Reviewerly/exHarmony huggingface dataset:
Dynamics of Recognition of Properties in Diagnostics

The issues of processing semantic information for diagnostic studies are considered, in which, with the advent of personalized medicine, the task of ensuring efficiency and accuracy has come to the fore. The solution of such problems is associated with the need to recognize the properties of the samples, which is done by experts/crowdsourcers using the means of semantic modeling. In this regard, the analysis is subject to communication (1) level of knowledge with (2) forms of information processing and with (3) levels of abstraction. For this, a dynamic semantic model is developed and applied that can take into account such parameterization.
Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO 2 , Ag, Ti, and TiO 2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm 2 , and is accurate enough to monitor film growth in real time.
Why the «Frame« Standards of School Education Are Replaced by the «Detailed» Standards in Russia: Problem-Risk Analysis

The article is devoted to the issue of standardization of the content of Russian school education. The article reveals new approaches to the regulation of the content of school education, proposed in the draft federal state educational standards for 2018-2019; identifi es the main problems and risks of the regulatory framework of the Russian school education; examples from the practice of standardization of education in England, Finland, and South Korea are given.
Risks of Distance Education with Electronic Support Identified During Training

The positive and negative aspects of distance education with the use of electronic means obtained during training courses and retraining of teachers of General and preschool education are considered.
The cross-project defect prediction based on PSO and Feature Dependent Naive Bayes

Abstract The software defect prediction often assumes that the software under test has rich historical information, which is a harsh condition in reality. Cross-project defect prediction can predict projects with rare historical information by using historically informative projects. The differences in software architecture and code environment pose challenges to cross-project software defect prediction. Based on the characteristics of software defect prediction sets, this paper introduces a two-stage algorithm based on particle swarm optimization algorithm and Feature Dependent Naive Bayesian classifier.
Computer simulation of the synchronous generator steady-state and dynamic regime

The stability of a power system is determined by the dynamics of its interconnected synchronous machines. Hence, the study and analysis of the steady-state and the dynamic behaviors of the power system depend highly on the synchronous machine modeling. Dynamic analysis of synchronous machines is of great importance to show how all control and protecting elements must react for any abnormalities on machine terminals. In this paper is presented an analysis and a full simulation to predict the electromechanical dynamics of a synchronous generator, with and without considering the saturation effects.
Educational app Proposal to Support Pregnant Women

Pregnancy is an important stage in a woman's life, yet many women still do not get proper prenatal care. Failure to follow prenatal care can cause problems for the woman and the child. An important step in prenatal follow-up is the educational training for the woman, which allows her to overcome fears and get to know herself better. This work proposes a mobile application to support the woman who performs prenatal care in a public health unit and with this receive a better educational training on gestation. In addition, the application should allow a better mapping of pregnancy as well as concentrate data to better analyze the use of public funds and identify needs of public agencies acting based on identified demands.
A new mutation operator with the ability to adjust exploration and exploitation for DE algorithm

This paper proposes a new mutation operator Current --- to --- better-AC with an angle control rule for DE algorithm. This new mutation operator has the potential to automatically adjust the exploration and exploitation based on the angle control rule. Under the control of this rule, the individuals pair for generating mutation vector is tendentiously selected. In Current --- to --- better-AC, only high-ranking individuals are permitted to use the angle control rule, while low-ranking individuals are encouraged to move to a random high-ranking individual. This method is embedded into JADE as a new algorithm AC-JADE, and its performance is tested by COCO benchmarks. Experimental results show that AC-JADE performs better than JADE.
Classification and regression trees in predicting listening item difficulty

This chapter introduces classification and regression trees (CART) to investigate the factors that determine listening test item difficulty. CART has been used extensively in education fields and machine learning but has received relatively less attention in language assessment. We discuss the major components of a reliable CART study such as articulation of theoretical frameworks, using cross-validation, estimation of fit statistics, and accuracy of classification. The chapter employs the construction-integration (CI) model of comprehension to measure item difficulty in a large pool of listening test items. The data comprised seven Michigan English Test (MET) listening tests comprising 321 items answered by 5039 international language learners. CART modeling generated 41 IF-THEN rules, which revealed nonlinear relationships between item difficulty and 12 independent variables (IVs) measured by Coh-Metrix. CART enabled us to show that the relationship between item difficulty and the 12 IVs is not linear, and different sets of rules would apply to predict the difficulty of different groups of listening test items.
Automatic Knowledge Acquisition in the Form of Fuzzy Rules From Cases for Solving Classification Problem

The authors consider an approach to automatic knowledge acquisition through machine learning on the basis of integrating the two basic reasoning methods – case-based reasoning and rule-based reasoning. Case-based reasoning allows using high-performance database technology for storing and accumulating cases, while rule-based reasoning is the most developed technology for creating declarative knowledge base on the basis of strong logical approach. This allows realizing the transformation of the spiral of knowledge, leading to continuous improvement of the knowledge quality in the management system. In the chapter, they propose one method of obtaining rules from cases based on fuzzy logic. Here the method is considered for solving classification problem, but it also can be applied for solving regression problem. The research shows acceptable accuracy of cases classification even for small training samples. At the same time, smoother (quadratic) membership functions show on average classification accuracy.
Fuzzy Control of a Low Cost Mobile Robot Based on Vertical Lines in An Edge Detected Image

Abstract Edge detection is a computationally efficient and economical image processing technique as this method retains only the edges of surrounding objects for guiding the navigation and localization of a mobile robot. However, present edge detection based methods are heavily dependent on fusion with odometrical data which gradually accumulates localization errors. Moreover, they lack descriptive capabilities. In this paper, a fuzzy view descriptor which fused the information of the detected adjacent primary colours of the vertical lines in the mobile robot’s view with the fuzzified digital compass readings was used to ascertain the actual view. This fusion provided a sense of direction in addition to recognizing the frontal view. The fuzzified distance of the base pixel coordinates of the vertical line of interest determined the attraction of the mobile robot towards it or the repulsion from it. A fuzzy control rule base guided the mobile robot towards the vertical line of interest by constantly reducing its deviation in the edge image. The mobile robot managed to execute its consecutive movements in an intuitive manner without a precise mathematical model. The deviations of the vertical lines were kept small and the detection of the correct views was constantly reliable.
Multiple human tracking with dual cost graphs

Visual tracking gives a trajectory of a person’s movement in a video. It is an important element of human’s behavior analysis. However, the performance of the most visual tracking methods does not satisfy precision and speed for use in the real world. High accuracy and low computation complexity are demanded but these two have a relation of trade-off. Therefore, we aim to propose a tracking method that balances accuracy and computational complexity. In this paper, we proposed a method named Dual Cost Graph (DCG)-Tracker using the graphs referred to as a clique graph and a flow network. We evaluated DCG-Tracker with PNNL parking lot 1 dataset<sup>1, 2</sup> and showed that it balances between accuracy and speed.
Research and Application of Target Tracking Algorithm in Ship Safety

Abstract Target tracking is often used in tasks such as video surveillance. Its purpose is to obtain the target of interest in the sequence image accurately, robustly and in real time, and to establish the connection between the moving objects in each image. In recent years, with the continuous development of the global shipping economy, ships have gradually developed into large-scale and high-speed, and how to ensure the safety of ships sailing in these sports has become an important issue. Based on the principle of particle filter and mean shift algorithm, this paper takes the moving ship as the research object and uses the polynomial fitting method to analyse the performance of the two algorithms. A large number of experimental results show that the mean shift algorithm has higher accuracy, and the particle filter method has better real-time performance. And as the number of particles increases, the accuracy of particle filtering will gradually increase.
Hate Speech in Online Health News: News about HIV/AIDS

Hate speech is based on exclusion and devaluation of certain groups in society, which represents a systemic hierarchy. This systemic hierarchy is constructed on the basis of discourse that represents the dominant ideology and media plays an important role in the dissemination of this discourse. In this sense, hate speech is a discursive practice that serves the benefits of the dominant ideology, which is endorsed through the discourse of media. Especially, news media plays an important role in the dissemination of hate speech because news is considered as the representation of real life. This study aims to analyze online health news in terms of hate speech against HIV/AIDS and understand how HIV positive people perceive this hate speech. For examining online news, critical discourse analysis method is adopted; and in-depth interviews are carried out with ten HIV positive people. Findings of the analysis of in-depth interviews are in accordance with the findings of critical discourse analysis, as HIV positive people claim that online news promotes and popularizes prejudices, stigmatization and discrimination against them and that online news’s content involves misrepresentations, misinformation, and incorrect use of terminology. Even though hate speech encompasses all types of expressions that promote, popularize, defend or justify discriminatory, intimidating, disapproving and prejudicial attitudes, HIV positive people do not describe this as hate speech, which points to the fact that hate speech is internalized and justified by its victims.
Part Attentioned Fine-Grained Categorization Using Context Sensitive Flow

Abstract It is difficult to recognize fine-grained objects (e.g., dogs or cars) because of the challenges of difficult region localization and fine-grained feature learning. Current approaches neglect the fact that local context feature and global feature are mutually correlated and thus it is possible to use the related information from each other. In this paper, we propose a novel multiple scales attention fusing convolutional neural network which can learn region-based feature and discriminative object attention and at multiple scales in a mutually improved way. The learning is composed of several scales which consist of a classification sub-network and an attention fusing module. A L refine loss was proposed to refine the second sub-network category performance. We do some overall experiments and show that our proposed method achieves the best performance in two fine-grained tasks, with relative mean accuracy gains of 2.2%, on CUB Birds, and Stanford Cars.
Facial-image based Age Estimation Using Imbalanced Datasets

Abstract Facial image based human age estimation is of great application significance. The public-available facial image datasets used for age estimation suffer greatly from the uneven distribution of images of different age groups, which may lead to the low estimation accuracy of the under-sampled age categories and limit the usage of the age estimation in certain applications. We propose a three-stage probability adjustment based CNN algorithm to solve the imbalanced distribution problem of the dataset. In particular, we construct an ENIN neural network structure by applying the Network in Network (NIN) structure to the traditional convolution neural network (CNN) and use the probability vector adjustment to improve the classification accuracy of the under-sampled age categories. Then, we filter out the images with high possibility of being misclassified after the probability vector adjustment and reset their categories by comparing cosine similarity and retraining the ensembled ENIN classifier. We also introduce a population-age-distribution based accuracy metric Accuracy-P to estimate the performance of the age estimation algorithm in real-world applications. Our experimental results confirm that our algorithm can effectively improve the overall estimation accuracy by significantly improving the accuracy of the under-sampled age groups while maintaining satisfactory accuracy for the other age groups.
User Stress Measurement of Remote Operation Supporting System with Hand Gesture Transmission Function

In this paper, we report the experimental results that try to measure user stress when the user communicates with a remote operation supporting system that applies a wearable camera and a wearable display. The remote operation supporting system refers to an audiovisual communication system that enables communication between a worker in real fields and a director in a remote site with sharing eyesight of the worker with wearable devices and mobile communication network. Various systems have been proposed such as early research by Kuzuoka [1] and a more recent project by Kasahara [2]. Some of these systems are commercialized, however, have not been widely used yet. Our developing system has a feature that enables experts (matured workers) send their hand movements and shapes to guide workers (non-matured workers) by using a gesture sensor in addition to conventional audiovisual communication systems. As the evaluation criteria of the effect of introducing worker support systems, the task completion time reduced by the system has been generally used. Reducing work completion time is an important measurement; however, that is not necessarily beneficial for the system users. Only applying this explicit measuring index could hinder finding further development of the system and popularizing of the system. In this report, we focus on stress as the evaluation measurement of the system users’ situation. We expect that the feature of our system that enables the expert’s natural hands movements as a directional method would reduce users’ stress. To verify the effect, we conducted experiments that measure users (experts and operators) physiological index data, i.e. the heart rate of users with wearable devices, while they are operating tasks using the proposing remote operating support system. Through that experiment, we report that physiological effect of hand gestures transmission function instead of task completion time. Moreover, we discuss the required functions or a development process to the remote operation supporting system to be used widely.
Closed Loop Control of a Compliant Quadruped with Spiking Neural Networks

Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing framework. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
Towards Formal Models of Blameworthiness

As we move towards an era in which autonomous systems are ubiquitous, being able to reason formally about moral responsibility for outcomes will become more and more critical. My research has focused on formalizing notions of blameworthiness and responsibility. I summarize here some work by myself and others towards this end and also discuss interesting directions for future work.
Modulation Recognition of MPSK Signals Based on Generalized Quartic Spectrum

Automatic modulation recognition (AMR) has become an interesting problem in communication systems with various civil and military applications. The modulation recognition method based on quartic spectrum will degrade seriously in the Alpha-stable distribution noise. To solve this problem, ageneralized transform is proposed in this paper, based on which, a new modulation recognition method of MPSK is investigated. First, generalized quartic spectrums of signals are analyzed. Thenthe method extracts spectrum lines of two times carrier frequency and four times carrier frequency in generalized quartic spectrum as the feature parameters. Finally the method realizes the recognition of signals through judging whether spectrum lines are impact spectral lines or not. Experimental results show that the novel method has good performance in both Alpha-stable distribution noise and Gaussian noise, and it is less affected by characteristic exponent of noise ranging from 1 to 2.
A Photo Booth That Finds Your Sports Player Lookalike

This paper presents a digital signage system that encourages fan engagement at sports venues. The system detects users' faces and displays the team player who looks most similar to the user in a composite image that they can capture as a souvenir. We describe the design choices of this photo booth and evaluate the similarity search accuracy in several user studies. The system has been operating continuously at a baseball stadium with consistently high user engagement. So far over 12,000 people have taken their souvenir photo.
SISTEM INFORMASI MONITORING PEMBAYARAN PAJAK (SIMPEJAK) PADA KANTOR PELAYANAN PAJAK PRATAMA SUKABUMI

As a government agency tasked with carrying out counseling, services, and supervision of taxpayers, one of the functions of the Primary Tax Service Office (KPP) is collecting, searching and processing data, observing potential taxation, presenting tax information, and collecting data on objects and subjects.In carrying out these tasks and functions, it is necessary to improve the system in terms of recapitulation of taxpayer data not report non-payment (WP TLTB).The Account Representative (AR) must first access the integrated tax website and search for taxpayer data one by one at the time of giving an appeal to the WP TLTB.This study uses the Software Development Life Cycle (SDLC) method of the Prototype model to shorten the time to develop information systems.With the existence of this information system AR does not need to struggle to recapitulate the taxpayer data one by one which will be given an appeal or visit.The WP TLTB form, WP data, AR data, and payment receipt data can be seen effectively and efficiently on the SIMPEJAK website, this information system is also designed to automatically recapitulate WP TLTB data (Taxpayers Not Reporting Not Pay) as well as printing an appeal letter automatically.
Global salient object detection based on multiple visual features

Abstract Salient object detection has become an important tool in the fields of computer vision and image processing. In this paper, we propose a novel global salient object detection model based on multiple visual features. Firstly, following the mechanism of parallel processing of visual information, we obtain three saliency maps based on colour, intensity difference, and spatial distribution. Secondly, introducing the concept of spatial gaze point of an image, we calculate three Gaussian weighted maps based on three saliency maps to constrain their background noise and get three weighted saliency maps. Finally, the final saliency map is produced by fusing three weighted saliency maps. We compare our method with 10 state-of-the-art saliency methods on a public dataset, and the results show that our method outperforms the other 10 methods.
The cost of morphological complexity

The evolutionary cost of morphological complexity in biological populations remains an open question. This study investigates the impact of imposing a cost on morphological complexity given co-adapting behavior-morphology couplings in simulated robots. Specifically we investigate the environmental and evolutionary conditions for which morphological complexity can be evolved without sacrificing behavioral efficacy. This study evaluates the relationship between task difficulty (environment complexity) and evolved morphological complexity. We use multi-objective neuro-evolution to evolve robot controller-morphology couplings in task environments of increasing difficulty where the objectives are to minimize the cost of (morphological) complexity and to maximize behavior quality (task performance). Results indicate that imposing a cost of complexity induces the evolution of simpler morphologies with negligible differences in behavior (task performance) across increasingly complex environments (increasing task difficulty).
Software Evolution and Profiling in a Competitive Environment

In this paper, we present our experience with software evolution and profiling at Master level of a technical university. We defined a set of tasks to find and solve an issue of an existing open source project on the web, extended by software profiling and improvement. This improvement has to be proved by application of a suitable software metric as well. The main aim of such a task is to emphasise the importance of program comprehension in software evolution. Our teaching approach on software evolution addresses comprehension in three ways: read, measure and use. Reading code is also important when preparing for evolution, measuring gives a second view on the system, but trying it out is the only way to complete comprehension. We also focus on the difference in students' behaviour that shows a kind of sobriety. We think this modest approach comes from the lack of experience and identify the need for change.