Reviewerly/exHarmony huggingface dataset:
Histogram Matching Schemes for Image Thresholding

This paper proposes several novel schemes for image thresholding.The idea is simply to compare the original image histogram to that of the thresholded image.Element by element comparison (sum of absolute difference between the two histograms) is found to be of better performance than a single feature (area or size) comparison.The optimum threshold is the one producing the best comparison.Cumulative histogram is introduced as a generalization to the area under the curve and found to be of better performance.In addition, a new performance measure is suggested based on percentage of correct assignments in both foreground and background.Comparative results with Otsu shows the effectiveness of the proposed schemes.
CURRENT REQUIREMENTS FOR THE DEVELOPMENT AND IMPLEMENTATION OF ELECTRONIC TEXTBOOKS IN THE EDUCATIONAL PROCESS OF PRIMARY SCHOOL

One of the decisive factors in the modernization of the education system is the creation of a new generation of learning tools that combine the achievements of modern pedagogical science with the powerful didactic capabilities of information technology. Therefore, the problem of implementation of electronic educational resources into the educational process of the elementary school, in particular, electronic textbooks becomes of special urgency nowadays. The article summarizes the results of the analysis of modern scientific literature regarding the requirements for the development of electronic textbooks and the peculiarities of their introduction to the system of general secondary education. The essence of the concept «electronic textbook» has been found out, its features, functions, components, as well as advantages and risks of application in modern educational institutions have been determined. The psychological and pedagogical, methodological and technical requirements for electronic textbooks have been determined and the possibility of their use in the elementary school has been substantiated. The analysis of peculiarities of structural organization, didactic functions and advantages of using electronic textbooks allowed showing their effectiveness in raising the level of interest of students, positive impact on the personal development of children, their qualitative training in the process of learning. The author paid attention to the importance of systematic use of electronic textbooks in the educational process, conducted an analysis of electronic textbooks for pupils of the New Ukrainian School. The article emphasizes the necessity of creating electronic textbooks for a modern school, specifies the concrete steps of the state educational policy in this direction, in particular, the legal regulation of the specified activity, as well as conducting an all-Ukrainian experiment «An Electronic Textbook for General Secondary Education».
Metrics for Analyzing Variability and Its Implementation in Software Product Lines

This summary refers to the paper Metrics for analyzing variability and its implementation in software product lines: A systematic literature review1. It was online first in 2018 and was finally published 2019 in the Information and Software Technology (IST) journal.
Role of Technology in Safer Evacuation during Emergency Response

Due to unpredictable nature of damage and the internal structure of the building it becomes mandatory to think how to act promptly in case of causalities. The big question is how to protect people from damages and send them to safer area. The technology allows monitoring and controlling such events dynamically. An evacuation model based on latest technology allow people to get escape from such situations in real time thereby saving the lives as well as reducing the economic losses. This paper focused on the role of technology in evacuation strategies during an emergency.
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action

The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency controllers. For example, the range of motion and the angles of the motors of a robot can be limited to physical boundaries. Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes. In this paper, we propose a simple modification of a state-of-the-art deep RL algorithm (DQN), enabling learning from forbidden actions. To do so, the standard Q-learning update is enhanced with an extra safety loss inspired by structured classification. We empirically show that it reduces the number of hit constraints during the learning phase and accelerates convergence to near-optimal policies compared to using standard DQN. Experiments are done on a Visual Grid World Environment and Text-World domain.
Numerical Discrimination of the Generalisation Model from Learnt Weights in Neural Networks

This research demonstrates a method of discriminating the numerical relationships of neural network layer inputs to the layer outputs established from the learnt weights and biases of a neural network's generalisation model. It is demonstrated with a mathematical form of a neural network rather than an image, speech or textual translation application as this provides clarity in the understanding gained from the generalisation model. It is also reliant on the input format but that format is not unlike an image pixel input format and as such the research is applicable to other applications too. The research results have shown that weight and biases can be used to discriminate the mathematical relationships between inputs and make discriminations of what mathematical operators are used between them in the learnt generalisation model. This may be a step towards gaining definitions and understanding for intractable problems that a Neural Network has generalised in a solution. For validating them, or as a mechanism for creating a model used as an alternative to traditional approaches, but derived from a neural network approach as a development tool for solving those problems. The demonstrated method was optimised using learning rate and the number of nodes and in this example achieves a low loss at 7.6e-6, a low Mean Absolute Error at 1e-3 with a high accuracy score of 1.0. But during the experiments a sensitivity to the number of epochs and the use of the random shuffle was discovered, and a comparison with an alternative shuffle using a non-random reordering demonstrated a lower but comparable performance, and is a subject for further research but demonstrated in this "decomposition" class architecture.
Open Set Medical Diagnosis

Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such models will invariably face previously unseen conditions. We frame machine-learned diagnosis as an open-set learning problem, and study how state-of-the-art approaches compare. Further, we extend our study to a setting where training data is distributed across several healthcare sites that do not allow data pooling, and experiment with different strategies of building open-set diagnostic ensembles. Across both settings, we observe consistent gains from explicitly modeling unseen conditions, but find the optimal training strategy to vary across settings.
TCAIOSC: Trans-Compiler Based Android to iOS Converter

Cross-platform development is the practice of developing software products or services for multiple platforms or software environments. The idea of cross-platform development is that a software application or product should work well in more than one specific digital habitat. This capability is typically pursued in order to sell software for more than one proprietary operating system. In general, cross-platform development can make a program less efficient. However, in many cases, the makers of software figured out that the limitations of cross-platform development are worth dealing with in order to offer an application or product to a wider set of users. This paper discusses the development of a code converter from Android to iOS and vice versa through trans-compiler approach, the challenges encountered in the process and the outcomes resulted by this attempt.
Machine Learning based Physical Human-Robot Interaction for Walking Support of Frail People

In the near future robots will permeate our daily life empowering human beings in several activities of daily living. Particular, service robots could actively support indoor mobility tasks thus to enhance the independent living of citizens. They should be able to provide tailored services to citizens to achieve higher physical human-robot interaction. Too often service robots were designed without taking into account end-users functional requirements, which can change with age and geriatric syndromes. In this paper, we present a robot smart control based on machine learning strategies and adaptable to different handgrip strengths. The smart control was implemented on ASTRO robot conceived to be a companion and to support indoor mobility, among other activities. Particularly, three smart controller strategies were implemented and tested with end users from technical and user point of view. The results show promising results that underline the proposed approach was suitable for the proposed application.
A Secure and Scalable Data Source for Emergency Medical Care using Blockchain Technology

A relationship exists between the emergency patient death rate and factors such as the failure to access a patient's critical data and the time it takes to arrive at hospitals.The ability for Paramedics to access a patient's complete picture of emergency-relevant medical data is critical and can significantly reduce the annual mortality rate.Today, the problem exists with a continuous recording system of the patient data between healthcare providers.In this paper, we have introduced a blockchain-based solution to record patient emergency relevant medical data as patient walks through from one medical facility to another, creating a continuous footprint of patient as a secure and scalable data source.Therefore, ambulance crews can access and use it to provide high quality pre-hospital care.
DSM-based Analysis for the Recognition of Modeling Errors in Supervisory Controller Design

The design of supervisory controllers for cyber-physical systems is steadily becoming harder as increasingly more functionality needs to be automated, the systems become larger, and safe operation becomes more important. Model-based systems engineering incorporating formal methods such as supervisory control synthesis can be used to synthesize these supervisory controllers based on models of the uncontrolled system components and models of the control requirements. Although synthesis is an automatic procedure, creating these models is still a manual activity prone to modeling errors. In this paper, we propose to use several DSM-supported analysis techniques to identify potential modeling errors. Analyzing the dependencies between uncontrolled system component models and requirement models with both a domain mapping matrix and a dependency structure matrix reveals potential modeling errors. We present several examples of models from literature to show the potential effectiveness of the DSM-supported analysis of the uncontrolled system and the associated control requirements.
ForeSight - Platform Approach for Enabling AI-based Services for Smart Living

In future, smart home and smart living applications will enrich daily life. These applications are aware of their context, use artificial intelligence (AI) and are therefore able to recognize common use cases reliably and adapt these use cases individually with the current user in mind. This paper describes a concept for such an AI-based platform. The presented platform approach considers different stakeholders, e.g. the housing industry, service providers and tenants.
Blockchain and AI: The technological revolution’s impact on corporate governance relationships

Disorientation and instability characterize the globalized context in which the relations of corporate governance are carried out today. Suffocated by the huge numerical and qualitative dimension of data to be analysed, companies need organizational systems and tools that allow them to interface with this reality. Through the creation of decentralized and more efficient systems able to process information at a higher speed than any human, Blockchain and artificial intelligence are the answer to these renewal requests. This paper aims to analyse the possible implications of the adoption of these technologies within the corporate governance relations, supporting how they can constitute effective and necessary support to the current organizational systems and, at the same time, how they are not yet able to replace them completely. Such a hypothesis would lead to the constitution of information oligarchies, would expose the shareholders to AI’s errors or manipulations by removing any management responsibility and, ultimately, to the creation of autonomous markets driven exclusively by economic purposes of the IT protocols that constitute them
A novel self-adaptive grid-partitioning noise optimization algorithm based on differential privacy

As the development of the big data and Internet, the data sharing of users that contains lots of useful information are needed more frequently. In particular, with the widespread of smart devices, a great deal of location-based data information has been generated. To ensure that service providers can supply a completely optimal quality of service, users must provide exact location information. However, in that case, privacy disclosure accident is endless. As a result, people are paying attention to how to protect private data with location information. Of all the solutions of this problem, the differential privacy theory is based on strict mathematics and provides precise definition and quantitative assessed methods for privacy protection, it is widely used in location-based application. In this paper, we propose a self-adaptive grid-partitioning algorithm based on differential privacy for noise enhancement, providing more rigorous protection for location information. The algorithm first partitions into a uniform grid for spatial two dimensions data and adds Laplace noise with uniform scale parameter in each grid, then select the grid set to be optimized and recursively adaptively add noise to reduce the relative error of each grid, and make a second level of partition for each optimized grid in the end. Firstly, this algorithm can adaptively add noise according to the calculated count values in the grid. On the other hand, the query error is reduced, as a result, the accuracy of partition count query (the query accuracy of the differential private two-dimensional publication data) can be improved. And it is proved that the adaptive algorithm proposed in this paper has a significant increase in data availability through experiments.
PENERAPAN METODE FUZZY MAMDANI UNTUK MEMPREDIKSI JUMLAH PRODUKSI KARET (STUDI KASUS: DATA PERSEDIAAN DAN PERMINTAAN PRODUKSI KARET PADA PTP NUSANTARA XIV (PERSERO) KEBUN AWAYA, TELUK ELPAPUTIH, MALUKU-INDONESIA)

Good corporate management will determine the development of a company. In addition, the necessary production planning is also required to achieve maximum profit. This study uses data from PTP Nusantara XIV (Persero) Awaya Garden, Teluk Elpaputih, Maluku Province Indonesia, which is engaged in the production of raw rubber. This research uses Fuzzy Mamdani method to predict the amount of rubber production based on the demand data, inventory and production of rubber per day in April 2016. From the research result obtained the exact amount of rubber production with the percentage of truth value equal to 87,83% and the resultant error is 12,17%.Keywords : Demand, Fuzzy Logic Mamdani Method, Inventory, Production.
A Text Mining Analysis of US-Chinese Leaders on Trade Policy

Using the methodologies of text mining, this paper examines the implications of US and Chinese policies on bilateral trade. Official speeches by political leaders of the U.S. and China on the issues of trade were collected and analytically examined for US-China gaps in major foreign policies, such as bilateral trade and the Belt and Road Initiative. In this paper, a term frequency-inverse document frequency word cloud, a network similarities index, machine learning-processed latent Dirichlet allocation (LDA), and structural equivalence are applied to examine the meanings of the speeches. The main arguments in this paper are as follows. First, the document similarity between the speeches of Chinese and US leaders appears to be completely different. Also, while the documents from Chinese leaders are considerably similar, the documents from US leaders differ by far. Secondly, LDA topic analysis indicates that China concentrates more on international and collaborative relationships, while the U.S. has more focus on domestic and economic interests. Third, from a word hierarchy analysis, the basic words used by American and Chinese leaders are also completely different. Agriculture, farmers, automobiles, and negotiations are the basic words for American leaders, but for Chinese leaders, the basic words are planning, markets, and education.
Speaker diarization during noisy clinical diagnoses of autism

Autism Spectrum Disorder (ASD) is characterized by difficulties in social communication, social interactions and repetitive behaviors. Some of these difficulties are apparent in the speech characteristics of ASD children who are verbal. Developing algorithms that can extract and quantify speech features that are unique to ASD children is, therefore, extremely valuable for assessing the initial state of each child and their development over time. An important component of such algorithms is speaker diarization in the noisy clinical environments where ASD children are diagnosed. Here we present a Gaussian Mixture Model (GMM) approach for speaker diarization that was applied to 34 recordings from clinical assessments using the Autism Diagnostic Observation Schedule (ADOS). We used mel-frequency cepstral coefficients (MFCC) and pitch based features to classify segments containing speech of the child, therapist, parent, movement noises (chair, toys, etc.) and simultaneous speech. We achieved an accuracy of 89% in identifying segments with children's speech and an accuracy of 74.5% in identifying children's and therapists' speech segments. These accuracy rates are similar to the diarization accuracy rates reported by previous similar studies, thereby demonstrating a promising route for the automated assessment of speech in children with ASD.
A Survey of Blind Forensics Techniques for JPEG Image Tampering

Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security.Firstly, the main achievements of domestic and foreign scholars in the blind forensic technology of JPEG image tampering were briefly described.Then, according to the different methods of tampering and detection, the current detection was divided into two types: double JPEG compression detection and block effect inconsistency detection.This paper summarized the existing methods of JPEG image blind forensics detection, and analyzed the two methods.Finally, the existing problems and future research trends were analyzed and prospected to provide further theoretical support for the research of JPEG image blind forensics technology.
Endoscopic Image Clustering with Temporal Ordering Information Based on Dynamic Programming

In this paper, we propose a clustering method with temporal ordering information for endoscopic image sequences. It is difficult to collect a sufficient amount of endoscopic image datasets to train machine learning techniques by manual labeling. The clustering of endoscopic images leads to group-based labeling, which is useful for reducing the cost of dataset construction. Therefore, in this paper, we propose a clustering method where the property of endoscopic image sequences is fully utilized. For the proposed method, a deep neural network was used to extract features from endoscopic images, and clustering with temporal ordering information was solved by dynamic programming. In the experiments, we clustered the esophagogastroduodenoscopy images. From the results, we confirmed that the performance was improved by using the sequential property.
Fuzzy Optimization in Discrete-Continuous Control Systems for Multiple-Operation Technological Processes

A numerical method for designing operating software for digital controllers of hybrid discrete-continuous control systems is considered. The method is based on implementing control algorithms on digital controller in the form of rule-based systems and uses a fuzzy optimization procedure to define controller operation rules. The continuous part of hybrid control systems consists of multiple-operation technological chains, which are nonlinear, multidimensional and multi-loop plants, and the discrete part consists of digital controllers. The developed digital controllers ensure satisfaction of the most general requirements for controlled processes, including fuzzy ones.
A Radar Main Lobe Pulse Correlation Sorting Method

The sorting of radar signals, as an important part of electronic reconnaissance, is the basis and core of radar reconnaissance. But in practical application, there are a large number of identical targets and only their radar main lobe pulses are intercepted, which brings great difficulty to accurate sorting. In this paper, a method of radar main lobe pulse correlation sorting is proposed. The main lobe pulse is extracted by pulse repetition frequency and the signal is sorted by correlation clustering. The result of signal sorting shows that in the case of main lobe detection with high pulse loss rate, the algorithm can achieve better sorting of emitter signals with identical Pulse Repetition Interval (PRI).
Design and implementation of a test setup for electric mobility scooter for the disabled

In this study, a test setup was designed for three- and/or four-wheeled electric mobility scooter models used by disabled or old people. This system is composed of a test platform that enables measurement of vehicular velocity, a data acquisition card, and an interface prepared in the C# program. Using the data acquisition card that was designed, seven quantities, namely, battery and motor currents, battery and motor terminal voltages, wheel speed in revolution per minute, and ambient and motor temperatures, were measured instantaneously during the test procedure and transferred to a computer via a USB device. Using these data that were transferred, motor speed in revolution per minute, torque generated by the motor, motor shaft power, motor and driver efficiency, instantaneous velocity of the vehicle, and total distance covered information obtained from the moment the vehicle began to be used were computed in real time throughout the experiment in the interface prepared in the C#, and their graphs were drawn and recorded. Thus, the faults in the battery, motor, or driver of electric mobility scooters became easily detectable in tests conducted under various conditions such as different ambient temperatures, different user weights, and roads with different slopes.