Reviewerly/exHarmony huggingface dataset:
Controlling your contents with the breath: Interactive breath interface for VR, games, and animations

In this paper, we propose a new interface to control VR(Virtual reality) contents, games, and animations in real-time using the user's breath and the acceleration sensor of a mobile device. Although interaction techniques are very important in VR and physically-based animations, UI(User interface) methods using different types of devices or controllers have not been covered. Most of the proposed interaction techniques have focused on screen touch and motion recognition. The direction of the breath is calculated using the position and angle between the user and the mobile device, and the control position to handle the contents is determined using the acceleration sensor built into the mobile device. Finally, to remove the noise contained in the input breath, the magnitude of the wind is filtered using a kernel modeling a pattern similar to the actual breath. To demonstrate the superiority of this study, we produced real-time interaction results by applying the breath as an external force of VR contents, games, and animations.
Identifying Compiler and Optimization Options from Binary Code using Deep Learning Approaches

When compiling a source file, several flags can be passed to the compiler.These flags, however, can vary between debug and release compilation.In the release compilation, in fact, smaller or faster executables are usually preferred, whereas for a debug one, ease-of-debug is preferred over speed and no optimization is involved.After the compilation, however, most of the flags used cannot be inferred from the compiled file.These flags could be useful in case we want to classify if an older build was made for release or debug purposes, or to check if the file was compiled with flags that could expose vulnerabilities.In this paper we present a deep learning network capable of automatically detecting, with function granularity, the compiler used and the presence of optimization with 99% accuracy.We also analyze the change in accuracy when submitting increasingly shorter amounts of data, from 2048 up to a single byte, obtaining competitive results with less than 100 bytes.We also present our process in the huge dataset creation and manipulation, along with a comparison with other less successful networks using functions of varying size.
Automatic Machine Translation of Poetry and a Low-Resource Language Pair

Automatic machine translation is gaining more and more attention in a particular segment of the research community that treats various topics from artificial intelligence, natural language processing, computational linguistics, machine learning and data science. Machine translation is a complex task in which a computer is utilised for the purpose of translating from source to one or more target languages without human involvement, or with a minimum of interventions. In general, machine translation could be implemented in higher education and academic curricula in a variety of possible fields and applications. Although there are several approaches to machine translation, two are dominant today - statistical and neural machine translation. Both are being used in this research in form of two online machine translation systems. The aim of this paper is to examine the usability of machine translation for poetry and a low-resource language pair, such as Croatian-German. The authors chose to use a data set that contained the works of a relevant contemporary poet of the Croatian language and the translations of his poems in German that were conducted by two professional literary translators. The paper demonstrates the effectiveness of machine translation of poetry with regard to special automatic quality metrics.
Dynamic Projection Mapping of Deformable Stretchable Materials

We present a method for dynamic projection mapping on deformable, stretchable and elastic materials (e.g. cloth) using a time of flight (ToF) depth camera (e.g. Azure Kinect or Pico-Flexx) that come equipped with an IR camera. We use Bezier surfaces to model the projection surface without explicitly modeling the deformation. We devise an efficient tracking method that tracks the boundary of the surface material using the IR-Depth camera. This achieves realistic mapping even in the interior of the surface, with simple markers (e.g. black dots or squares) or without markers entirely, such that the projection appears to be printed on the material. The surface representation is updated in real-time using GPU based computations. Further, we also show that the speed of these updates is limited by the camera frame rate and therefore can be adopted for higher speed cameras as well. This technique can be used to project on several stretchable moving materials to change their appearance.
Impression analysis of trending topics in Twitter with classification algorithms

The use of a simple categorization of emotions or even the use of universal expressions of emotions is unsuitable to properly identify sentiments in posts in some situations. The main goal of this paper is to analyze impressions of Twitter messages in the 19S Mexican earthquake of 2017 through machine learning techniques, specifically with classification algorithms. To identify impressions, we applied sentiment analysis based on supervised methods, and we identified a customized list of terms that we called impressions, which reflects the nature of tweets related to the event of study. Our proposed impressions analysis is useful to understand Twitter messages during different events since impressions adapt to each situation and context, based on emotional frameworks. We found that Twitter is useful to prove or disprove the information disseminated by the mass media and mainly for asking for help. Analyzing this kind of data in real-time will be useful for decision-making. The contribution of this paper is to fill the gap in the sentiment analysis area and the automatic identification of eleven impressions for disaster events in Twitter using machine learning techniques. This method has been called impression analysis.
Design of Arduino Uno Based Duck Egg Hatching Machine With Sensor DHT22 and PIR Sensor

Duck Egg hatching machine is a tool to petrify the farmers duck in cultivating duck. In the process of hatchery eggs naturally have a percentage of 70% to 75%. This is what makes the main benchmark in the process of designing a duck egg-based egg hatching machine with DHT22 and PIR sensors. The problem is the concept of how the incubator machine is designed and how the performance of the incubator machine is able to increase the egg percentage to hatch. In conducting this research, it is done by observing directly to the farm. Of the observation was then refortified by taking some literature discussing the egg hatching machine. The literature taken in the form of a handbook is held by the farmer itself as well as an existing research journal. Performance on automatic egg hatching machines designed in this research can increase the percentage of hatch for eggs by 95%. In the process of mechanical immersion by the appliance has been quite stable either in its temperature or humidity.
Web Users Clustering by their Behaviour on the Network

Aim of this paper is to analyse possibility of cluster users of a selected network on the base of their browsing behaviour. Source of information is web access log file which consist of all the important information. Paper presents idea of pre-processing of information from a web access log file and it also presents K-means clustering algorithm used for identification of user groups on the base of their behaviour on the internet. Presented methodology can be used in the area of cyber defence also for other tasks.
Overview of JavaScript Engines for Resource-Constrained Microcontrollers

IoT devices often use small, constrained micro-controllers to implement their functionality. Usually, they are programmed with languages like C or C++, but there is a trend to use interpreted languages for this task. In this paper, we focus on JavaScript. We discuss the advantages and disadvantages of using this interpreted language for microcontroller development, we give an overview of the available JavaScript engines for constrained devices, and we compare these interpreters' general properties. One finding shows that the projects can be divided into embeddable interpreters (JerryScript, Duktape, mJS) and standalone runtimes (Espruino, Moddable, Mongoose OS). It was also identified that the interpreters follow different architectural approaches.
The Effect of Spatial Reference on Visual Attention and Workload during Viewpoint Guidance in Augmented Reality

Considering human capability for spatial orientation and navigation, the visualization used to support the localization of off-screen targets inevitably influences the visual-spatial processing that relies on two frameworks. So far it is not proven which frame of reference, egocentric or exocentric, contributes most to efficient viewpoint guidance in a head-mounted Augmented Reality environment. This could be justified by the lack of objectively assessing the allocation of attention and mental workload demanded by the guidance method. This paper presents a user study investigating the effect of egocentric and exocentric viewpoint guidance on visual attention and mental workload. In parallel to a localization task, participants had to complete a divided attention task using the oddball paradigm. During task fulfilment, the heart rate variability was measured to determine the physiological stress level. The objective assessment of mental workload was supplemented by subjective ratings using the NASA TLX. The results show that egocentric viewpoint guidance leads to most efficient target cueing in terms of faster localization, higher accuracy and slower self-reported workload. In addition, egocentric target cueing causes a slight decrease in physiological stress and enables faster recognition of simultaneous events, although visual attention seemed to be covertly oriented.
Filter Pruning using Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks

Since the convolutional neural networks are often trained with redundant parameters, it is possible to reduce redundant kernels or filters to obtain a compact network without dropping the classification accuracy. In this paper, we propose a filter pruning method using the hierarchical group sparse regularization. It is shown in our previous work that the hierarchical group sparse regularization is effective in obtaining sparse networks in which filters connected to unnecessary channels are automatically close to zero. After training the convolutional neural network with the hierarchical group sparse regularization, the unnecessary filters are selected based on the increase of the classification loss of the randomly selected training samples to obtain a compact network. It is shown that the proposed method can reduce more than 50% parameters of ResNet for CIFAR-10 with only 0.3 % decrease in the accuracy of test samples. Also, 34% parameters of ResNet are reduced for TinyImageNet-200 with higher accuracy than the baseline network.
A Granular Approach to Interval Output Estimation for Rule-Based Fuzzy Models

Rule-based fuzzy models play a dominant role in fuzzy modeling and come with extensive applications in the system modeling area. Due to the presence of system modeling error, it is impossible to construct a model that fits exactly the experimental evidence and, at the same time, exhibits high generalization capabilities. To alleviate these problems, in this study, we elaborate on a realization of granular outputs for rule-based fuzzy models with the aim of effectively quantifying the associated modeling errors. Through analyzing the characteristics of modeling errors, an error model is constructed to characterize deviations among the estimated outputs and the expected ones. The resulting granular model comes into play as an aggregation of the regression model and the error model. Information granularity plays a central role in the construction of granular outputs (intervals). The quality of the produced interval estimates is quantified in terms of the coverage and specificity criteria. The optimal allocation of information granularity is determined through a combined index involving these two criteria pertinent to the evaluation of interval outputs. A series of experimental studies is provided to demonstrate the effectiveness of the proposed approach and show its superiority over the traditional statistical-based method.
A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition

Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to better understand and analyze systems under development. Domain modelling is used during requirements analysis or the early stages of design to transform informal requirements written in natural language to domain models which are analyzable and more concise. Since domain modelling is time-consuming and requires modelling skills and experience, many approaches have been proposed to extract domain concepts and relationships automatically using extraction rules. However, relationships and patterns are often hidden in the sentences of a problem description. Automatic recognition of relationships or patterns in those cases requires context information and external knowledge of participating domain concepts, which goes beyond what is possible with extraction rules. In this paper, we draw on recent work on domain model extraction and envision a novel technique where sentence boundaries are customized and clusters of sentences are created for domain concepts. The technique further exploits a BiLSTM neural network model to identify relationships and patterns among domain concepts. We also present a classification strategy for relationships and patterns and use it to instantiate our technique. Preliminary results indicate that this novel idea is promising and warrants further research.
iBox

We present a vision of data-informed network simulation to address significant shortcomings in the state of the art. We substantiate our position with proof points based on iBox, which leverages networking domain knowledge and machine learning (ML) models, coupled with plentiful data, to provide a pathway to perpetual renewal of network simulators.
Papaya Fruit Type Classification using LBP Features Extraction and Naive Bayes Classifier

This research proposes the classification method of papaya types based on leaf images using the Naïve Bayes classifier and LBP feature extraction. Papaya leaves are used because they have a unique pattern and texture from their leaf bones, besides leaf-based classification can be done before papaya trees produce fruit. In the preprocessing process, three stages are carried out in which conversion to grayscale, image adjustment and resize, to produce a good LBP feature extraction. The resize process is useful to reduce computational time during the training and testing process, where this process is done at the end of the preprocessing process to get a better pixel value. Image adjustment is used to sharpen the papaya leaf bone which is the main pattern of the papaya leaf. At the feature extraction stage, an image zoning process is carried out, by dividing the image into nine zones, to produce nine LBP features for each image. In the implementation phase, a total of 150 papaya leaf images were used which consisted of 125 training images and 25 testing images. Based on the results of the classification using the Naïve Bayes classifier by using nine zones each with 128-pixel cell size and image adjustment resulting 96% accuracy. The results of this accuracy are better than using cell sizes 32 and 64 and without image adjustment.
Neural Geometric Parser for Single Image Camera Calibration

We propose a neural geometric parser learning single image camera calibration for man-made scenes. Unlike previous neural approaches that rely only on semantic cues obtained from neural networks, our approach considers both semantic and geometric cues, resulting in significant accuracy improvement. The proposed framework consists of two networks. Using line segments of an image as geometric cues, the first network estimates the zenith vanishing point and generates several candidates consisting of the camera rotation and focal length. The second network evaluates each candidate based on the given image and the geometric cues, where prior knowledge of man-made scenes is used for the evaluation. With the supervision of datasets consisting of the horizontal line and focal length of the images, our networks can be trained to estimate the same camera parameters. Based on the Manhattan world assumption, we can further estimate the camera rotation and focal length in a weakly supervised manner. The experimental results reveal that the performance of our neural approach is significantly higher than that of existing state-of-the-art camera calibration techniques for single images of indoor and outdoor scenes.
Investigation on Watermarking Algorithm for Secure Transaction of Electronic Patient Record by Hybrid Transform

Due to the development of the latest technologies in the areas of communication and networking, the present businesses are moving to the digital world for accuracy, accessibility, safety and security. Normally, the medical image requires additional care to handle because treatment is mainly focused on the detail received from the medical images. Nowadays, digital watermarking is a blooming methodology for protecting medical data as for as security is concerned. This proposed method gives a new watermarking scheme for a secure transfer of Electronic Patient Record (EPR) using Hybrid Transform (DWT+DCT+SVD). Initially, the medical image is decomposed by Discrete Wavelet Transform then after Discrete Cosine Transform is performed in LH band and then Singular Value Decomposition is performed and a similar process to be done for the watermark image for embedding process and perform an inverse process for obtaining the watermarked image. Similarly, the above process to be done for the extraction process on the watermarked image to exact the EPR data and medical image. The effectiveness and performance of the proposed watermarking scheme being analyzed by measuring Peak Signal to Noise Ratio (PSNR) and Quality Index. The result is discussed with the help of experimental results using MATLAB tools.
SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold

Complex-valued data are ubiquitous in signal and image processing applications, and complex-valued representations in deep learning have appealing theoretical properties. While these aspects have long been recognized, complex-valued deep learning continues to lag far behind its real-valued counterpart. We propose a principled geometric approach to complex-valued deep learning. Complex-valued data could often be subject to arbitrary complex-valued scaling; as a result, real and imaginary components could covary. Instead of treating complex values as two independent channels of real values, we recognize their underlying geometry: we model the space of complex numbers as a product manifold of nonzero scaling and planar rotations. Arbitrary complex-valued scaling naturally becomes a group of transitive actions on this manifold. We propose to extend the property instead of the form of real-valued functions to the complex domain. We define convolution as the weighted Fréchet mean on the manifold that is equivariant to the group of scaling/rotation actions and define distance transform on the manifold that is invariant to the action group. The manifold perspective also allows us to define nonlinear activation functions, such as tangent ReLU and G -transport, as well as residual connections on the manifold-valued data. We dub our model SurReal, as our experiments on MSTAR and RadioML deliver high performance with only a fractional size of real- and complex-valued baseline models.
Trans-Compiler based Mobile Applications code converter: swift to java

Numerous commercial tools like Xamarin, React Native and PhoneGap utilize the concept of cross-platform mobile applications development that builds applications once and runs it everywhere opposed to native mobile app development that writes in a specific programming language for every platform. These commercial tools are not very efficient for native developers as mobile applications must be written in specific language and they need the usage of specific frameworks. In this paper, a suggested approach in TCAIOSC tool to convert mobile applications from Android to iOS is used to develop the reverse path translation. Moreover, native mobile apps functionalities like making a phone call, alert messages, vibration and more functions are tested by using extensive techniques like BLEU and tokens accuracy. Primarily results showed substantial success in code conversion from swift to java.
Attending the Emotions to Detect Online Abusive Language

In recent years, abusive behavior has become a serious issue in online social networks.In this paper, we present a new corpus for the task of abusive language detection that is collected from a semi-anonymous online platform, and unlike the majority of other available resources, is not created based on a specific list of bad words.We also develop computational models to incorporate emotions into textual cues to improve aggression identification.We evaluate our proposed methods on a set of corpora related to the task and show promising results with respect to abusive language detection.
Generalization of Rayleigh's criterion on parameter estimation with incoherent sources

The basic idea behind Rayleigh's criterion on resolving two incoherent optical point sources is that the overlap between the spatial modes from different sources would reduce the estimation precision for the locations of the sources, dubbed Rayleigh's curse. We generalize the concept of Rayleigh's curse to the abstract problems of quantum parameter estimation with incoherent sources. To manifest the effect of Rayleigh's curse on quantum parameter estimation, we define the curse matrix in terms of quantum Fisher information and introduce the global and local immunity to the curse accordingly. We further derive the expression for the curse matrix and give the necessary and sufficient condition on the immunity to Rayleigh's curse. For estimating the one-dimensional location parameters with a common initial state, we demonstrate that the global immunity to the curse on quantum Fisher information is impossible for more than two sources.
Preparation of nonclassical states by displacement-based quantum scissors

We theoretically study the statistical properties of non-Gaussian states generated by inputting coherent state, single-mode squeezed vacuum state and thermal state to the displacement-based single-side quantum scissor. Compared to the case without the displacement, it is shown that the displacement-based single-side quantum scissor with large coherent amplitudes can not only effectively enhance the success probability of quantum state preparation in the high-transmittance range, but also improve the statistical properties of the generated states involving the average photon number, the signal to noise ratio and the nonclassicality. These results indicate that the usage of the displacement-based single-side quantum scissor may have potential applications for the optical interferometry.