Reviewerly/exHarmony huggingface dataset:
Interpretation, Modeling, and Visualization of Crowdsourced Road Condition Data

Nowadays almost everyone has a mobile phone and even the most basic smartphones often come embedded with a variety of sensors. These sensors, in combination with a large user base, offer huge potential in the realization of crowdsourcing applications. The crowdsourcing aspect is of interest especially in situations where users’ everyday actions can generate data usable in more complex scenarios. The research goal in this paper is to introduce a combination of models for data gathering and analysis of the gathered data, enabling effective data processing of large data sets. Both models are applied and tested in the developed prototype system. In addition, the paper presents the test setup and results of the study, including a description of the web user interface used to illustrate road condition data. The data were collected by a group of users driving on roads in western Finland. Finally, it provides a discussion on the challenges faced in the implementation of the prototype system and a look at the problems related to the analysis of the collected data. In general, the collected data were discovered to be more useful in the assessment of the overall condition of roads, and less useful for finding specific problematic spots on roads, such as potholes.
The Solution of the Problem of Unknown Words Under Neural Machine Translation of the Kazakh Language

The paper proposes a solution to the problem of unknown words for neural machine translation. The proposed solution is shown by the example of a neural machine translation of a Kazakh-English language pair. The novelty of the proposed technology for solving the problem of unknown words in the neural machine translation of the Kazakh language is the proposed algorithm for searching of unknown words in the vocabulary of a trained model of neural machine translation using the dictionary of synonyms of the Kazakh language. A dictionary of synonyms is used to search for words that are similar in meaning to the unknown words, which was defined. Moreover, the found synonyms are checked for the presence in the vocabulary of a trained model of neural machine translation. After that, a new translation of the edited sentence of the source language is performed. The base of words-synonyms of the Kazakh language is collected. The total number of synonymous words collected is 1995. Software solutions to the unknown word problem have been developed in the python programming language. The proposed technology solution to the problem of unknown words for neural machine translation was tested on the two source parallel Kazakh-English corpus (KAZNU Kazakh-English parallel corpus and WMT19 Kazakh-English parallel corpus) in both variants: baseline and with using of the proposed technology.
Decision tree matrix algorithm for detecting contextual faults in unmanned aerial vehicles

The importance of detecting faults in Unmanned Aerial Vehicles motivated researchers to work in this area over recent years. Complex relationships among UAV attributes (Sensor readings, and Commands) make the task a bit challenging. Many known algorithms consider detecting the faults by spotting da ta anomalies in the values of each attribute without concern for their context, which leaves an opportunity for potential improvement. The contextual faults occur when a defected sensor shows an invalid value concerning other attributes. Our contribution is a novel matrix platform for detecting the potential contextual faults. This platform consists of multiple small Decision Trees, instead of using one huge single Decision Tree, which could be difficult and time-consuming to produce, particularly in the case of a large dataset with too many attributes. We propose to use the C4.5 decision tree algorithm to build each decision tree. The Decision Tree is a machine learning technique, which is an effective supervised method used for classification. It is computationally inexpensive and capable of dealing with noisy data. Besides, our approach uses a sliding window technique during training and testing phases, which brings into consideration the effect of the previous state of the system on the process of detecting the contextual faults. The algorithm starts by collecting the attributes of the UAV into a table of pairs, where each pair consists of two attributes; then, it defines the Decision Tree matrix by assigning one Decision Tree for each pair of attributes. The Training step includes constructing training sub-datasets using the values of sliding windows. The C4.5 algorithm uses each constructed training sub-dataset to induce one Decision Tree in the matrix. Finally, the testing step is responsible for reading the values of the sliding windows and using the concerned Decision Tree to detect the contextual faults. We evaluated our approach using Detection Rate, False Alarm Rate, Precision, and F1-score indicators. Moreover, we made a comparison with other broadly used algorithms, such as K-Means and One-Class SVM. Our approach showed superior results in detecting different types of faults (sensor-offset, sensor-stuck, sensor-drift, and sensor-cut). The DT-Matrix performance was neither affected by the small values of the outliers, nor by the number of the outliers, and this caused the DT-Matrix to work better in most of the experiments compared to the other algorithms.
Fractional Order Savitzky-Golay Differentiator based Approach for Mammogram Enhancement

Mammogram enhancement plays vital role in detection of abnormality present in low contrast mammogram images. This paper explores a new application of Fractional Order Savitzky-Golay(SG) Differentiator for mammogram enhancement. It encompasses a new approach for low contrast mammogram image enhancement based on the concept of convolution. The enhancement is performed by three different test cases. The performance of proposed approaches is validated with quantitative as well as visual results. The result shows that the proposed algorithm produces better results. The effect of size of differentiator and order of derivative has also been analyzed.
Detection and Control System for Automotive Products Applications by Artificial Vision Using Deep Learning

Object recognition is among the most important subjects in computer vision, it has undergone a huge evolution during these last decades, but in the last years artificial intelligence has seen the appearance of Deep Learning, and through the efforts of researchers, Deep Learning is having great success, its applications have touched on different fields, such as robotics, industry, automotive … In this context, in collaboration with an Automotive components manufacturer and FST faculty of sciences and technologies of tangier (UAE University) have taken the initiative to develop and implement an object recognition and inspection system for automotive products application that requires a good accuracy of image classification using the Deep Learning which is the purpose of this paper. This report summarizes the work done within this Company concerning the development and implementation of a system aims to realize an artificial vision system for the inspection of automotive products based mainly on the “Deep Learning” method. Before, during and after manufacturing, many products in automotive sector (electronic components, …) are subjected to a visual inspection phase, in this context we have replace this phase by our vision system so that the piece will be accepted or not accepted, as well as to act to parameters (for example: winding shape, welding quality …) in the case of not accepted. The convolutional neural networks have become advanced methods for classification and detection of objects over the last five years. At present, they work better than conventional image processing method set, on many image classification data sets. Most of these datasets are based on the notion of concrete classes. In this paper, we present a new set of image classification data as well as object detection data, which should be easy for humans to solve, but its variations are difficult for CNN. The classification performance of popular CNN architectures is evaluated on this dataset and variations of this dataset may be of interest for future research.
Provisioning Input and Output Data Rates in Data Processing Frameworks

Abstract This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.
Cryptocurrency Price Prediction Based on Historical Data and Social Media Sentiment Analysis

The cryptocurrency space is highly volatile, and predictive systems working in this space are still in their infancy phase. The findings made during an extensive literature survey suggest the lack of a balanced approach and the right combination of data sources, which lead to biased feature sets and discriminative results. These have an impact on the accuracy of the models and skew the classification and prediction results. In this paper, we explore a better approach where a combination of sentiment analysis of social media content, contemporary pricing and market volume data is considered to extract a refined feature set. The features extracted from the preprocessing pipeline will then be used to classify and predict future pricing using a neural network model.
Towards visual debugging for multi-target time series classification

Multi-target classification of multivariate time series data poses a challenge in many real-world applications (e.g., predictive maintenance). Machine learning methods, such as random forests and neural networks, support training these classifiers. However, the debugging and analysis of possible misclassifications remain challenging due to the often complex relations between targets, classes, and the multivariate time series data. We propose a model-agnostic visual debugging workflow for multi-target time series classification that enables the examination of relations between targets, partially correct predictions, potential confusions, and the classified time series data. The workflow, as well as the prototype, aims to foster an in-depth analysis of multi-target classification results to identify potential causes of mispredictions visually. We demonstrate the usefulness of the workflow in the field of predictive maintenance in a usage scenario to show how users can iteratively explore and identify critical classes, as well as, relationships between targets.
An Improved Scan Matching Method Based on Laser Reflection Intensity

High-precision localization performance of mobile robots in featureless environments plays an important role in the field of navigation. This paper describes an effective localization solution using a landmark whose surface is covered with two different materials resulting in different laser reflection intensities. Based on the landmark, a feature extraction algorithm is proposed and an improved scan matching method for mobile robot localization in featureless environments is described. Unlike traditional scan matching methods which only use geometric features, the proposed method mixes geometric information extracted from environment and laser reflection intensity extracted from the landmark to achieve accurate pose estimation. Experiments show that our method has higher precision than previous methods.
Fastconv: Fast Learning Based Adaptive BitRate Algorithm for Video Streaming

For video streaming, Adaptive BitRate (ABR) algorithms are usually used to improve end-to-end userâ€™s Quality of Experience (QoE). Many of the state-of-the-art ABR algorithms are based on simplified models, leading to conservative predictions of real situations. To optimize the QoE in complex end-to-end transmission environments, ABR algorithms based on Deep Reinforcement Learning (DRL) has shown a great improvement compared to traditional algorithms. However, the slow convergence of existing DRL-based ABR algorithms limits the QoE performance under dynamic video streaming environments. In this paper, we propose Fastconv, a novel DRL-based ABR algorithm that has a fast convergence speed to ensure a satisfactory QoE performance. Our work can be mainly divided into two parts. First, we preprocess the input data with large fluctuation in order to obtain the steady input and reduce the indeterminacy of convergence. Second, in order to reduce the structural complexity of the neural network itself and the number of parameters, we propose a neural network architecture based on multiplexed convolution kernel. Experiment results based on a real traced mobile dataset have demonstrated that Fastconv outperforms both the traditional and DRL-based ABR algorithms in terms of QoE.
Currency Recognition Based on Deep Feature Selection and Classification

Advanced technology has played an important role in the circulation of the banknote counterfeit and currency value recognition. This study proposes an approach for the currency recognition based on the fundamental image processing and deep learning for the extraction characteristics and recognition of currency values. The large capacity of traditional techniques was proposed for currency recognition based on infrared spectrometer and chemometrics using special devices. This paper presents a recognition method to detect face values from currency paper and. The proposed method can recognize some kinds of currency values ​​and national currencies. The study investigated and proposed the deep neural network, which reaches appropriate accuracy rate and reduces consumption time. In order to improve accuracy of recognition model, data augmentation techniques are also investigated for training data preprocessing. The experimental results show that the proposed approach is applicable to the practical applications.
Diversity Maintenance for Efficient Robot Path Planning

Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.
Recognizing Compressed Videos: Challenges and Promises

This paper studies the effect of quality degradation, caused by lossy video compression, on video recognition. We investigate how the state of the art video enhancement restores the video quality needed for an effective video recognition. Furthermore, we study the impact of various enhancement objectives, namely pixel-level, feature-level, and adversarial, on action recognition performance. Our experiments demonstrate that the models trained on pixel-level loss perform well in terms of visual quality but they hurt the accuracy of action recognition due to over smoothing discriminative features. On the other hand, models trained on perceptual and adversarial loss types not only generate better perceptual quality but also further improve the action recognition performance.
Data‐driven benchmarking in software development effort estimation: The few define the bulk

Abstract Context The rapid evolvement of software development effort estimation models created the need for empirical evaluation of their quality. The empirical evaluation is based either on hypothesis tests with respect to a single criterion or on aggregating methods for multiple criteria. However, a model can be considered as a multidimensional entity performing differently on alternative datasets and its performance can be divergent when expressed by alternative criteria. Objective In this study, we explore this multidimensional nature of models by considering them as points in two different spaces (domain and criteria spaces). Method Introducing an alternative approach for data‐driven benchmarking, a new framework based on archetypal analysis is proposed for evaluation purposes of multiple models. Results The benefits of the framework are illustrated through a large‐scale experimental setup on a set of 93 effort estimation models, trained and tested on 10 datasets under 8 criteria providing answers to critical research questions. Conclusion The results indicate that a small minority of reference models is enough to define the performance of the bulk of all models. The framework focuses on models that have behavior close to archetypes and especially those that are close to a “best” archetype.
Research on Augmented Reality Method Based on Improved ORB Algorithm

Abstract Aiming at the problems of time-consuming three-dimensional registration algorithm in augmented reality system, low accuracy of feature point matching and poor registration effect, an improved ORB (S-ORB) algorithm is proposed and applied to the Majiayao painted pottery augmented reality system. Firstly, the scale space is established by Gaussian filtering and downsampling, and the feature points are extracted by FAST corner detection. Secondly, the descriptors of feature points are calculated by rBFEF algorithm. Finally, the feature matching set is obtained by hamming distance and the mismatching point is eliminated by RANSAC algorithm to complete the three-dimensional registration. Make the virtual information stack correctly added to the real scene. The experimental results show that the S-ORB algorithm is better than the ORB, SURF and BRISK algorithms in time and accuracy when the target position is rotated and scaled. It has better real-time and robustness in the AR system of the painted pottery.
Combining highlight removal and low‐light image enhancement technique for HDR‐like image generation

Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of the dynamic range of conventional image sensors. Low-light and highlight phenomena limit colour richness and visibility of objects in an image. Therefore, it can cause a reduction in the quality of images and a loss in accuracy in the application of image recognition. To overcome this, high dynamic range (HDR)-like images have been developed with rich colours such as those seen by the human eye. In this study, the authors propose a method to obtain an HDR-like image from a single LDR image by removing the specular component from highlight pixels as well as strengthening the actual colour. Next, they select low-light image enhancement via illumination map estimation as a low-light enhancement technique by showing the comparison with gamma-based expansion operator. They evaluate their HDR-like output images with non-reference and full-reference metrics. They show the comparison of their proposed method with six other methods. Besides, visually, their proposed method delivers more pleasing output than the output of other competitive methods.
Facial Recognition Experiments on a Robotic System Using One-Shot Learning

One of the crucial tasks during the interaction human-robot is the face recognition task on digital images. Firstly, the task of recognition requires face detection and a continuing procedure for extracting face characteristics and dealing with the brightness of the environment's faces positioned at different angles and sometimes occlusion problems. In this paper, the aim is to explore the One-shot learning technique, which considers only one image of each person for face detection and uses information extracted from other image databases for this. One of its modifications, Face Recognition algorithm, is applied to recognize people during sessions of interaction with a humanoid robot. This algorithm uses the Dlib capabilities to detect, extract and recognize faces through bases with a singular face image. The results of the experiments performed are presented and show how useful is this kind of learning technique for interactions of a robot with humans.
An Area-Power Efficient Denoising Hardware Architecture for Real EOG Signal

This paper presents an optimized noise reduction hardware architecture for real electrooculogram (EOG) system. The proposed denoise architecture is developed using differential evolution (DE) algorithm. The algorithm design the filter with fewer sign-power-of-two (SPT) terms to optimize the denoise filter hardware with desired frequency response. The proposed denoise filter architecture with a DE coefficient set uses shift and add approach and is implemented in gate-level Verilog HDL. The real EOG denoise filter’s functionality is checked with Altera DSP Builder and synthesized using Cadence RTL compiler. Both FPGA and ASIC synthesis results are compared with the recently published works. The area and power consumption results show that the proposed filter occupies less area and with low power consumption as compared to the existing architectures.
Cognitive Approach to Modeling Human-Computer Interaction with a Distributed Intellectual Information Environment

The paper proposes an approach to modelling human interaction with the distributed intellectual information environment, using the system of “power-society” as an example. The approach involves the consideration of the human factor in modelling of information interaction. The human factor in this article is considered simplified, as a set of cognitive characteristics of the user. Within the framework of the developed approach, it is proposed, in the process of human-computer interaction, to implicitly test users in order to obtain information about the cognitive styles of each specific user, the effectiveness and efficiency of human-computer interaction, and the degree of subjective satisfaction from the human-computer interaction. Since the information system “power-society” will operate on a national scale, the processing of collected data is supposed to be carried out using the technology of “big data”. The proposed approach allows us to take into account the influence of a person's cognitive equalities on indicators of his interaction with the information systems that implement a set of public services on the Internet.