumption. This study seeks to pinpoint possible future directions to reduce human intervention in constructing machine learning models and understanding Artificial Neural Networks.
Systematizing the Meta-Analytical Process in Software Engineering

The generalization of knowledge is a necessary part of every scientific field. Meta-analysis is already advocated as a tool for generalization in different areas such as medicine, psychology, business, and this process is already standardized for them. Software engineering started using meta-analysis as a tool for aggregating results from families of experiments, but not so long for generalization of results coming from different studies, and for this purpose, the meta-analytical approach is not yet clarified. In this paper, we attempt to systematize the application of meta-analysis as a secondary study to the software engineering field suggesting our preliminary protocol. To see the reliability of the proposed protocol we conducted several studies using it. Following even uniform protocol with these studies, we identified the issues preventing the wide usage of meta-analysis in software engineering and proposed our solutions for them.
Spatial analysis of health facility service coverage in handling of COVID-19 patients in the area Surabaya City Settlement

Abstract Settlements in the city of Surabaya are growing rapidly along with the increasing number of residents in the city of Surabaya. Since the COVID-19 pandemic, the population in the city of Surabaya has decreased. The city of Surabaya is included in the top 10 districts/cities with the highest cumulative number of deaths and the highest death rate per 100 thousand inhabitants. The widespread use of Geographic Information Systems (GIS) and technological developments in obtaining, recording, and collecting spatial data can be utilized to solve current problems. The purpose of this study is to develop a geographic information system for the coverage of health facilities for residential areas in the city of Surabaya. The first stage is to identify the location points for the distribution of COVID-19 health facilities and residential areas, analyze the reach of COVID-19 health facility services, and analyze the service coverage of settlements to the services of COVID-19 health facilities. The method used in this research is spatial analysis using the Multiple Ring Buffer method. The output is the service covergae of settlements to hospitals is divided into two categories, namely the medium category by 98% and the distant category by 2%. Meanwhile, for the housing service covergae of the health community centre is 100% in the medium category.
Extension-Compression Learning: A deep learning code search method that simulates reading habits

To speed up the efficiency of software development, the ability to retrieve codes through natural language is fundamental. At present, the approach of code search based on deep learning has been extensively researched and achieved a lot of results. However, these models are much complex and the training relies on artificially extracted features. Different from other deep learning models, we simulate people's reading habit of expanding content first and then refining content when learning new knowledge and propose the concept of Extension-Compression Learning. The model can effectively express the features of code and natural language through Extension Learning and Compression Learning. We evaluate the effect of the approach on the code search task with a small dataset and a large dataset, and the results show that all indicators are better than those of other approaches that embed code and text into a joint vector space.
LAMDA-HSCC: A semi-supervised learning algorithm based on the multivariate data analysis

• A semi-supervised learning algorithm based on a multivariate data analysis. • A new metric for semi-supervised contexts, called Semi-Supervised Criterion. • The first semi-supervised learning algorithm based on the LAMDA approach. • Migration, merging, and separation processes for assignment to classes/clusters. • Membership degree of an individual to a class/cluster based on features. In this work, we propose a semi-supervised learning algorithm, which can solve problems of classification, clustering, or a combination of them. This algorithm is based on the LAMDA family (Learning Algorithm for Multivariate Data Analysis), which computes the membership degree of an individual to a class or cluster considering the contribution of all features/descriptors. Thereby, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Also, it is composed of three sub-models for the migration, merging, and separation problems to improve the assignment of individuals to the classes/clusters. This proposal, called LAMDA- HSCC (Hybrid Scenarios of Classification and Clustering), is applied to several datasets of classification, clustering, and hybrid, in order to compare its performance with other algorithms, showing very encouraging results. Particularly, we define a new metric for evaluating performance in a semi-supervised context, called the Semi-Supervised Criterion (SSC), in which our approach achieves very good results.
Design Order Guided Visual Note Layout Optimization

With the goal of making contents easy to understand, memorize and share, a clear and easy-to-follow layout is important for visual notes. Unfortunately, since visual notes are often taken by the designers in real time while watching a video or listening to a presentation, the contents are usually not carefully structured, resulting in layouts that may be difficult for others to follow. In this article, we address this problem by proposing a novel approach to automatically optimize the layouts of visual notes. Our approach predicts the design order of a visual note and then warps the contents along the predicted design order such that the visual note can be easier to follow and understand. At the core of our approach is a learning-based framework to reason about the element-wise design orders of visual notes. In particular, we first propose a hierarchical LSTM-based architecture to predict a grid-based design order of the visual note, based on the graphical and textual information. We then derive the element-wise order from the grid-based prediction. Such an idea allows our network to be weakly-supervised, i.e., making it possible to predict dense grid-based orders from visual notes with only coarse annotations. We evaluate the effectiveness of our approach on visual notes with diverse content densities and layouts. The results show that our network can predict plausible design orders for various types of visual notes and our approach can effectively optimize their layouts in order for them to be easier to follow.
Gaze Mapping for Immersive Virtual Environments Based on Image Retrieval

In this paper, we introduce a novel gaze mapping approach for free viewing conditions in dynamic immersive virtual environments (VEs), which projects recorded eye fixation data of users, who viewed the VE from different perspectives, to the current view. This generates eye fixation maps, which can serve as ground truth for training machine learning (ML) models to predict saliency and the user’s gaze in immersive virtual reality (VR) environments. We use a flexible image retrieval approach based on SIFT features, which can also map the gaze under strong viewpoint changes and dynamic changes. A vocabulary tree enables to scale to the large amounts of data with typically several hundred thousand frames and a homography transform re-projects the fixations to the current view. To evaluate our approach, we measure the predictive quality of our eye fixation maps to model the gaze of the current user and compare our maps to computer-generated saliency maps on the DGaze and the Saliency in VR datasets. The results show that our method often outperform these saliency predictors. However, in contrast to these methods, our approach collects real fixations from human observers, and can thus serve to estimate ground truth fixation maps in dynamic VR environments, which can be used to train and evaluate gaze predictors.
Protecting the Integrity of IoT Sensor Data and Firmware With A Feather-Light Blockchain Infrastructure

Smart cities deploy large numbers of sensors and collect a tremendous amount of data from them. For example, Advanced Metering Infrastructures (AMIs), which consist of physical meters that collect usage data about public utilities such as power and water, are an important building block in a smart city. In a typical sensor network, the measurement devices are connected through a computer network, which exposes them to cyber attacks. Furthermore, the data is centrally managed at the operator’s servers, making it vulnerable to insider threats.Our goal is to protect the integrity of data collected by large-scale sensor networks and the firmware in measurement devices from cyber attacks and insider threats. To this end, we first develop a comprehensive threat model for attacks against data and firmware integrity, which can target any of the stakeholders in the operation of the sensor network. Next, we use our threat model to analyze existing defense mechanisms, including signature checks, remote firmware attestation, anomaly detection, and blockchain-based secure logs. However, the large size of the Trusted Computing Base and a lack of scalability limit the applicability of these existing mechanisms. We propose the Feather-Light Blockchain Infrastructure (FLBI) framework to address these limitations. Our framework leverages a two-layer architecture and cryptographic threshold signature chains to support large networks of low-capacity devices such as meters and data aggregators. We have fully implemented the FLBI’s end-to-end functionality on the Hyperledger Fabric and private Ethereum blockchain platforms. Our experiments show that the FLBI is able to support millions of end devices.
Intelligent Identification Apparoch for Healthcare Systems

Intrusion detection is important for healthcare system, where Patient health data is a part of healthcare that is vulnerable to attacks by hackers. These attacks are among the major challenges facing the health system, especially healthcare system which has a weak infrastructure that makes it simpler for these systems to be targeted. Therefore, an accurate attacks detection system must be designed to prevent health data from being manipulated. This paper proposed a system to detect whether there is an attack on healthcare data or not. The system goes through two phases which are data preprocessing & classification. The first phase contains several steps where we merged our existing data, Filling the Missing Values & Scaling. In the second phase, we feed the processed data to three machine learning classifiers, namely Naive Bayes (NB), K-Nearest Neighbors (KNN) & Support Vector Machine (SVM) in order to classify whether there was an attack or not. Through experiments & comparing the results of the three classification algorithms with each other in terms of detection accuracy, the SVM algorithm was found to have the highest classification accuracy rate of 94% compared to KNN algorithm 92% & NB algorithm 90%.
Multi-objective auto-scaling scheduling for micro-service workflows in hybrid clouds

A novel multi-objective (cost, delay, and reliability) auto-scaling optimisation model is proposed for micro-service workflows in containerised hybrid clouds. We compare the container-based model with VM-based model and conclude that the former significantly supersedes. The benchmark of three mainstream algorithms is conducted by the Hypervolume metric, showed that the performance of MOEA/D is inferior to NSGA family, and NSGA-III is not always superior to NSGA-II. So we design an improved NSGA-II based on dynamically changing crossover and mutation operators, which outperforms NSGA-III both in stability and performance by over 60% and 80% in all multi-scale tests.
Impact of Fire Assistant Personnel on the Reduced Amount of Fire Damage in South Korea

Previous studies have examined the status of volunteer fire brigade members, who are classified as firefighting assistants, observed problems in the firefighting administration, and suggested solutions for these problems. These studies are meaningful because they have led to the proposal and implementation of policies for an efficient firefighting administration. However, the references do not provide evidence on how firefighting assistants reduce the amount of fire damage in firefighting administration. Therefore, this study aimed to statistically analyze the impact of fire assistant personnel on the reduction in the amount of property damaged (RAPD) in South Korea. For this purpose, panel data based on firefighting officers (FFOs), volunteer firefighters (VFFs), obligatory firefighters (OFFs), and the RAPD of 16 metropolitan councils in South Korea from 2011 to 2018 were obtained and analyzed. The results demonstrate that population (POP)/FFO, VFF/FFO, and OFF/FFO did not significantly influence RAPD (p &lt; 0.01). However, negative regression coefficients are shown for POP/FFO and VFF/FFO, and positive regression coefficients are shown for OFF/FFO. These results can form an evidence base to explain the relationship between fire assistant personnel and RAPD.
Time-aware sport goods sale prediction for healthcare with privacy-preservation

Sports industry has been playing an important role in achieving good healthcare for public. However, with the advent of COVID-19, sports industry has been influenced significantly and the industry scale is decreased considerably. In this situation, how to accurately predict the sports industry scale in terms of production and consumption is becoming a practical and valuable task, because the whole world's economy is not growing stably and users' demand to sport goods is fluctuating sharply. However, three challenges are often existing in the sports industry scale prediction. First of all, there are so many kinds of sport goods that it is hard to quickly predict their future production or consumption scales accurately. Second, for a certain sport commodity, its production or consumption scale is often related to time especially in the COVID-19 environment. Third, sports industry scale data often contain some privacy, which probably disables data stakeholders to disclose their data. In view of these three challenges, a novel sports industry scale prediction approach (named SISP) is proposed for healthcare, which is basically according to time series analysis. Through SISP approach, we can quickly and accurately predict the future production or consumption scales of sport goods, in a privacy-aware way. At last, we validate the feasibility of the proposed SISP approach in this paper.
ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks

Object detection has attracted great attention in recent years. Many experts and scholars have proposed efficient solutions to address object detection problems and achieve perfect performance. For example, coordinate-based anchor-free (CBAF) module was proposed recently to predict the category and the adjustments to the box of the object by its feature part and its contextual part features, which are based on feature maps divided by spatial coordinates. However, these methods do not work very well for some particular situations (e.g., small object detection, scale variation, deformations, etc.), and the accuracy of object detection still needs to be improved. In this paper, to address these problems, we proposed ODEM-GAN based on CBAF, which utilizes generative adversarial networks to implement the detection of a deformed object. Specifically, ODEM-GAN first generates the object deformation features and then uses these features to enhance the learning ability of CBFA for improving the robustness of the detection. We also conducted extensive experiments to validate the effectiveness of ODEM-GAN in the simulation of a parachute opening process. The experimental results demonstrate that, with the assistance of ODEM-GAN, the AP score of CBAF for parachute detection is 88.4%, thereby the accuracy of detecting the deformed object by CBAF significantly increases.
Explorations into the mean nearest-neighbor distance in uniform and unimodal random distributions

The two-dimensional mean nearest neighbor distance and other distance distribution metrics, in uniform and unimodal normal distributions of random sequential addition hard disc computer-generated patterns, have been studied. First, more instances have been recorded in sufficient number to provide for more averaging and higher resolution data. Second, a facile, surprisingly accurate, method for estimating mean nearest neighbor distance in 2D uniform random distributions is presented. Third, the change in mean nearest neighbor distance as a function of the area fraction and standard deviation of the normal distribution of the disc diameter is discussed. It has been found that, while the changes are small (on the order of a few percent) both positive and negative deviations are observed. Consequences for higher dimension problems and multi-modal distributions are discussed.
Maximizing coverage, reducing time: a usability evaluation method for web-based library systems

The usability of a Web Based Library System (WBLS) is an important quality attribute that must be met in order for the intended users to be satisfied. These usability quality attributes are available in two forms: general to web systems and domain-specific. It must be evaluated through some evaluation method such as checklist. Many evaluation checklists have been proposed, although they mostly facilitate the evaluation of WBLS's general usability aspects, but they lack in covering domain-specific usability aspects of WBLS. There is a need to define domain specific usability aspects to maximize the usability for such systems. The purpose of this research is to develop and validates a usability evaluation checklist that supports the evaluation of general as well as specific usability aspects of WBLS. To accomplish this, a control experiment was conducted in the first phase with undergraduate students to develop a usability evaluation checklist that includes both general and specific usability aspects. Another controlled experiment will be used in the second phase to evaluate the effectiveness and efficiency of the proposed checklist with the existing checklist as "Academic Library Website Evaluation Checklist". The manual and statistical result shows that, the proposed usability evaluation checklist is effective with maximum coverage of general and specific usability aspects. Furthermore, the proposed checklist is equally efficient while identifying the usability errors in WBLS. The proposed checklist is beneficial for the academia as well as industry to evaluate the usability of WBLS to an optimal level.