Reviewerly/exHarmony huggingface dataset:
Air-Conditioning Control with Spatial Recognition Using Stereo Infrared Array Sensors

Depending on the location of the air conditioner and the shape of a room, air-conditioning control may be inefficient resulting in temperature imbalance. When attempting to solve this problem, it is vital to understand the spatial structure of a room (including its size and shape) and the location of air conditioners and then automatically control the airflow and direction according to the structure. However, such a method for recognizing spatial structures has not yet been established. In this paper, we propose a spatial recognition method using stereo infrared array sensors (SIRA sensors) installed in an air conditioner. Our system detects objects in the obtained thermal images and estimates their distances using triangulation. In addition, the room's size and shape are estimated based on the assumption that the room size lies within the detection range. The distances to the front and left/right walls were estimated in one-meter-wide classes. The estimation accuracy was compared using two types of IRA sensors: thermopile array sensors and thermal diode infrared sensors. Regarding the distance estimation of persons from the captured stereo thermal images, the average error rate was 12.5% for both types. The distance to each wall was estimated within a 1 m error range for the thermal diode infrared sensor. Moreover, applications of the proposed spatial recognition to air-conditioning control were demonstrated. Specifically, we propose a method to control the airflow direction and volume by considering the room’s geometry. An L-shaped room was modeled and simulated. From the results, the spatial recognition reduced the unevenness in temperature by adjusting the airflow based on the room shape. These results indicate that the proposed method can be practically used for spatial recognition to efficiently improve user comfort by controlling air-conditioning based on the spatial structure and eliminating uneven temperature.
The On-line Filtering Technology of INS/CNS Composite Navigation

The INS/CNS (Inertial Navigation System/Celestial Navigation System) composite navigation system error models and different error filtering estimation methods are studied in the paper. To avoid the difficult parameter selection of the standard UKF’s (Unscented Kalman Filter) sigma points weights calculation, a new sampling point weights calculation method is presented. The new sampling point weights are only related to the state vector’s probability density function. Furthermore, both of EKF (Extended Kalman Filter) and the new UKF are used to analyze the impact of the initial installation error on the filtering accuracy. Numerical simulation results show that when the star sensor has large installation misalignment angles, the new UKF can improve the nonlinear errors more effectively than EKF.
Understanding cyber technologies in a physical way

In this paper, we would discuss a bit more how cyber technologies operate in a physical way and why it's important to understand the limitations of a physical reality. So commonly - mathematical terms would get assumed as abstract - so they could be considered as somehow perfect. On the other hand, the reality would offer many boundaries and obstacles that should be avoided or better explained. As it's known, cyber technologies belong to digital systems containing so many electronic components that would have their physical performances. Those performances as well as the entire cyber defense of such digital technologies would be analyzed deeper through this effort.
Cybercriminals' Motivations for Targeting Government Organizations

The increasing growth associate degreed diversification within the methods and practices of lawbreaking has become a troublesome obstacle so as each to grasp the extent of embedded risks and to outline economic policies of bar for corporations, establishments and agencies. This study represents the foremost comprehensive review of the origin, typologies and developments of law-breaking development over the past decade therefore far. By means that of this e- laborate study, this paper tackles the difficulty initial describing and discussing former totally different criteria of classification in the field and secondly, providing a broad list of definitions and an analysis of the cybercrime practices. An abstract taxonomy of law-breaking is introduced and described. The proposal of a classification criterion is employed in con- junction with a cybercrime hierarchy derived from the degrees and scale of vulnerability and targets
Toward Internal-state-based Parameterized model of Robot’s Touching Manners based on Subjective Evaluation

In this paper, we discuss an emotional touch model of a stuffed toy robot based on the user’s perception. To enable the robot’s physical contact with the user toward future bidirectional touch interaction, we investigate a comprehensive model of active touch by a stuffed toy robot on the user’s body focusing on potentially cyclic parameters of the touch, which are 1) cycle duration and 2) dragging length, to include various touches such as patting, stroking, and hugging. The testbed robot contained three servomotors to move its left arm to touch the user and make physical contact. From the result of subjective evaluation varying the two touching parameters, 1) the factor analysis extracted not only emotional parameters, “activeness,” “mildness,” and “happiness,” but also a faithful attitude, “honesty,” and 2) the contribution rate and the ANOVA for the standard factor scores showed the importance of “activeness” and “mildness,” especially affected by the cycle duration.
A visual approach for analyzing readmissions in intensive care medicine

Intensive care units (ICUs) are under constant pressure to balance capacity. ICUs have a limited number of resources and therefore effective monitoring of planned and unplanned transfers is crucial. Transfers can result in critical readmissions, i.e. "down" transfers from ICUs to a normal ward or an intermediate care unit (IMC) and back "up" to an ICU within a short time span. In this work, we present a tool to visually analyze such readmissions. Patient transfer data is extracted from a clinical data warehouse via HL7-FHIR. The interactive prototype consists of a timeline of readmission cases, an aggregated view of transfer flows between wards, and histograms and calender heatmaps to show a set of key performance indicators. The aim of our tool is to support identifying peaks, discovering temporal patterns, comparing wards, and investigating potential causes. We report on our user centered approach, describe the data pipeline, present the visualization and interaction techniques of the functional prototype, and discuss initial feedback.
Recent Developments in Privacy-preserving Mining of Clinical Data

With the dramatic increases in both the capability to collect personal data and the capability to analyze large amounts of data, increasingly sophisticated and personal insights are being drawn. These insights are valuable for clinical applications but also open up possibilities for identification and abuse of personal information. In this paper, we survey recent research on classical methods of privacy-preserving data mining. Looking at dominant techniques and recent innovations to them, we examine the applicability of these methods to the privacy-preserving analysis of clinical data. We also discuss promising directions for future research in this area.
Developers’ viewpoints to avoid bug-introducing changes

During software development, developers can make assumptions that guide their development practices to avoid bug-introducing changes. For instance, developers may consider that code with low test coverage is more likely to introduce bugs; and thus, focus their attention on that code to avoid bugs, neglecting other factors during the software development process. However, there is no knowledge about the relevance of these assumptions for developers. This study investigates the developers’ viewpoints on the relevance of certain assumptions to avoid bug-introducing changes. In particular, we analyze which assumptions developers can make during software development; how relevant these assumptions are for developers; the common viewpoints among developers regarding these assumptions; and the main reasons for developers to put more/less relevance for some assumptions. We applied the Q-methodology, a mixed-method from the psychometric spectrum, to investigate the relevance of assumptions and extract the developers’ viewpoints systematically. We involved 41 developers analyzing 41 assumptions extracted from literature and personal interviews. We identified five viewpoints among developers regarding their assumptions around bug-introducing changes. Despite the differences among the viewpoints, there is also consensus, for example, regarding the importance of being aware of changes invoking high number of features. Moreover, developers rely on personal and technical reasons to put relevance on some assumptions. These findings are valuable knowledge for practitioners and researchers towards future research directions and development practices improvements.
Restoration of Image using Discrete Wavelet Transform with Filtering Technique

An image worth thousands of words and is often beneficial media to interpret knowledge. But the image captured is often corrupted by atmospheric noises, camera sensors, instrumentation noises, etc. This effect will decrease the accuracy of the image and harm the fine details it holds. This paper proposes a hybrid technique that involves a Discrete WaveletTransformatin based algorithm to restore and denoise the degraded images. The workflow, methodologies, performance analysis of this new technique is presented in this paper. The paper further compares the performance of the algorithm with the well known Lucy Richardson filter. These methods output is evaluated on the two benchmark metrics, namely peak signal to noise ratio (PSNR) and mean square error (MSE).
The performance of distributed rooftop photovoltaic power generation system: a case of Anhui Province, China

The distributed rooftop photovoltaic power generation system is an important system of solar energy utilization in China. In the present paper, the performance of distributed rooftop photovoltaic power generation system is analyzed. The results showed that the data of Meteonorm, Solargis and NASA is effective in China. And the Meteonorm data source is preferable. The effect of actual installation inclination angle on the system power generation can be ignored when the angle is less than 20°.
Enhanced Virtual Reality

Virtual Reality (VR) training is an emerging method, which is widely deployed in more and more applications. Compared with traditional physical training and video games-based training, VR training can not only provide a sense of realism and immersion similar to physical training but can also train at any time and place, saving time and money. However, due to some constraints like lacking reflections of the ambient environment, the realism and immersion of VR training are insufficient. Therefore, in this paper, we propose enhanced VR training which senses the ambient environment and reflects them as dynamic unexpected training tasks to solve the above problems.
Spammers Detection on Online Social Media Based on Machine Learning

Online social media is one of the most used platforms in the past decade. It is used to build social networks or social relationships with other people who share similar interests, work, backgrounds or any real-life connections. Having produced such people-friendly benefits, the social media platform has met high-end communication needs of the people. This, being a platform for millions of people, also attracts spammers who intend to divert the positive utility of the platform. Twitter, on top of it, which is highly content driven, is more vulnerable. Therefore, this paper presents an approach to identify the spammers on Twitter platform. It examines the machine learning algorithm. The feature-based selection technique increases the accuracy of the parameters. It distinguishes the bot users from that of legitimate users, thus, providing a more secure and trustworthy social media platform.
A Color and Composition-based Image Retrieval System by Image-Query Drawing Method with Color-Impression Database

In this paper, we propose a color and composition-based image retrieval system with an image-query drawing method for color-impression database. The aim of this system to provide a method of image-query expression in image retrieval for representing his/her imaginary image. This system retrieves image data similar to an image-query drawn as a grid image corresponding to the overall color combination and composition features on the searcher’s imagination. The most significant features of this system are that it sets a color-impression database storing expert’s knowledge about impression words and their corresponding to color combinations, and an image-query drawing function on the query part of image database. Thus, the searcher chooses three color combinations as color feature and paints a N*N grid image by the color combination to reflect composition feature in the image-query. All images stored into the image database also have metadata containing keywords that describe image’s contents to categorize the images. A semantic space of the images is selected by the keywords to retrieve similar images in the specific domain. In this time, we adopted color combinations with impression words based on psychology-color research knowledge into the color-impression database. The data stored in the color-impression database can be replaced with other color-impression knowledge. So that, a searcher can paint each grid with various color usage of favorite painters and artists. For image retrieval, this system also compares color histograms, that consist of representative colors, on each grid between image-query and all images that are divided into N*N grids to calculate local similarity between grids. The sum of the local similarity on the color histograms corresponds with global similarity between the image-query and images stored into the image database. Godlove’s color difference formula in HSV color space is applied to the similarity calculation. We show the details of this method and its effectiveness and feasibility in this paper.
Particle filter of measurement information fusion through Cokriging method

For the higher nonlinear or non-Gauss dynamic system, this paper put forward a new centralized fusion particle filter (PF) method which uses the Cokriging method to fuse the measurement information of different precision sensors. It effectively makes full use of the statistical property of all the sensors’ noises, and the property of which can be calculated out of line, and the calculated form of measurements weighted summation of particle filter state estimation under Linear Minimum Mean Square Error (LMMSE) estimation with the new Cokriging method. Then the particle filter of all the measurements weighted summation is formed. Compared with the traditional particle filter fusion algorithm, the fusion particle filter in this paper not only has the better optimal fusion estimation accuracy, but also can save lots of running time through calculating weighted coefficient out of line. Both the theoretical analysis and computer simulation indicate that this algorithm is valid and advantageous.
Fuzzy Influence Graph: A Model for Human Trafficking

In this chapter, we present infuence fuzzy graphs, a new fuzzy graph based structure, capable of modeling illicit flows like illegal immigration networks. Basic results and characterization of concepts are provided and susceptibility of trafficking routes are examined.
Speech Signal Processing for Identification of Under-Resourced Languages

The present research paper addresses the problem of language identification (LID) in short-time utterances. The success of support vector machine in acoustic modeling for speech recognition and accent recognition motivated the author to extend its applicability to LID. Acoustic model and the classifier play an important role in system performance. The paper presents the LID task by the use of state-of-the-art i-vector system using MFCC-SDC as acoustic feature and support vector machine (SVM) as backend classifier. Both the systems have been implemented for the identification of two Indian languages (Hindi and Punjabi). The speech utterances are divided into short segments of 5, 10, 20 and 35 s. The performance of the system is measured in EER (%), and the results obtained highlights the efficiency of the model in short duration utterances.
Missing trajectory estimation using single station EOT system data

During test and evaluation (T&E) process of airborne objects/targets, electro-optical tracking (EOT) system has garnered importance for providing accurate and precise positional information of trajectory along with monitoring of various critical events. In absence of a ranging sensor like LRF (Laser Range Finder) or RoR (Range only Radar), a single EOT system only provides line-of-sight (LoS) information i.e. azimuth and elevation as captured through its passive optical (IR & CCD) sensors. However, range information is obtained with the LOS data of two or more time-synchronized EOT systems, positioned at distributed geographical locations (for a fairrelative apex angle of LOS),by means of triangulation in order to furnish the positional information with respect to time. It is pertinent that EOT system is likely to miss the target of interest (ToI) due to various environmental constraints. Hence, during some T&E processes, it is not feasible to produce a portion of trajectory information even with the data available from a single EOT system. This paper aims towards extracting data of such missing portions, through a novel approach, based on estimation correlated with LOS data available from a single EOT system. The approach has also been incorporated through a neural network model that estimates the missing segment accurately under supervised learning. Extensive offline experiments have been conducted to assess the efficacy of the proposed prediction paradigm with the results of remarkable improvement.
Cross-domain Robot Map Building and Planning in Emergencies

When a cross-domain mobile robot encounters a variety of emergencies, it needs to quickly plan a new path at the bottom of the controller architecture. The frequency of map construction and planning of traditional robot autonomous systems is low, which cannot meet the real-time requirements of the bottom control system. To solve this problem, a method based on multi-sensor data fusion is proposed to construct a real-time grid map in a complex and multi-emergency environment. This not only achieves a higher map refresh frequency but also enriches the information represented by the map. Then, based on the real-time map, a path planning method combining dynamic window approach (DWA) and safe search strategy is proposed. The feasibility of the designed planning algorithm is verified in simulation and experiment. The algorithm used has a high frequency and gives a path to avoid dangerous conditions, which can meet the real-time planning needs of the bottom control system under emergencies.
Speaker identification analysis for SGMM with k-means and fuzzy C-means clustering using SVM statistical technique

Speaker Identification denotes the speech samples of known speaker and it identifies the best matches of the input model. The SGMFC method is the combination of Sub Gaussian Mixture Model (SGMM) with the Mel-frequency Cepstral Coefficients (MFCC) for feature extraction. The SGMFC method minimizes the error rate, memory footprint and also computational throughput measure needs of a medium-vocabulary speaker identification system, supposed for preparation on a transportable or otherwise. Fuzzy C-means and k-means clustering are used in the SGMM method to attain the improved efficiency and their outcomes with parameters such as precision, sensitivity and specificity are compared.
Pruning by leveraging training dynamics

We propose a novel pruning method which uses the oscillations around 0, i.e. sign flips, that a weight has undergone during training in order to determine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classification architectures, show that it is competitive with existing methods and achieves state-of-the-art performance for levels of sparsity of 99.6 % and above for 2 out of 3 of the architectures tested. Moreover, we demonstrate that our method is compatible with quantization, another model compression technique. For reproducibility, we release our code at https://github.com/AndreiXYZ/flipout.
Visible Light Images

This chapter is concerned with the acquisition, processing, and display of image data. We will first learn about the physical principles of electromagnetic waves, which include visible light (Sect. 5.2), and then discuss how cameras capture visible light images (Sect. 5.3). The various ways that raster data can be stored on a computer are explained in Sect. 5.4 and the main tools for importing, manipulating, and exporting image data are then presented in Sects. 5.5 and 5.6. We will then make use of the knowledge that we have gained to acquire, process, and display images to complete the exercises in Sect. 5.7.
Geometric based Feature Extraction Techniques for Facial Expression Recognition using SVM

In recent years, facial expression recognition becomes one of the interesting research topic in developing human computer interaction applications. In geometric-based methods, irrelevant features increases the computational cost of the facial expression recognition system (FERs). To overcome this issue, a geometric-based feature extraction method is proposed to extract the most relevant feature map to recognize six basic facial expressions including neutral. In this proposed method, the difference in measure of distance and triangle formation of 9 facial landmark points are calculated for each image with reference to the neutral image. Moreover, the subtle changes in facial muscle forms the deformation of facial points for different facial expressions. Hence, the difference of Euclidean distance and triangulation of each facial points of diverse expression images are calculated with respect to neutral image which are generated as feature maps and given to the SVM classifier. Further, the proposed method is evaluated using CK+, MMI and Self-collected datasets.
Chapter 9. Scoledit

The purpose of Scoledit is to build a computer-aided longitudinal corpus of texts written by pupils between 6 and 11 years as well as associated automatic processing tools. This project seeks to produce linguistic descriptions of pupils’ writings and to facilitate the teaching of spelling and writing. Currently, an increasing number of projects aim to create large primary school corpora of French (Elalouf, 2005; Garcia-Debanc & Bonnemaison, 2014; David & Doquet, 2016). However, these corpora are neither longitudinal nor associated with natural language processing (NLP) tools (Wolfarth, 2017). This chapter discusses some of the automated tools for linguistic analyses developed and the advantages of the Scoledit project in the context of language teaching