Reviewerly/exHarmony huggingface dataset:
Data fusion of multi-spectral cameras on a low-power processing platform for self-sufficient outdoor operation

Multi-spectral camera set-ups may generally allow for creating surveillance applications even under unfavorable conditions, such as low-light environments or scenes involving vastly different lighting conditions. A high- resolution color camera, a high-dynamic-range camera and an infrared thermal camera were combined into a self-sufficient platform for continuous outdoor operation. The sheer amount of produced data poses a serious challenge, both in terms of available bandwidth and processing power, because self-sufficiency requires using relatively low-power components, and privacy, as high-resolution, multi-spectral image data are sensitive information. Thus, relevant objects of interest had to be efficiently extracted, tracked and georeferenced on the sensor platform. These data, from one or more sensorheads, are then sent via WLAN or mobile data link to a central control unit, possibly anonymized, e.g. prompting immediate action by a human operator in a disaster response use case, or stored for further offline analysis when used in the framework of "Smart City". Applying the classic stereo vision approach would require calibrating both intrinsic and extrinsic parameters of all cameras. The input data's multi-spectral nature complicates the correspondence problem for extrinsic parameter calibration and subsequent stereo matching, while intrinsic parameter calibration according to the pinhole camera model is made difficult due to the cameras having to be focused at infinity. However, by making certain reasonable assumptions about the observed scene in typical use cases, accepting a possible loss in localization accuracy, camera calibration could be limited to the bare minimum and less computational power was required at run-time.
Generative Adversary Networks in Signal Generation Problems

The object of the research is a multilayer perceptron which is being considered as a base neural network for the generative adversary neural network. The goal of the study is to make a neural network and make observations about how it can possibly work on a problem with separating: linear separable, semi separable and not separable subsets. The methodology of the study is a representation of the mathematical model of the multilayer perceptron on a python programming language. The results of the study are represented on the figures 2 - 5. The future application of the results are relevant for the further analysis in high particle physics for the track identification problem. Conclusions on the research are mostly based on the results with the tasks of separating objects, the accuracy of the dividing subsets is important for the future basis analysis on the subsets.
A novel all-reference image denoising quality evaluation method based on joint sparse representation

Numerous digital image algorithms have been proposed along with the emergence of new signal representation methods. However, denoising quality evaluation attracts less attention yet. In this paper, a novel all-reference image denoising quality evaluation method based on Joint Sparse Representation (JSR) is proposed. In this method, we model the clean image and the corresponding denoising image as a group images sensing the same scene but have differentiated contents. By joint sparse decomposition, these images are separated as a common part and two differentiated private parts; each of them is a linear combination of atoms in the given learning dictionary and stand for different aspects of image content. With this distinguishable information, we can distinguish image detail loss and noise remnants, and then evaluate the denoising quality from these two aspects. Compared with traditional statistical evaluation index, our method provides a more informative measure method in sparse feature space. Extensive experimental results demonstrate the superiority of our method.
Active Authentication on Mobile Devices

In recent years, we have witnessed a significant growth in the use of mobile devices such as smartphones and tablets. In this context, security and privacy in mobile devices becomes vital as the loss of a mobile device could compromise personal information of the user. To deal with this problem, Active Authentication (AA) Active authentication systems have been proposed in the literature where users are continuously monitored after the initial access to the mobile device. In this chapter, we provide a survey of recent face-basedFace-based Authentication AAActive authentication methods.
Performance in the spotlight

Performance in its various guises features prominently in research evaluations, and rightly so. Without adequate performance a system is not fit for purpose. That doesn't necessarily mean we should pursue performance at all costs though. In this talk we'll explore a variety of additional evaluation criteria, with a focus on those that are most important to practitioners, and ask whether or not considering them can open up interesting avenues of research.
Documentation Technical Debt

Context: Technical debt refers to software development pending issues that may cause great damages in the future if not resolved in the present. The longer the payment is delayed, the greater the interests. Documentation technical debt comprises problems concerning non-existent, inadequate or incomplete software projects' documentation. Motivation: Despite the importance of documentation, there is a lack of reports on how documentation technical debt occurs in software development organizations. Objective: We executed a qualitative study in a software development team of a large public health organization aiming to identify causes that led to documentation technical debts, along with possible consequences of their occurrence, and best practices to avoid documentation problems. Method: We interviewed four members of the development team and codified the collected data using thematic analysis technique. Results: We identified 35 causes, 20 consequences, and 15 best practices. Conclusions: We identified several problems that led to poor documentation despite the existence of well-defined processes. Also, we could observe how organizational culture influences the processes execution, and how team members' technical expertise can minimize the effects of poor documentation.
Techniques for Simultaneous Quadrature Image Detection for Imaging SAL and Vibrometry

Techniques for simultaneous detection and pre-processing of spatially resolved quadrature signals are explored for applications to synthetic aperture lidar and imaging vibrometry. Analytic descriptions, simulations and experimental results will be presented.
A Hybrid Model of Clustering and Classification to Enhance the Performance of a Classifier

Clustering and Classification are significant and widely used task in data mining. Their incorporation together is rare. When we integrate them together they can give more promising, accurate and robust results compare to - unaccompanied. The integration of these methods can be done by an ensemble method or hybrid method. This paper uses a hybrid model; K-means clustering method for the preprocessing of the data. Pre-learning by K-means clustering keeps similar cases in the same group. This improves the on-hand classifier’s performance. To demonstrate applicability of this new hybrid approach the experiments on PIMA diabetic datasets from UCI repository were conducted and the results are compared on several parameters. Clustering before classification provides an added description to the data and improves the effectiveness of the classification task. This model can be deployed with any classification algorithms to improve its performance.
A Solution of Synthesized Optimal Control Problem for Interaction of Robots by Evolutionary Computations

A paper considers a synthesized optimal control problem. To solve this problem, it is necessary to solve the problem of control system synthesis for stabilization of control object relatively a point in the state space, and then to solve the second problem of optimal control to find some stabilization points for movement from initial position to terminal one with the best value of the quality criterion. For the first problem a network operator method was used. It finds a mathematical expression of a feedback control system in the form of oriented calculating graph by evolutionary algorithm. For solution of the second problem a particle swarm optimization was applied. In experiment the control object is a pair of robots that move a cart joined by rigid hinged hitch from initial point to terminal one trying to avoid obstacles.
Effects of Statistical Learning Ability on the Second Language Processing of Multiword Sequences

A substantial body of research has demonstrated that both native and non-native speakers are sensitive to the statistics of multiword sequences (MWS). However, this research has predominantly focused on demonstrating that a given sample of participants shows evidence of learning the statistical properties of MWS. Recent theoretical approaches to language learning and processing emphasize the importance of moving away from group-level analyses towards analyses that account for individual differences (IDs). Here, through a within subject design embedded within an IDs framework, we investigate whether and to what extent individual variability in the online processing of MWS are associated with the statistical learning (SL) ability of an individual. Second language learners were administered a battery of SL tasks in the visual and auditory modalities, using verbal and non-verbal stimuli, with adjacent and non-adjacent contingencies along with two online processing tasks of MWS designed to assess sensitivity to the statistics of spoken and written language. We found a number of significant associations between the SL ability and the two processing tasks: Individuals who performed better on an auditory verbal adjacent SL task demonstrated greater sensitivity to the statistics of MWS in the spoken language, whereas individuals with better performance on a visual, non-verbal sequence learning task demonstrated greater sensitivity to the statistics of MWS in the written language. We discuss the implications of these findings for the study of IDs in the processing of MWS.
A Modelling of Stereo Matching Algorithm for Machine Vision Application

Stereo matching algorithm is a part of machine vision research area. The most challenging issue for stereo matching algorithm is to get an accurate corresponding point on the low texture region. Hence, this article proposes an algorithm utilizing the Sum of Absolute Differences (SAD), gradient matching and Bilateral Filter (BF) to increase the accuracy on this region. The combination of SAD with Red, Green and Blue (RGB) channels differences and gradient matching could improve the matching accuracy on the low texture region. Furthermore, the use of edge preserving filter such as BF that is capable to refine and remove the remaining noise on the final result. This filter is robust against high contrast and brightness. Based on the experimental analysis using standard benchmarking dataset from the Middlebury, the proposed work in this article achieves good accuracy on the low texture region. The comparison is also conducted with some established methods where the proposed framework performs much better.
A Meter Pointer Detection Method Based on Window Function Scanning

The pointer meter is widely used in industrial production, and its data mainly relies on manual acquisition with low efficiency. The automatic recognition algorithm of pointer meter reading based on meter image is a hot research topic. In this paper, aiming at the complex environment of open-air pointer meter and the low stability of existing meter reading recognition algorithm, a method of pointer detection based on window function scanning is proposed. Firstly, the meter image is taken from the scene by template matching method. Separate and enhance the meter image based on the improved homomorphic filtering method; then construct a window function and scan the binary image of meter by the circumferential scanning method to find the position of the pointer. Experiments show that the pointer detection method proposed in this paper has better adaptability and stability under complex illumination environment.
Application of Improved VIBE Algorithm in Robot Grabbing System Based on Visual Servo

Object detection is the essential part for arm robot grabbing system. Traditional VIBE (Visual Background Extractor) algorithm is used to capture moving objects in a visual servo-based robot grabbing system, but there are three major problems in the image: ghost, shadow and hole. Aiming at the three defects of VIBE algorithm, the corresponding improvement schemes are put forward: the frame difference method combined with VIBE algorithm is designed to solve the 'ghost' problem, the improved model based on normalized RGB space is used to eliminate the 'shadow'. Finally, image enclosure algorithm is used to deal with "holes". Through the comparative analysis of the experimental results, the effectiveness of the improved algorithm is verified, and the above three problems are well solved.
Generating Digital Libraries of M.Sc. and Ph.D. Theses

Postgraduate degrees are one of the most important propellers of all areas of science. M.Sc. and Ph.D. theses witness the important developments and provide a solid and global account of research projects. This paper describes a platform developed with the aim of generating digital libraries of theses and dissertations. Printed theses have to be scanned and then processed by the platform for marginal border removal, skew and orientation correction, image segmentation and enhancement, compression and pdf file generation. Both scanned and digitally generated theses are processed in the platform to extract relevant indexing information.
Intelligent System for Monitoring Vital Signs at Patient’s Home

The primary concern in present healthcare systems is increasingly shifting towards chronic diseases. This presents itself with the difficulty to monitor and follow-up a demanding number of patients. Technology development allows to significantly improving the quality of life. The aim of this project is to monitor a number of vital functions while making it accessible for the patients and the care takers. The vital functions of interest were: heart’s electrical activity, heart rate, oxygen saturation levels in peripheral blood, arterial blood pressure, temperature and galvanic skin response. In addition, there is the possibility of geo-tracking. The system uses a Healthy Pi board for data acquisition using the Linux operating system. The ECG module is based on an operational amplifier with multiple filtering levels so as to acquire a series of signals corresponding to a physiological graph in one lead which are sent to an Atmega microcontroller. They are forwarded towards the Raspberry Pi platform which compiles and shows it on the attached display. Signal calibration was done using a simulator for ECG testing for physiological and pathological heart conditions (ventricular fibrillation, heart stroke). The pulse-oximetry module determines the oxygen saturation levels in blood, but also helps monitor/compare the peripheral values (upper limbs) of pulse against heart rate according to the ECG. The advantages of this system consist of: efficient, quick data acquisition and storage, remote real time monitoring, friendly user interface, possibility of geo-tracking, accessibility for the patients and care takers.
Focus Estimation in Academic Environments Using Computer Vision

In this paper we propose a system capable of monitoring students’ focus through cameras and using Computer Vision algorithms. Experimental results show that our system is capable of identifying students and tracking their focus during a class. At the end of the class, the system outputs graphical feedback to teachers regarding the average level of students’ focus. Moreover, it can identify lecture periods in which students were less watchful and the corresponding topics that potentially need extra focus. In this paper we start by presenting the architecture of the system, followed by results obtained both during a small-group workshop and a classroom with a large number of attending students. The main goal of this work is to contribute to the transformation of the classroom as a sensing environment, providing information to both teachers and students about their engagement during the class.
An Empirical Analysis of Supply Chain BPM Model Based on Blockchain and IoT Integrated System

Cross-organizational supply chain as a generic form of business process management has become a hot topic of research and application. Blockchain and IoT technology provide effective technical support for this business process model. However, the business process model design and analysis based on blockchain and IoT technology has the following problems and challenges: (1) Lack of targeted BPM model. (2) The analysis issue of the BPM model across organizational supply chains are not clear enough. This paper proposes a cross-organizational luxury supply chain BPM(Business Process Management) Model based on Internet of Things(IoT in short) and Blockchain integrated system, also gives the simulation design and simulation analyzing results of the BPM model. The simulation results show that the designed BPM model has no deadlock and is a model that can be used directly.
Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.
Reversible Logic Synthesis Using Gröbner Base

The characteristics of reversibility have made the reversible circuits open up new possibilities in the development of the lower power design for integrated circuits and quantum computer research. This paper analyzes mechanism of reversible logic synthesis and presents a modeling to describe synthesis process. The modeling is analyzed and realized with C++. Change the problem of reversible circuit synthesis into the problem of solving decomposition tree by Gröbner basis. The modeling is verified on a set of reversible benchmarks. Results show that the modeling can significantly describe synthesis process of reversible circuit.
The Issue of Individual Adjustment of Active Manual Wheelchairs to People with Disabilities

The growing number of people with disabilities in Poland forces researchers to take a completely different approach to the issue of individual adjustment of active manual wheelchairs. A properly adjusted wheelchair, and especially an active wheelchair, is the basic equipment of a disabled person. The issues of scientific research focuses mainly on the study of upper extremities demands, then the construction and simulation of kinematic models. The results are the basis for determining guidelines to minimize injuries caused by excessive demands of upper extremities, and to develop optimal ranges of limb movements. The review and analysis of the literature have shown a number of important issues that have not found their place in the field of research on the broadly understood problem of the disability-wheelchair system. It is necessary to increase the amount of research in a given field. This conclusion became the premise for defining the purpose of future research, i.e. developing a methodology for individual adjustment of active manual wheelchairs to the possibilities, limitations and expectations of potential users based on the principles of user-centered design. The implementation of the objective provides for the use of modern measurement techniques, including electromyography (EMG) or wireless analysis of 3D motion kinematics using inertial sensors.
Towards Data Anonymization in Data Mining via Meta-heuristic Approaches

In this paper, a meta-heuristics model proposed to protect the confidentiality of data through anonymization. The aim is to minimize information loss as well as the maximization of privacy protection using Genetic algorithms and fuzzy sets. As a case study, Kohonen Maps put in practice through Self Organizing Map (SOM) applied to test the validity of the proposed model. SOM suffers from some privacy gaps and also demands a computationally, highly complex task. The experimental results show an improvement of protection of sensitive data without compromising cluster quality and optimality.
Implementation of a Training Parser Using Explicit Abstract Syntax Tree

The paper presents new implementation of a parser as a part of a training compiler, used for education in compilers and language processors. The proposed parser implementation has two significant differences compared to other existing implementations of training parsers. First, it contains building of explicit abstract syntax tree, presenting the structure of the input program. Second, the parser is implemented as a completely separate module from the next compiler phases, which makes it easy to be understood, completed successfully and tested by students. The main goal of this implementation is to increase the success of students in their study of the most complex compilation phases -- parsing, semantic analysis and code generation.
Intelligent Flight Control of Combat Aircraft Based on Autoencoder

The intelligent flight control of the aircraft is the key process in the air combat maneuver process. The traditional flight control method has many steps, long time and low precision, which have great drawbacks in the air combat process. In this paper, based on the background of deep learning, a flight control model based on autoencoder is proposed. Using the characteristics of autoencoder dimension reduction and feature extraction, the low-dimensional attitude parameters of high-dimensional aircraft can be extracted from high-dimensional flight attitude parameters. The eigenvalues are then automatically obtained through the neural network to change the attitude control of the aircraft. In this paper, the basic framework and training methods of the model are designed, and the influence of various parameters of the autoencoder network on the performance of the model is deeply studied. The experimental results show that the proposed model has better prediction accuracy and convergence performance than the traditional BP neural network, and achieves the purpose of intelligently and quickly obtaining flight attitude control to intelligently control aircraft flight.