Reviewerly/exHarmony huggingface dataset:
Autonomous Obstacle Avoidance Robot Using Regression

Obstacle avoidance is considered as one of the main features of autonomous intelligent systems. There are various methods for obstacle avoidance. In this paper, obstacle avoidance is achieved by the difference between left wheel velocity and right wheel velocity of differential drive robot. The magnitude of difference between the wheel velocities is used to steer the robot in the correct direction. Data is collected by driving the robot manually. Ultrasonic sensors are used for distance measurement and IR sensors are used to collect the data of wheel velocities. This data is used to build a linear machine learning model which uses sonar data as input features. The model is used to predict the wheel velocities of the differential drive robot. The model built is then programmed into Atmega328 microcontroller using Arduino IDE. This enables the mobile robot to steer itself to avoid the obstacles. Since all the components used for this robot are highly available and cost-effective, the robot is economically affordable.
Automatic Assessment of Language Ability in Children with and without Typical Development

This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance-We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.
3D human pose estimation model using location-maps for distorted and disconnected images by a wearable omnidirectional camera

Abstract We address a 3D human pose estimation for equirectangular images taken by a wearable omnidirectional camera. The equirectangular image is distorted because the omnidirectional camera is attached closely in front of a person’s neck. Furthermore, some parts of the body are disconnected on the image; for instance, when a hand goes out to an edge of the image, the hand comes in from another edge. The distortion and disconnection of images make 3D pose estimation challenging. To overcome this difficulty, we introduce the location-maps method proposed by Mehta et al.; however, the method was used to estimate 3D human poses only for regular images without distortion and disconnection. We focus on a characteristic of the location-maps that can extend 2D joint locations to 3D positions with respect to 2D-3D consistency without considering kinematic model restrictions and optical properties. In addition, we collect a new dataset that is composed of equirectangular images and synchronized 3D joint positions for training and evaluation. We validate the location-maps’ capability to estimate 3D human poses for distorted and disconnected images. We propose a new location-maps-based model by replacing the backbone network with a state-of-the-art 2D human pose estimation model (HRNet). Our model is a simpler architecture than the reference model proposed by Mehta et al. Nevertheless, our model indicates better performance with respect to accuracy and computation complexity. Finally, we analyze the location-maps method from two perspectives: the map variance and the map scale. Therefore, some location-maps characteristics are revealed that (1) the map variance affects robustness to extend 2D joint locations to 3D positions for the 2D estimation error, and (2) the 3D position accuracy is related to the 2D locations relative accuracy to the map scale.
Framework Choice Criteria for Mobile Application Development

Mobile applications development is complex. In order to build mobile apps that target all users, developers must put up with all the existing operating systems, software development kits (SDKs), devices various screen resolutions and existing mobile app development technology that is in constant evolution. And if this is not enough, developers must choose the right approach to build their app. Taking those decisions has a huge impact on the eventual cost, time, and success of an application building. In order of importance, it comes right after choosing the functionality of the app itself. If developers fail to match an app demands to the right development approach, it can turn their project into a certain failure. This paper aims to compare between the mobile app development approaches and suggest a decisional framework to choose between them.
Hierarchical Resolution For Structured Predicate Definitions

Structured predicates are defined by sets of non-Horn clauses satisfying certain constraints. These predicates form hierarchies. Resolution-based inference for structured predicates and its relationship with computability are analyzed. This paper introduces hierarchical resolution for knowledge bases of structured predicate definitions. The rules defining predicates outside of the hierarchies of the predicates of a goal can be eliminated from its derivations. Two classes of these definitions are identified for which hierarchical resolution is complete and coincides with SLD resolution, respectively.
The Effect of Camera Calibration on Multichannel Texture Classification

The efficiency of a texture classification procedure depends on the color space in which it is performed. Classification in a perceptually meaningful space requires chromatic coordinates obtained from a calibrated acquisition setup. The authors assess the impact of camera calibration, within a generic color picture acquisition workflow, on the performance of a number of texture classification techniques. An image calibration pipeline is established and applied to a texture database, and the accuracy of the classification algorithms is evaluated for each step. The results show that the most significant step of the workflow is color rendering although the effect is relatively small. Hence precise scene-referred characterization of the raw data from an acquisition camera is not essential for most texture classification tasks. In addition, working with output-referred RGB data is likely to be adequate for the majority of classification tasks.
Stochastic graph recurrent neural network

Representation learning over dynamic graphs has attracted much attention because of its wide applications. Recently, sequential probabilistic generative models have achieved impressive results because they can model data distributions. However, modeling the distribution of dynamic graphs is still extremely challenging. Existing methods usually ignore the mutual interference of stochastic states and deterministic states. Besides, the assumption that latent variables follow Gaussian distributions is usually inappropriate. To address these problems, we propose stochastic graph recurrent neural network (SGRNN), a sequential generative model for the representation learning over dynamic graphs. It separates stochastic states and deterministic states in the iterative process. To improve the flexibility of latent variables, we set the prior distribution and posterior distribution as semi-implicit distributions and propose DSI-SGRNN. In addition, to alleviate the KL-vanishing problem in SGRNN, a simple and interpretable structure is proposed based on the lower bound of KL-divergence. The proposed structure introduces a few extra parameters and can be implemented with a few lines of code modification. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed model.
A Novel Visual Cryptography Scheme Shared with Edge Information Embedded QR Code

QR code has been widely used in our daily life due to its convenience, and the security of QR code becomes a rising problem. In this paper, we introduce a new algorithm that adds the edge of the gray image to the QR code, and the edge information is captured by the Canny detector. And then we use the random grid based visual cryptography scheme (RG-VCS) to generate shares. Each share of the VCS is an individual QR code that can be scanned by a QR code reader, and the secret information can be decoded by purely stacking without computation. Meanwhile, we can also recover the secret image with better visual quality with the help of computation.
Introduction to the Special Issue on Federated Machine Learning

The articles in this special section focus on federated machine learning, an emerging research paradigm focusing on solving data-silos challenges in real-world industrial applications. It is a broad discipline that touches many topics, including distributed and collaborative learning, privacy-preserving machine learning, edge computing, and data valuation, etc. Its interdisciplinary nature calls for collaborative efforts from a variety of fields to establish new protocols, frameworks and systems to address unique challenges, and open problems. These articles highlight a selection of high-quality and original works in this new area, including accepted papers to the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019.
A Social Sensing Approach for Quality Changes of Real-World Services

In today's dynamic business environment, services keep changing and being updated to adapt to user demands evolution and technology trends continuously. Customers are eager for information about changes in the quality of services. So, they can make accurate decisions on service selection and get a better user experience. There are quite a lot of ways for service providers to actively promote their services, such as email notifications, mobile Apps notifications, real-world advertisements, etc. However, it is still difficult for customers to catch these changing information comprehensively and timely. Customers must have a quality changing sensing tool. Previous studies on quality sensing in services computing community are usually focused on simple web-based API services, and quality indicators to be sensed are usually technical-oriented. In this paper, we present a new approach for sensing service quality changes by social sensing. It makes use of publicly-disseminated news reports and user reviews on real-world services to identify what quality indicators are changing and which direction a change is towards (positive or negative). Deep learning is used for mining quality change information from a text corpus, and experiments conducted on real data of nursing home services show the effectiveness of the proposed method.
Watchers do not follow the eye movements of Walkers

Eye movements are a functional signature of how the visual system effectively decodes and adapts to the environment. However, scientific knowledge in eye movements mostly arises from studies conducted in laboratories, with well-controlled stimuli presented in constrained unnatural settings. Only a few studies have attempted to directly compare and assess whether eye movement data acquired in the real world generalize with those in laboratory settings, with same visual inputs. However, none of these studies controlled for both the auditory signals typical of real-world settings and the top-down task effects across conditions, leaving this question unresolved. To minimize this inherent gap across conditions, we compared the eye movements recorded from observers during ecological spatial navigation in the wild (the Walkers) with those recorded in laboratory (the Watchers) on the same visual and auditory inputs, with both groups performing the very same active cognitive task. We derived robust data-driven statistical saliency and motion maps. The Walkers and Watchers differed in terms of eye movement characteristics: fixation number and duration, saccade amplitude. The Watchers relied significantly more on saliency and motion than the Walkers. Interestingly, both groups exhibited similar fixation patterns towards social agents and objects. Altogether, our data show that eye movements patterns obtained in laboratory do not fully generalize to real world, even when task and auditory information is controlled. These observations invite to caution when generalizing the eye movements obtained in laboratory with those of ecological spatial navigation.
Feature Selection for Embedded Media in the Context of Personification

Now-a-days increase in embedded media applications attracted huge number of users because of no limitations and easy chat conversations. Most of the information is shared between known to unknown persons resulting in safety issues. Mainly embedded media data shared among users is high dimensional consists of comments, text messages, images and association data about users which basically describes context and relationship between embedded media users. In addition scale, noise, errors and incompleteness exacerbates the ever challenging problem for feature selection. As a result it is very much needed to analyse the importance and relevance of features to understand the personification of user identity. This paper proposes a machine learning technique for feature selection of safety domain features in a vector for personification. Detailed illustration of the importance and relevance of attributes towards better understanding of the personification is studied. Proposed approach is tested on real world embedded media data like Whatsapp and BlogCatlog to demonstrate findings. In addition, analysis of the user to user relationships and user to data relationship towards feature selection criteria is done. This proposed solution can improve the performance of feature selection of embedded media data prioritizing personification.
Application of genetic and K-means algorithms in clustering Babakoohi Anticline joints north of Shiraz, Iran

Abstract The fuzzy clustering technique is one of the ways of organizing data that presents special patterns using algorithms and based on the similarity level of data. In this study, in order to cluster the resulting data from the Babakoohi Anticline joints, located north of Shiraz, K-means and genetic algorithms are applied. The K-means algorithm is one of the clustering algorithms easily implemented and of fast performance; however, sometimes this algorithm is located in the local optimal trap and cannot respond with an optimal answer, due to the sensitivity of this algorithm to the centers of the primary cluster. In addition, it has some basic disadvantages, such as its inappropriateness for complicated forms and also the dependency of the final result upon the primary cluster. Therefore, in order to perform the study more accurately and to obtain more reliable results, the genetic algorithm is used for categorizing the data of joints of the studied area. Applying this algorithm for leaving the local optimal points is an effective way. The results of clustering of the aforementioned data using the two above techniques represent two clusters in the Babakoohi Anticline. Furthermore, for validity and surveying of the results of the suggested techniques, various mathematical and statistical techniques, including ICC, Vw, VMPC, and VPMBF, are applied, which supports the similarity of the obtained results and the data clustering process in two algorithms.
Geometric Invariant Image Authentication System using Hashing

In the case of image authentication, it has always been a challenging issue to achieve a tradeoff amongst robustness, discrimination, and tampering localization. In this work, this tradeoff has been achieved to a certain extent using the proposed hashing using features such as global and local. The global features are extracted from a modified ring partition approach. Markov absorption probabilities extract the saliency map from the image, which is then used to construct local features. The additional feature, the central orientation information, the distance between image center and furthest non-zero pixel, and image centroid are used to make the proposed system invariant to geometric distortion. The experimental analysis proves the robustness of our approach against content-preserving operations, discrimination, and detection of malicious tampering. The receiver operating characteristics (ROC) depicts the efficacy of the proposed method compared to existing ones.
An interpretable fuzzy system in the on-line signature scalable verification

This paper proposes new original solutions for the use of interpretable flexible fuzzy systems for identity verification based on an on-line signature. Such solutions must be scalable because the verification of the identity of each user must be carried out independently of one another. In addition, a large number of system users limit the possibilities of iterative system learning. An important issue is the ability to interpret the system rules because it explains how the similarity of test signatures to reference signature templates is assessed. In this paper, we propose an approach that meets all of the above requirements and works effectively for the on-line signatures' database used in the simulations.
Joint learning of convolution neural networks for RGB‐D‐based human action recognition

RGB-D-based human action recognition aims to learn distinctive features from different modalities and has shown good progress in practice. However, it is difficult to improve the recognition performance through directly training multiple individual convolutional networks (ConvNets) and fusing features later because complmentary information between different modalities cannot be learned. To address this issue, this Letter proposes a single two-stream ConvNets framework for multimodality learning that extract features through RGB and depth streams. Specifically, the authors first represent RGB-D sequence to motion images as the inputs of the proposed ConvNets for obtaining spatial–temporal information. Then, a features fusion and joint training strategy is adapted to learn RGB-D complementary features simultaneously. Experimental results on benchmark NTU RGB+D 120 dataset validate the effectiveness of the proposed framework and demonstrate that two-stream ConvNets outperforms the current state-of-the-art approaches.
How MT errors correlate with postediting effort: a new ranking of error types

ABSTRACT As a result of the rapid development of translation technologies such as machine translation (MT), the global translation industry has inevitably entered a stage of intensive human-machine interaction. A clear manifestation of this is the industrial shift from the traditional workflow of translating from scratch to a new paradigm in which translations are produced by postediting, i.e., translating by fixing the errors in machine-translated texts). Recently, this shift has been expedited by the advent of neural machine translation, a new MT approach that delivers higher output quality in various domains and language pairs. However, although the core of postediting is the identification and correction of the errors generated by MT systems, more research is still in need on how MT errors correlate with postediting effort (the human effort demanded to postedit a machine-translated text). This paper reports on a preliminary study that examines the correlation between translation error types and cognitive postediting effort using empirical data on participants’ pauses in the postediting process. It is found that cognitive effort expended on an error is positively correlated with the stretch of text in which the error is embedded.
Analysis of Weiner Filter Approximation Value Based on Performance of Metrics of Image Restoration

Image restoration is used to recover the image quality by reducing or eliminating the noise to go back to its original image. This study aims to investigate the performance of the Wiener Filter approximation value based on Mean Squared Error (MSE), Structural Similarly Index Image (SSIM), Root Mean Squared Error (RMSE) and Peak Signal Noise Ratio (PSNR) using the three (3) sample images in different dimensions and image quality with the integration of five (5) different Gaussian noise and K-filter approximation values. Based on experiment result, the Gaussian Noise value of 10 marked a good performance based on the MSE for all the sample images however for the RMSE it performs very well in Image2 and SSIM for Image3 and the PSNR is evident in Image2. Indeed, the application of the said filter depends on the quality of the given image basis for the application of suitable measurements to achieve optimal results.
Research and Application of Information Data Retrieval System in Station Based on Lucene Technology

With the continuous improvement of Internet technology, how to quickly and accurately find the target content in the Internet's massive big data has become the focus of current IT research. This paper first explained the function and the architecture of Lucene, and discussed the index technology and search technology of Lucene. Moreover, the basic architecture of a small-scale traffic illegal act retrieval system based on Lucene technology was analyzed in detail. This system was designed effectively from three aspects of crawling information, index database construction and data retrieval. The results will provide the application basis for better embedding Lucene toolkit into the station search engine.
Using Quantization-Aware Training Technique with Post-Training Fine-Tuning Quantization to Implement a MobileNet Hardware Accelerator

In recent years, the internet of things (IoT) has been developed near the public's life circle. At the edge device, for real-time data analysis of data, a lightweight deep learning neural network (DNN) is required. In this paper, the lightweight model MobileNet is used to design an energy efficiency hardware accelerator at the edge device. In the software framework (Tensorflow), the quantization-aware training technique with post-training fine-tuning quantization is applied to quantize the model to improve training convergence speed and parameter minimization. In hardware design considerations, fixed-point operations can reduce computational complexity and memory storage space as compared to floating-point operations, which directly affects the power consumption of the circuit. The proposed MobileNet hardware accelerator can achieve low power consumption and is suitable for the edge devices.
Double-color digital image watermarking technology based on double transform domain

The traditional binary image is no longer sufficient to bring richer information in modern production and life. Color images have more information data than binary images. Based on the development of color image models and digital watermarking technology, this paper uses the Arnold scrambling and DWT-DCT method improves the robustness and invisibility of double color digital image watermarking. The encryption method improves the security of the watermark itself to a certain extent.
Design, Control, and Simulation of a Neonatal Incubator

In this project, a fully functional incubator with precise control with respect to temperature, humidity, and airflow was developed and assessed. In parallel with the development of the incubator, a heuristic simulation was created to test and tune the Mamdani fuzzy logic controller. The controller was then applied to the incubator prototype.