Reviewerly/exHarmony huggingface dataset:
Development of Algorithms and Software for Automatic Pedestrian Recognition for Intelligent Situation Control Systems

Abstract The article is devoted to the development of algorithms and software that automatically recognize road users - pedestrians, build their trajectory according to the analysis of the video stream. The architecture of the system for pedestrian recognition and inter-frame tracking is proposed. The developed algorithms can be used to estimate road situation with participation of pedestrians for the traffic monitor systems.
Remote Memory Swapping for Virtual Machines in Commercial Infrastructure-as-a-Service

Memory swapping is an essential feature offered by operating systems (OS) to prevent applications from crashing due to the system running out of main memory. Traditional swapping works by using a part of the storage disk as backup memory. Unfortunately, storage disk speeds are orders of magnitude slower than main memory. This implies that as useful as swapping is, it causes expected performance degradation of the application. A more recent swapping mechanism in clusters is remote swapping, which uses one host's main memory as swap space over the network for another host. For faster networks, remote swapping could outperform disk swapping. However, for users leasing Virtual Machines (VMs) in existing commercial Infrastructure-as-a-Service (IaaS) providers, choosing a swapping mechanism for their VMs is non-trivial. They are subjected to additional constraints and considerations such as lack of access to the underlying hypervisor, OS, and hardware. On the other hand, network speeds within clouds have evolved to be faster than storage device speeds, making remote swap appealing. This paper explores implementing remote swapping for leased VMs within IaaS providers. A setup is proposed that implements remote swapping within a cluster of VMs, abiding by the constraints of IaaS. The setup does not need any modification of the hypervisor, or any special hardware, making it suitable for users currently in commercial IaaS clouds. Through extensive experimental evaluations on Amazon Web Services' ec2 instances and Google Cloud's Compute Engine, the paper demonstrates the performance benefits of using remote memory versus the standard storage disks as swap space.
Semantic-Based System for Exercise Programming and Dietary Advice

Growing health awareness results in interest in healthy eating and “fitness”. While information about exercising and dieting is readily accessible, it is difficult, for an inexperienced person, to find what is right for her/him in terms of both diet and exercise. The aim of this work is to introduce a system, based on semantic technologies, which addresses user goals with joint exercise programs and meal suggestions.
Data Augmentation for Face Recognition System Implemented in Multiple Transform Domains

A face recognition system which represents each of the augmented facial images as a superposition of the dominant components in two transform domains is proposed. Each face in the spatial domain is divided into horizontal, vertical halves and diagonal format. These partitions are concatenated to generate four more faces per subject in any database used. All images are first preprocessed then compressed using two different domains. The Discrete Wavelet Transform (DWT) and the Discrete Cosine Transform (DCT). Accordingly, each face will have two feature matrices. A voting scheme is used to define ground truth identity. The performance of the proposed system is evaluated using k-fold cross validation of ORL, Yale and FERET databases. Sample results are presented. The proposed technique achieves higher recognition rates while retaining 74% savings in storage recently reported.
Efficient Genetic-Based Detection Algorithm for Large-Scale MIMO Systems

In this paper, a genetic-based signal detection algorithm is proposed for large-scale multi-input multi-output (MIMO) systems. First of all, the random Gaussian noise is utilized to serve for the population initialization in Genetic algorithm (GA), where candidates in the population can be easily generated. Then, the Euclidean distance $\Vert \mathrm{y}-\mathrm{H}\mathrm{x}\Vert$ is applied as the fitness function for the candidate selection. After that, two-point recombination as well as random mutation is introduced for the following evolution, thus completing an iteration of the proposed algorithm. Meanwhile, a flexible trade-off is established between detection performance and complexity, which can be adjusted by the population size and the iteration numbers. Furthermore, a pre-detection stage that relies on decoding radius is also proposed for the efficient detection without any performance loss. Finally, simulation results confirm that considerable performance gain can be achieved in a low complexity cost.
2D Object Transformation by Mapping of Hand Gesture Using Leap Motion Controller

The maneuver of the traditional classroom is mostly teachers monologue supported by elementary components like presentation with chalk and talk etc. But the classroom content may not be equally understood by every individual student due to the variation in cognitive divulge. Sometimes simple class content getting complicated due to less interactive concept illustration. In this work, we try to explore the potential of the Immersive User Interface(IUI) powered by Leap Motion Controller to make an interactive experience by the student to realize classified content to elementary teaching system. The 2D transformation is mainly emphasized on this project. Animated interaction designs created with a computational model for application to identify the significant enhancement in learning outcome. The work try to explore the immersive feeling and natural interaction system with the problem so that the transformation of 2D object is clear to the people's perceptions. Participants can easily preforms and understand Translation, Rotation and Scaling operations. The consequence of this work has clearly indicated the enhancement in learning outcome with effective interaction designs using leap motion. This can also be extended for combating learning disability, entertainment activity design and special purpose training and simulation application.
Design of Japanese Characters using an Interactive Genetic Algorithm

In this paper, we show a system using an interactive genetic algorithm which is effective in the design of Japanese characters. We set the type, thickness, curvature and color of the lines that make up the character as parameters. First, prepare two images for which each parameter is determined by a random number, and let the research participants choose the preferable one. This tournament process was repeated ten times, and the generation range of the random number was manipulated so that parameter values possessed by the favorite image group are likely to be generated, and parameter values possessed by an image group that is not preferred are difficult to be reproduced. We refer to this as narrowing the parameters range (NPR). After that, we used an interactive genetic algorithm that generates ten characters per generation, selects parents from favorite letters, and inherits its parameters to descendants, and use the method of narrowing parameter ranges. Through these processes, by the tenth generation, we succeeded in making 90% of the created Japanese characters preferred by research participants.
Visualizing 3D Objects Using Augmented Reality Application to Enhance Students Retention in Social Science Subject

An excellent retention is one of the keys for student success in the learning process because they can keep in mind about material that has been learned. Traditional house (TH) in Java island is one subject matter that should be taught to 5thgrade of elementary students (SD) in Indonesia. It is a part of social science, which in the learning process requires excellent retention. One way to improve retention is to make material subjects clearer, such as making an object to be more concrete than an abstract model by visualization. This article discusses augmented reality (AR) application that visualizes virtual 3D objects of TH in real-world to enhance students' retention. The research method started from the literature review and problem identification, developing and validating the application, testing, and data analyzing. The testing was conducted in SD Negeri 4 Klambu, Grobogan, Indonesia, with 28 students involved in the research. Data collected by questionnaire and described by descriptive analysis. After using the AR application in the experiment, the students stated in the questionnaire that response about ease is 83.93%, 84.82% for attractiveness, and 90.18% for motivation. Among the three assessment aspects, motivation has the highest score of response. It means that the students perceive more motivated with this application for learning media. With higher motivation, the retention will be more easily to be enhanced. So, this application could be used for an alternative in teaching and learning method to improve student's retention.
Application of the neural modelling method for the Solar radiation analysis in a number of cities in the Russian Federation for the solution of environmental problems

The paper gives the assessment of using the methods of data mining including the artificial neural networks (ANN) in researching solar radiation for various regions of the Russian Federation, in particular, such cities as: Astrakhan (latitude of 46.4) and Sochi (at the latitude 43.6) -located in the south, in Vladivostok (latitude 43.1), Yuzhno-Sakhalinsk (latitude of 47) - in the south-east of the country, PetropavlovskKamchatsky (latitude of 53.3) — in the east, Petrozavodsk (latitude of 61) -in the south-west, and in the Russian capital - Moscow (latitude of 55.7). A neural network model has been developed, the most significant 15 input variables have been determined, as well as hidden layers numberand the number of neurons. The most optimum functions were chosen, including the Bayesian Regularization as the training functions, the function of gradient descent with regard for moments as the Learning Function, the hyperbolic tangent activation function was taken as an activation function and the Mean Square Error was taken as an execution function. The feedforward backprop function was ap lied. The equations of regression and the correlation parameters were obtained for the calculation of solar radiation.The presented work can be useful for developers of different types of electric and solar heating systems to determine the requiered parameters for solar radiation with regard to the large bulk of meteorological and geographic data for improving the environmental situation including in civil engineering and municipal economy.
Context-aware multi-agent planning in intelligent environments

In ambient intelligence, systems should interact intelligently with users and assist them in integrated and almost transparent way. Hence, planning and context awareness is crucial for building such systems. To cope with the high dynamics and the openness of ambient intelligence environments, repre sentation formalism allowing a run-time analysis and monitoring of plans is required. In this paper, we focus on context awareness and planning ability on ambient environments. First, we outline a formal modeling approach for context-aware planning based upon a hierarchical colored Petri nets formalism. The approach enables the specification of plans through many levels of abstractions. Second, we address the problem of assigning tasks to groups of agents; we describe the use of resulted model in conjunction with contract net protocol to form the collaborative agent networks. Again we show how the formal foundation of this framework allows plan verification and execution monitoring. Finally, we highlight the main aspects of the approach through an assisted healthcare system case study.
An Online Network Traffic Classification Method Based on Deep Learning

Traffic classification plays an important role in network traffic analysis. In this paper, an online network traffic classification method based on deep learning was proposed. By pre-training CNN and fine-tuning the fully connected layer, the complexity of model update is greatly reduced, thus enabling online learning. To deal with the stability-plasticity dilemma, we introduce a proficiency mechanism. The proposed method uses the raw binary data of packet header as input of the model instead of statistical features, which avoid complex feature selection process. And it can identify traffic by a small number of packets, which meets the needs of early stage identification. Besides, our model has the ability of online learning, and can adapt dynamically to the network environment. Experimental results show good performance of the proposed method in both accuracy and speed on traffic classification task.
An Integrated Framework for Pronominal Anaphora Resolution in Malayalam

Anaphora resolution is one of the old problems in Natural Language Processing.It is the process of identifying the antecedent of an anaphoric expression in a natural language text.Most of the NLP applications such as text summarization, question answering, information extraction, machine translation etc. require the successful resolution of anaphors.In this paper, we propose a methodology for the resolution of pronominal anaphors present in Malayalam text document.The proposed methodology is a hybrid architecture employing machine learning and rule-based techniques.In our study, we have used a deep level tagger developed using a machine learning based algorithm.The deep level tagger provides detailed information regarding the number and gender of nouns in a text document.The morphological features of the language are effectively utilized for the computational analysis of Malayalam text.Despite using less amount of linguistic features, our system provides better results which can be utilized for higher level NLP tasks such as question answering, text summarization, machine translation, etc.
Committees: History and Applications in Machine Learning

The article outlines a brief history and applications of the committee theory. The use of committees in the problems of recognition and optimization is discussed. The application of the committee structures, ambiguous interpretation of non-formalized and contradictory data are given. The ways of rational regard on environmental factors in the context of a lack of resources are considered. The question of the numerical finding of committee structures is discussed, and these results are directly related to the theory of voting. The class of non-classical logics also contains MK-logic (Mazurov, Khachay). This section of non-classical logic includes the works by N. A. Vasiliev, L. Wittgenstein, J. Lukashevich, and Latin American mathematicians having a wrong term in their titles parainconsistent logic. One of the important results achieved by M. Yu. Khachay: For arbitrary positive integers q and k, $$k<q,$$ the minimum estimate of the subsystem power is given that is resolvable by a committee of k-elements for the inconsistent system having a committee of q-elements. Further the history of this field will be mentioned.
Generative Policies for Coalition Systems - A Symbolic Learning Framework

Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.
Analysis of Telemonitoring Multi Vital Data for Alert Detection on Telecare System

Telecare is an effective method for medical doctors to monitor conditions of elderly patients suffering from chronic diseases. It is crucial to detect acute diseases or the deterioration of chronic disease condition in such patients in order to prevent progression to fatal conditions. Therefore, we analyzed clinical telemonitoring data that were collected in a telecare trial. By using a principal component analysis of time sequences of multi vital data, it was observed that the possibility of the alert detection being detected more easily by using first or second principal component scores was higher than that only using vital data.
RRT* Algorithm Under Holonomic Conditions for UAVs

Traditional path planning algorithms such as artificial potential field method, fuzzy rule method, genetic algorithm, simulated annealing algorithm, ant colony optimization algorithm, etc. are not suitable for solving path planning problems of robot with multi-degree of freedom in complex environment. Their computational complexity is exponential with robot freedom. This paper presents an RRT* algorithm under holonomic conditions facing anytime demand for UAVs. In the absence of differential constraints, the UAV can be degraded into mass points for consideration, and the path planning algorithm based on RRT (rapidly exploring random tree) can effectively solve the path planning problems in the condition of high dimensional space with complex constraints.
An Experimental Study for Optimal Homotopy Property of Motion Planning Algorithms

Path Planning Problem is very hard for its huge search space. In this paper, we quote the definition of homotopy in mathematics to describe the right "direction" of the optimal path, which is a necessary condition for the optimal paths under the holonomic condition. Based on the concept of homotopy, optimal homotopy property is proposed for paths. Numerical experiments involving RRT*, sPRM, RRT, PSO, A* and Jump Point Search (JPS) are carried out. Although A* and JPS are gird space optimal, these do not satisfy optimal Homotopy property.
Gaze Estimation Based on Neural Network

In this study, we selected some state-of-the-art or classic neural network models for recently popular research problem gaze estimation. Experimental analysis based on eyegaze data set compares different models and suggests that neural networks can achieve excellent performance in gaze estimation problem while original Capsule Network provides the best performance and 18-layers ResNet is second-best in all models.
Learning to Translate Between Real World and Simulated 3D Sensors While Transferring Task Models

Learning-based vision tasks are usually specialized on the sensor technology for which data has been labeled. The knowledge of a learned model is simply useless when it comes to data which differs from the data on which the model has been initially trained or if the model should be applied to a totally different imaging or sensor source. New labeled data has to be acquired on which a new model can be trained. Depending on the sensor, this can even get more complicated when the sensor data becomes more abstract and hard to be interpreted and labeled by humans. To enable reuse of models trained for a specific task across different sensors minimizes the data acquisition effort. Therefore, this work focuses on learning sensor models and translating between them, thus aiming for sensor interoperability. We show that even for the complex task of human pose estimation from 3D depth data recorded with different sensors, i.e. a simulated and a Kinect 2 TM depth sensor, human pose estimation can greatly improve by translating between sensor models without modifying the original task model. This process especially benefits sensors and applications for which labels and models are difficult if at all possible to retrieve from raw sensor data.
Evaluating Student Learning Effect Based on Process Mining

As education is taking an increasingly significant role in society today, efficient and precise evaluation of student learning effect is calling for more attention. With recent advances of information technology, learning effect can now be evaluated via mining student's learning process. This paper proposes an interactive student learning effect evaluation framework which focuses on in-process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, and feature engineering. In order to enable effective student learning evaluation and deliver practical value, we have developed a comprehensive online modeling platform to collect operation data of modelers and to support the corresponding analysis. We have carried out a case study, in which we applied our approach to a real dataset, consisting of student online modeling behavior data collected from 24 students majoring in computer science. The results of our analysis show that our approach can effectively and practically mine student modeling patterns and interpret their behaviors, contributing to assessment of their learning effect.
A Coalgebraic Semantics Framework for Quantum Systems

As a quantum counterpart of labeled transition system (LTS), quantum labeled transition system (QLTS) is a powerful formalism for modeling quantum programs or protocols, and gives a categorical understanding for quantum computation. With the help of quantum branching monad, QLTS provides a framework extending some ideas in non-deterministic or probabilistic systems to quantum systems. In this paper, we propose the notion of reactive quantum system (RQS), a variant of QLTS, and develop a coalgebraic semantics for both QLTS and RQS by an endofunctor on the category of convex sets, which has a final coalgebra. Such a coalgebraic semantics provides a unifying abstract interpretation for both QLTS and RQS. The notions of bisimulation and simulation can be employed to compare the behavior of different types of quantum systems and judge whether a coalgebra can be behaviorally simulated by another.
Static Coverage Path Planning for UAVs with Conical Field of View When Monitoring Rectangular Ground Areas

Finding a suitable UAV path to cover a given ground area is of paramount importance when accomplishing monitoring or mapping missions. In order to obtain an appropriate UAV route to inspect a rectangular ground area using a sensor with a conical field of view, this paper analysis three classic trajectories, namely scan, rectangular spiral and Hilbert paths. Using carefully chosen metrics we conclude that the scan path is the right solution to this static coverage planning problem.
From Desktop to Mobile View: A Simplified Approach to Mobile Website Development

Desktop version websites are designed for viewing on desktops or laptops screens and are not optimized for mobile devices. With increased adoption of mobile technologies, businesses and governments, which already own a desktop version website but not a mobile version website are often required to invest heavily in a full web development life cycle to obtain a website optimized for mobile devices. Given that website development is complex, time consuming and costly, we propose a simplified approach in the form of a tool, which makes use of open source technologies to automatically convert existing desktop websites into mobile versions without the need to go through a complete website development life cycle. The proposed tool presented in this paper particularly targets, but is not limited to, Governments from developing countries with strict budget constraints.