Reviewerly/exHarmony huggingface dataset:
An Improved Genetic Algorithm Based on k-means

The traditional genetic algorithm has the disadvantage of slow convergence speed and prematurity. In order to optimize the algorithm from the perspective of spatial analysis, a multi-granular genetic algorithm proposes a spatial partitioning method based on a completely random tree to improve the genetic algorithm. However, the accurate analysis of space by completely random trees is time-consuming. Therefore, an improved genetic algorithm based on k-mean is proposed in this paper. The individuals obtained by the genetic algorithm are clustered through k-means. Then, according to the clustering results, new individuals are generated in the subspace containing a small number of individuals and in the subspace to which the current optimal solution belongs, thus improving the performance of the genetic algorithm.
Sentiment Analysis of Local College in the Philippines using Facebook Posts towards Good Governance: A Framework Proposal

Good governance means that the institutions are able to meet the needs of their stakeholders. It is participatory and inclusive through considering the opinions and sentiments of the stakeholders for decision-making. To analyze the massive amount of opinions and sentiments, sentiment analysis is necessary. Thus, this study intends to devise and propose a framework for sentiment analysis that will be used by the Local Colleges in the Philippines towards the attainment and maintenance of good governance. The proposed framework presented the phases for sentiment analysis to develop an application/software: data acquisition, data pre-processing, data classification, validation and evaluation, and integration.
Characterization of Auxiliary Problems for Automated Generation in Error-Based Simulation

Error-based Simulation (EBS) is a learning support framework that visualizes learner's errors. Learning with EBS encourages trial and error, unlike learning by instruction, because learners are required to actively challenge problems. However, when problems are complex and difficult, learners may get stuck. In such cases, it is effective to present auxiliary problems that can support learning of the original problem. Learning with auxiliary problems requires learners to actively challenge problems compared with more passive learning by instruction. However, the creation of many auxiliary problems places a burden on teachers, especially because there are no rules to creating these problems. Therefore, we considered that a system that automatically generates auxiliary problems according to rules would provide the required auxiliary problems. Such a system would lift the burden on teachers who would otherwise need to create the auxiliary problems. In this paper, we discuss characterization and generation rules for the purpose of automated generation of auxiliary problems in EBS. We propose three types of operations that generate auxiliary problems: replace, balanced delete, and simple delete. Using these operations to generate auxiliary problems in a trial, we generated fourteen auxiliary problems.
A Bricklayer-Tech Report

Technology is playing an increasingly prominent role in all human endeavors, including education. Tech enables the realization of educational environments that are adaptive, interactive, and immersive. Such environments are well-suited for appropriately engaging student populations comprised of digital natives. Bricklayer is an educational ecosystem whose focus is on the development of visuospatial, mathematical, and computational abilities foundational to computer science. This article gives an updated report on the core (Ed)Tech elements comprising the Bricklayer educational ecosystem.
Efficient Dataflow Modeling of Peripheral Encoding in the Human Visual System

Computer graphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers an opportunity to efficiently allocate computation and compression to appropriate areas of the viewer’s visual field, of particular importance with the rise of high-resolution and wide field-of-view display devices. However, while variations in acuity and contrast sensitivity across the field of view have been well-studied and modeled, a more consequential variation concerns peripheral vision’s degradation in the face of clutter, known as crowding. Understanding of peripheral crowding has greatly advanced in recent years, in terms of both phenomenology and modeling. Accurately leveraging this knowledge is critical for many applications, as peripheral vision covers a majority of pixels in the image. We advance computational models for peripheral vision aimed toward their eventual use in computer graphics. In particular, researchers have recently developed high-performing models of peripheral crowding, known as “pooling” models, which predict a wide range of phenomena but are computationally inefficient. We reformulate the problem as a dataflow computation, which enables faster processing and operating on larger images. Further, we account for the explicit encoding of “end stopped” features in the image, which was missing from previous methods. We evaluate our model in the context of perception of textures in the periphery, including a novel texture dataset and updated textural descriptors. Our improved computational framework may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to computer graphics.
Android Printing: Towards On-Demand Android Development Employing Multi-Material 3-D Printer

In this paper, we propose the concept of Android Printing, which is printing a full android, including skin and mechanical components in a single run using a multi-material 3D printer. Printing an android all at once both reduces assembly time and enables intricate designs with a high degrees of freedom. To prove this concept, we tested by actual printing an android. First, we printed the skin with multiple annular ridges to test skin deformation. By pulling the skin, we show that the state of deformation of the skin can be adjusted depending on the ridge structure. This result is essential in designing humanlike skin deformations. After that, we designed and fabricated a 3-D printed android head with 31 degrees of freedom. The skin and linkage mechanism were printed together before connecting them to a unit combining several electric motors. To confirm our concept’s feasibility, we created several motions with the android based on human facial movement data. In the future, android printing might enable people to use an android as their own avatar.
Investigating What You Share: Privacy Perceptions of Behavioural Biometrics

This study examines people’s perceptions of biometrics, in the context of the inherent privacy concerns surrounding behavioural biometrics as an alternative to conventional password systems. We present the knowledge and opinions of behavioural biometrics collected in this study. The main theme which is present throughout the research is that users have privacy concerns around behavioural biometrics, but that these concerns do not necessarily translate into privacy-conscious actions.
A Declarative Gradualizer with Language Transformations

lang-n-change is a domain-specific language for expressing language transformation algorithms. We have previously used lang-n-change to express simple transformations, which begs the question on whether lang-n-change can be applied to sophisticated aspects of programming languages.
RETRACTED: Methodology for using web technologies to develop the intellectual abilities of future mathematics teachers

• The current stage of development of Kazakh education is characterised by intensification of large-scale transformations. • The Internet has made significant changes in the organisation of public space. • In the process of teaching a discipline, the Internet can play the role of a source of information, a means of control, a field of communication, a way of organising the learning process itself. The article actualises the problems of professional-pedagogical education, in particular, the issues of using web technologies in the process of psychological and pedagogical training of future mathematics teachers. The authors outlined a range of relevant issues, the solution of which is possible under the condition of implementing elements of a mixed learning system using cloud technologies. The article presents the results of an online survey of students of the specialisation “Secondary education” to determine the degree of using web applications in the process of psychological and pedagogical training and the degree of their interest in the use of cloud technologies in their future professional activities. The scientific novelty is determined by the fact that the ways of implementing cloud services into the educational process of high school are highlighted. The study identifies the system of principles that contribute to the effective organisation of psychological and pedagogical training of future mathematics teachers using web technologies. The practical significance of the study is determined by the fact that the main lines of improvement of the psychological and pedagogical training of future mathematics teachers are characterised, provided that web technologies are used.
Adaptive Code Completion with Meta-learning

Since human-written programs have useful local regularities, the ability to adapt to unseen, local context is an important challenge that successful models of source code must overcome. However, the current source code models mostly learn a common code pattern from large scale open-source codebases, which cannot make use of the localness nor satisfy developers’ personal preferences. Consequently, fast learning and adapting to unseen code patterns from limited developers’ code can provide new insights into source code completion. In this work, we train a base code model that is best able to learn semantic and structural information from context to improve predictions of unseen local tokens and propose an adaptive code model leveraging meta-learning techniques. We demonstrate highly improved performance in experiments on a large scale Java GitHub corpus compared with baselines.
Twister2 Cross‐platform resource scheduler for big data

Abstract Twister2 is an open‐source big data hosting environment designed to process both batch and streaming data at scale. Twister2 runs jobs in both high‐performance computing (HPC) and big data clusters. It provides a cross‐platform resource scheduler to run jobs in diverse environments. Twister2 is designed with a layered architecture to support various clusters and big data problems. In this paper, we present the cross‐platform resource scheduler of Twister2. We identify required services and explain implementation details. We present job startup delays for single jobs and multiple concurrent jobs in Kubernetes and OpenMPI clusters. We compare job startup delays for Twister2 and Spark at a Kubernetes cluster. In addition, we compare the performance of terasort algorithm on Kubernetes and bare metal clusters at AWS cloud.
Detecting Malicious URL using Neural Network

The increasing usage of internet, the network is facing different dangerous attacks. Phishing attacks represent a threat for internet user because attackers are tending to design web pages very easily, attacks are done by inserting executables or SQL injections to steal user’s sensitive information. In this research, we demonstrated how digital signature can improve URL detection. Also, we showed an effective steps organization can do to enhance their security over user’s usage and data. Therefore, it’s very important to develop new approaches for URL detection. Our paper presents the Malicious URL cyber-attacks by introducing a method for Malicious detection of URLs using Neural Network to classify the URLs according to its type, either normal or malicious. The use of neural network to detect malicious attacks is used by using feed-forward network and apply CICANDMAL2017 to it.
Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization

Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors. Therefore, we propose a blind super-resolution method for infrared images of power equipment to improve the imaging quality of low-cost infrared sensors. If the blur kernel estimation and non-blind super-resolution are performed at the same time, it is easy to produce sub-optimal results, so we chose to divide the blind super-resolution into two parts. First, we propose a blur kernel estimation method based on compressed sensing theory, which accurately estimates the blur kernel through low-resolution images. After estimating the blur kernel, we propose an adaptive regularization non-blind super-resolution method to achieve the high-quality reconstruction of high-resolution infrared images. According to the final experimental demonstration, the blind super-resolution method we proposed can effectively reconstruct low-resolution infrared images of power equipment. The reconstructed image has richer details and better visual effects, which can provide better conditions for the infrared diagnosis of the power system.
Local Adaptive LMS Filtration of Multidimensional Images

In this paper a new method for local adaptive three-dimensional filtration of multidimensional halftone images is presented. The adaptation is based on the local information from the neighbourhood pixels of processed 3D image using generalized 3D LMS filter. An analysis of the quality of the processed multidimensional images is made on the basis of the calculated PSNR, SNR, MSE and the subjective observation. The given experimental results from the simulation in MATLAB environment for operating systems Windows of the developed filtration algorithms, based on a set of real test images are presented. By using the adaptive 3D LMS algorithm the average signal to noise ratio improvement is about 3 dB.
A Secure Key Agreement Framework for Cloud Computing Using ECC

Cloud computing has the ability to share data via public channel. Digital identity is needed for clients to use facilities of cloud in cloud computing. Presently, public key and asymmetric cryptography are used for most of cloud-based system to provide safe communication. Due to its characteristics, ID-based cryptography is very important in cloud domain. Our framework has three phases like as initialization, user registration and authentication, and key agreement phase. Also, we have compared our work with other works in the same domain and also found that our work is more secure than others. In our framework, user and server communicates to each other and establish key agreement.
From Hieroglyphs to Emoji, to IKON: The Search of the (Perfect?) Visual Language

Writing evolved from the expression of the meanings to the expression of the sounds. In modern times new projects like Emoji came back to semantic writings. We show some main characteristics of these ancient and modern systems, analyzing aspects that can be improved, and describing ikon, a new iconic language conceived as a new step in the evolution of visual languages. We discuss a case study about hair and gender, how these theoretical results were applied in some ikon icons, and a test to evaluate the comprehensibility of these icons through a tool called iconometer. Finally, we discuss wikon, a crowdsourced extension of ikon.
Benchmarking of metaheuristic algorithms to design flotation circuits to full scale

Computing intelligence is an important branch of artificial intelligence, and its techniques have been applied successfully in different research fields, such as mineral processing. These techniques include metaheuristic algorithms (MAs), which have been used to address difficult optimization problems. In this line, MAs have been proposed as an alternative to exact algorithms for designing flotation circuits, since the latter exhibit an extremely slow convergence even for moderately small instances. However, the MAs implemented in the literature to design flotation circuits are generally not validated or compared to other MAs to guarantee the quality of the solutions obtained. In this work, we present a methodology for developing, validating, and comparing MAs to design flotation circuits to full scale. The methodology is described considering the Tabu search algorithm, Differential Evolution Algorithm, and Particle Swarm Optimization, and their hybridizations. To improve the performance of MAs, we consider aspects such as competitive coevolution and one-dimensional and two-dimensional chaotic maps. The results reveal that the methodology provides algorithms that exhibit a good relationship between the execution time and the quality of the results.
A Bayesian nonparametric model for classification of longitudinal profiles

Across several medical fields, developing an approach for disease classification is an important challenge. The usual procedure is to fit a model for the longitudinal response in the healthy population, a different model for the longitudinal response in the diseased population, and then apply Bayes' theorem to obtain disease probabilities given the responses. Unfortunately, when substantial heterogeneity exists within each population, this type of Bayes classification may perform poorly. In this article, we develop a new approach by fitting a Bayesian nonparametric model for the joint outcome of disease status and longitudinal response, and then we perform classification through the clustering induced by the Dirichlet process. This approach is highly flexible and allows for multiple subpopulations of healthy, diseased, and possibly mixed membership. In addition, we introduce an Markov chain Monte Carlo sampling scheme that facilitates the assessment of the inference and prediction capabilities of our model. Finally, we demonstrate the method by predicting pregnancy outcomes using longitudinal profiles on the human chorionic gonadotropin beta subunit hormone levels in a sample of Chilean women being treated with assisted reproductive therapy.
Rotated Fusion Network (RFN) Algorithm for Aerial Vehicle Detection

The improvement of intelligent transportation systems requires high-precision vehicle detection under full-weather condition. From the view of unmanned aerial vehicle (UAV), vehicle information in the global scope can be monitored effectively. At present, most of vehicle datasets are monomodal or unregistered multimodal, failing to be trained to detect vehicles under the harsh environment. In this paper, a visible and infrared aerial vehicle dataset, named VIA, has been proposed, which is highly temporal registered and covers a variety of scenarios. In addition, a visible and infrared rotation fusion detection framework based on YOLOv3, named RFN, has been proposed. The framework uses a dual-path feature extraction network to extract features separately and uses rotated bounding boxes to locate vehicles, solving the problem of confusion and redundancy of vehicle information caused by horizontal bounding box detection. Besides, the effectiveness of Oriented Response Network (ORN) and Asymmetric Convolution Blocks (ACB) modules for feature enhancement is explored. The promising results have been obtained in the VIA dataset by this method.
METHOD AND EXPERIENCE OF IMPLEMENTATION OF QUANTITATIVE ASSESSMENT OF DISSERTATIONS QUALITY OF DOCTORS OF PHILOSOPHY ON THE EXAMPLE OF THE SPECIALTY GEODESY AND LAND MANAGEMENT

Quantitative assessment of the quality of scientific activity occupies a significant place in the implementation of innovative developments. As part of the study, the authors proposed a method of expert quantitative evaluation of the quality of dissertations of doctors of philosophy on the example of the specialty Geodesy and Land Management, which proposes to determine the properties of the dissertation, develop an evaluation scale, quantify each property and calculate an integrated indicator, based on which the conclusion of the dissertation. Modern mobile computing tools allow you to create a mobile application for gadgets of experts, members of specialized scientific councils, and in real time (in the process of conducting a professional seminar and defending a dissertation) to quantify the quality of the dissertation. Gadgets of two reviewers, two opponents, the chairman of the specialized scientific council, and the scientific secretary are elements of the generalized scheme of collecting and integrating expert assessments on the quality of the dissertation performed for the degree of Doctor of Philosophy. The gadget of the scientific secretary is a server and is endowed with functions of transfer of information on an estimation on system of support of educational processes of department on which special page the corresponding estimations of quality of dissertations can be placed. The proposed letter assessing the quality of the dissertation consists of three parts: the first is information about the expert, the second is a system of sign-numerical scales that allow in the usual form for research and teaching staff to assess individual features of the dissertation, the third may contain specific proposals of the expert. A generalized scheme for collecting and integrating expert assessments in real time using mobile technologies has also been developed. An example of approbation of the proposed method is given. The boundaries of productive and unproductive intervals of the sign-numerical scale are considered. The proposed method meets modern requirements for digitalization. It allows you to determine in real time a quantitative indicator of the quality of research conducted by the applicant, which in turn ranks dissertations and makes them attractive to investors, which will develop promising areas of research and accelerate scientific and technological progress. The given examples of realization of a method based on theories close to human psychology – the theory of usefulness, the psychological theory of decision-making, and also wide experience of estimation of dissertation will allow to make a certain contribution to experimental researches of the organization of dissertations for the degree of doctor of philosophy.
A novel quantization method combined with knowledge distillation for deep neural networks

Abstract The massive parameters and intensive computations in neural networks always limit the deployment on embedded devices with poor storage and computing power. To solve this problem, a novel quantization algorithm combined with Knowledge Distillation (KD) is proposed to reduce the model size and speed up the inference of deep models. The proposed method consists of two phases, KD-Training and Quantization-Retraining. KD-Training attempts to train a compact student model with pre-quantized weights by the proposed pre-quantized constraint loss. In Quantization-Retraining, the pre-quantized weights are quantized to 2 n and the first and last layers of the network are retained to make up for the accuracy loss caused by quantization. Experiments on the CIFAR-10 dataset show that the proposed method can obtain a low-precision (2-5bit) quantized student model with a compact structure, and the test accuracy even exceeds its full-precision(32bit) reference as the improvement of the generalization ability. It can get higher performance compared with that obtained by other quantization methods. Also, since the quantized weights are constrained in { ± 2 n }, it is suitable for the acceleration of the network calculation in hardware.