Reviewerly/exHarmony huggingface dataset:
Complexity Issues in Data-Driven Fuzzy Inference Systems: Systematic Literature Review

The development of a data-driven fuzzy inference system (FIS) involves the automatic generation of membership functions and fuzzy if-then rules and choosing a particular defuzzification approach. The literature presents different techniques for automatic FIS development and highlights different challenges and issues of its automatic development because of its complexity. However, those complexity issues are not investigated sufficiently in a comprehensive way. Therefore, in this paper, we present a systematic literature review (SLR) of journal and conference papers on the topic of FIS complexity issues. We review 1 340 papers published between 1991 and 2019, systematize and classify them into categories according to the complexity issues. The results show that FIS complexity issues are classified as follows: computational complexity, fuzzy rules complexity, membership functions complexity, input data complexity, complexity of fuzzy rules interpretability, knowledge inferencing complexity and representation complexity, accuracy and interpretability complexity. The results of this study can help researchers and practitioners become familiar with existing FIS complexity issues, the extent of a particular complexity issue and to decide for future development.
ViTA: A flexible CAD-tool-independent automatic grading platform for two-dimensional CAD drawings

Grading engineering drawings takes a significant amount of an instructor’s time, especially in large classrooms. In many cases, teaching assistants help with grading, adding levels of inconsistency and unfairness. To help in grading automation of CAD drawings, this paper introduces a novel tool that can completely automate the grading process after students submit their work. The introduced tool, called Virtual Teaching Assistant (ViTA), is a CAD-tool-independent platform that can work with exported drawings originating from different CAD software having different export settings. Using computer vision techniques applied to exported images of the drawings, ViTA can not only recognize whether or not a two-dimensional (2 D) drawing is correct, but also offers the detection of many important orthographic and sectional view mistakes such as mistakes in structural features, outline, hatching, orientation, scale, line thickness, colors, and views. We show ViTA’s accuracy and its relevance in the automated grading of 2 D CAD drawings by evaluating it using 500 student drawings created with three different CAD software.
Optimal Trajectory Planning with Dynamic Obstacles Avoidance for Mobile Robots Navigation

This paper develops an optimal trajectory planning strategy for an omnidirectional mobile robot with dynamic obstacles avoidance. The proposed method uses the information of the obstacle’s movement to design a collision-free trajectory. This collision-free trajectory generation approach uses an optimal path planning that minimizes the energy cost needed to move the robot from an initial point to a final position. The optimal trajectory planning considers the robot’s model as a dynamic constraints in the optimization problem. Beyond it, constraints such as the physical saturation on torque and speed of the actuators are included as inequality constraints. The obstacles avoidance are included into the optimization problem to restrict the feasible path that the robot can take to achieve the target position. Additionally, a nonlinear feedback control strategy is used to guarantee the tracking trajectory. The trajectory generation technique and the control system are evaluated in a numerical simulation in a model of a Festo Robotino $${\textregistered }$$ platform. The numerical evaluation shown an effective trajectory planning with results into the feasible solutions.
Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks

This chapter concentrates on aspect-based sentiment analysis, a form of opinion mining where algorithms detect sentiments expressed about features of products, services, etc. We especially focus on novel approaches for aspect phrase extraction and classification trained on feature-rich datasets. Here, we present two new datasets, which we gathered from the linguistically rich domain of physician reviews, as other investigations have mainly concentrated on commercial reviews and social media reviews so far. To give readers a better understanding of the underlying datasets, we describe the annotation process and inter-annotator agreement in detail. In our research, we automatically assess implicit mentions or indications of specific aspects. To do this, we propose and utilize neural network models that perform the here-defined aspect phrase extraction and classification task, achieving F1-score values of about 80% and accuracy values of more than 90%. As we apply our models to a comparatively complex domain, we obtain promising results.
ROS-Based Pedestrian Detection and Distance Estimation Algorithm Using Stereo Vision, Leddar and CNN

Pedestrian detection systems are of paramount importance in today's cars, as we depart from conventional ADAS toward higher levels of integrated autonomous driving capability. Legacy systems use a variety of image processing methodologies to detect objects and people in front of the vehicle. However, more robust solutions have been proposed in recent years with the advent of better sensors (lidars, stereo cameras and radars) along with new deep learning algorithms. This paper details a ROS-based pedestrian detection and localization algorithm utilizing ZED stereo vision camera and Leddar M16, employing darknet YOLOv2 for localization, to yield faster and credible results in object detection. Distance data is obtained using a stereo camera point cloud and the Leddar M16, which is later fused using ANFIS. YOLOv2 was trained using the Caltech Pedestrian dataset on a NVIDIA 940MX GPU, with the sensor interfacing done via ROS.
When Technology Meets Finance

There is a paradigm shift in the financial services industry. Combined with ever-changing customer expectations and preferences, emerging technologies such as artificial intelligence (AI), machine learning, the internet of things (IoT), and blockchain are redefining how financial institutions deliver services. It is an enormous task to remain competitive in this ever-changing environment. Financial institutions see FinTech as a major part of the digital future, and as proof of this, since 2015, financial institutions have invested over US$ 27 billion in FinTech and digital innovation. This chapter is an introductory chapter that explores FinTech in the literature. It focuses on how FinTech is reshaping the financial industry by describing FinTech phases and development process. The financial products and services using FinTech are also described with a highlight on Islamic FinTech. The chapter finally concludes by describing the future of FinTech.
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression

Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems. Due to the high nonlinearity of problems in this area, data-driven approaches gain increasing attention for identifying nonparametric models. In order to increase the prediction performance of these models, abstract prior knowledge such as stability should be included in the learning approach. One of the key challenges is to ensure sufficient flexibility of the models, which is typically limited by the usage of parametric Lyapunov functions to guarantee stability. Therefore, we derive an approach to learn a nonparametric Lyapunov function based on Gaussian process regression from data. Furthermore, we learn a nonparametric Gaussian process state space model from the data and show that it is capable of reproducing observed data exactly. We prove that stabilization of the nominal model based on the nonparametric control Lyapunov function does not modify the behavior of the nominal model at training samples. The flexibility and efficiency of our approach is demonstrated on the benchmark problem of learning handwriting motions from a real world dataset, where our approach achieves almost exact reproduction of the training data.
An Enhanced Prospective Jaccard Similarity Measure (PJSM) to Calculate the User Similarity Score Set for E-Commerce Recommender System

E-commerce is an exemplary representation for online commercial transactions which permits business applications in dealing with different organizations. A business intelligent (BI) technique implicated in predicting the user’s buying preference in advance such as Flipkart and Amazon is said to be recommender system (RS). It builds a product–customer relationship in an efficient manner. Thus, collaborative filtering(CF) is the most promising approach followed by the E-commerce firm in generating product recommendations to the end user. Collaborative filtering approach searches for a “like - minder ” people with similar buying preferences. The conventional machine learning algorithms used to calculate the user similarity index (USI) value are identified as Pearson correlation coefficient (PCC), cosine (COS) and Jaccard similarity measure. These algorithmic similarity measures are not successful in several situations particularly in cold start and data sparsity problem. From the literature survey, it is inferred that data sparsity is the most important primary issue that needs to be resolved first in CFRS in order to generate a reliable product recommendation listing to the targeted user. So that a novel method called prospective Jaccard similarity measure (PJSM) was proposed. Its novelty is to overcome the data sparsity issues in CFRS method as well as the conventional algorithmic method limitations. MovieLense dataset is extracted and applied for an implementation. Research objective involves in showing rapid improvements in users’ rating prediction accuracy. Compare conventional algorithms results with the newly proposed PJSM algorithm results by finding the MAE and RMSE. Accordingly, the proposed PJSM algorithm results in showing superiority for the predicted rated data and reduces the error ratio as well. The PJSM algorithm is also effective in managing the 10 lakh data too.
Sedimentary and geochemical characteristics of Triassic new type of polyhalite potassium resources in Northeast Sichuan and its genetic study

Abstract Polyhalite has been discovered for years in the Triassic of the Sichuan Basin. However, it is difficult to exploit and utilize such polyhalite because of its deep burial depth and its coexistence with anhydrite or dolomite. Therefore, it has always been regarded as “dead ore”. Based on slice identification, X-powder diffraction, chemical analysis, REEs analysis and strontium isotope test on halite samples from the fourth and fifth member of Jialingjiang Formation to Leikoupo Formation of Wells ZK601 and ZK001 in Xuanhan area, Northeast Sichuan Basin, this paper discovers thick layers of granular polyhalite associated with halite and the polyhalite content accounts for 10–30%. These deep polyhalites can be obtained by water-soluble mining and utilized, so they are called “new type polyhalite potash deposits”. The deposit is deep buried at 3,000 m underground, and the thickness of a single layer can be more than 30 m. It is stable in regional distribution. The discovery of the “new type polyhalite potash deposits” has “activated” polyhalite, which has been considered as deep “dead ore” and has great significance for potash prospecting in China.
Semiparametric mixture cure model analysis with competing risks data: Application to vascular access thrombosis data

The article is motivated by a nephrology study in Taiwan, which enrolled hemodialysis patients who suffered from vascular access thrombosis. After treatment, some patients were cured of thrombosis, while some may experience recurrence of either type (acute or nonacute) of vascular access thrombosis. Our major interest is to estimate the cumulative incidence probability of time to the first recurrence of acute thrombosis after therapy. Since the occurrence of one type of vascular access thrombosis precludes occurrence of the other type, patients are subject to competing risks. To account for the presence of competing risks and cured patients, we develop a mixture model approach to the regression analysis of competing‐risks data with a cure fraction. We make inference about the effects of factors on both the cure rate and cumulative incidence function (CIF) for a failure of interest, which are separately specified in the logistic regression model and semiparametric regression model with time‐varying and time‐invariant effects. Based on two‐stage method, we develop novel estimation equations using the inverse probability censoring weight techniques. The asymptotic properties of the estimators are rigorously studied and the plug‐in variance estimators can be obtained for constructing interval estimators. We also propose a lack‐of‐fit test for assessing the adequacy of the proposed model and several tests for time‐varying effects. The simulation studies and vascular access thrombosis data analysis are conducted to illustrate the proposed method.
Multi-Scale Vehicle Detection and Tracking Method in Highway Scene

Based on tracking-by-detection strategy, this paper proposes a detection and tracking method for multi-scale vehicle targets. The method consists of object detection and trajectory correlation. Targeted at object detection, an improved SNIPER sampling strategy was proposed, which combined with Faster R-CNN deep learning model to achieve variable scale vehicle detection. For the trajectory correlation, motion features, apparent features and trajectory features are matched to achieve the matching between the detection target and the vehicle trajectory. The experimental results show that the method in this paper has made significant improvements over previous methods, both in accuracy and speed, and can detect and track multi-scale vehicle targets in highway scenes in real time.
A Survey on Graphical Authentication System Resisting Shoulder Surfing Attack

In today's modern world, security is a major concern. To provide security, the most widely recognized authentication methods are credentials, OTP, LTP, etc, and these methods are more prone to brute-force attack, shoulder surfing attack, and dictionary attack. Shoulder surfing attack (SSA) is a data theft approach used to obtain the personal identification numbers or passwords by looking over the user's shoulder or by external recording devices and video-capturing devices. Since SSA occurs in a benevolent way, it goes unnoticed most of the time. It is one of the simple and easy methods for hackers to steal one's sensitive information. The hacker has to simply peek in while the user types in the password without any much effort involved. Therefore, this phenomenon is widely unknown to people all over the world. Textual passwords are a ubiquitous part of digital age. Web applications/mobile applications demand a strong password with at least one capital letter and a special letter. People tend to give easy passwords in order to remember them which can be easily shoulder surfed. To overcome this, graphical password techniques are used to provide a more secure password. In the graphical authentication system, the users click on target images from a challenge set for authentication. Various graphical systems have been proposed over the years which are shown to be more secure when compared to other authentication systems. In this paper, an overview of various graphical authentication systems is presented.
A Study of Dimensionality Reduction Impact on an Approach to People Detection in Gigapixel Images

Digital images are found in several sizes and are easily displayed on a computer screen using techniques that can reduce their dimensions. Moreover, algorithms are used to process images to perform several tasks, for instance, detection of people. Recently, gigapixel images emerged, providing a huge amount of data; however, algorithms for people detection have been usually tested only on regular size images. This paper presents an impact analysis of the resolution reduction in the detection of people in gigapixel images. People detectors were trained with the INRIA and CALTECH data sets and results show that, although gigapixel images provide a huge false positive rate, the resolution reduction significantly decreases the number of bounding boxes and false positives, however, increasing the rate of missing people.
Multiple Layered Deep Learning Based Real-time Face Detection

This paper proposes an approach to real-time face detection working on the CPU. The challenge of the face detection system is the non-frontal face position and the use of accessories that cover the face area, even conventional detection systems that rely on facial features are difficult to get high accuracy. The proposed system can overcome these problems and work for multiple faces. The deep learning system can recognize facial features with complex backgrounds. The CNN (Convolution Neural Network) architecture with shallow layers to produce light computing then the system can work real-time. Multiple layer detection on the last feature map is used to detect varied face sizes. The system result shows sequential images of face localization. In the validation step, this system gets AP is 90.84. In the testing process using the CPU, this system shows it can work real-time on various types of hardwares.
Yaiao Application Collecting Log File RF 4G Android-Based Replacing Data Cables to Laptop on Traditional RF Drive Test Models

The number of network users is expected to continue to grow an average of 3% year-on-year, mainly because the market is developing. Drive test is the process of monitoring a 4G frequency radio network to monitor any changes that occur in the operation of network devices. RF drive test equipment itself is quite a lot and is quite expensive. In this research, is to make the function of the Yaiao application to accommodate 4G RF files that replace the data cable connected to the laptop. The drive test tool is simpler because it only uses an Android smartphone with G-Net Report Pro Software and uses stages by running the Yaiao application and measuring the signal using G-Net Report Pro Software based on the specified location and then the Yaiao software will detect log files of the results of a successful RF collected on the smartphone's internal storage and sends RF log files directly to the server. If sending fails, there is also an upload menu that works to send files manually. This application serves to replace the function of the cable by using the internet network and replace the measurement data storage using internal storage on smartphones and a success rate of 70%
Representative Image Selection for Data Efficient Word Spotting

This paper compares three different word image representations as base for label free sample selection for word spotting in historical handwritten documents. These representations are a temporal pyramid representation based on pixel counts, a graph based representation, and a pyramidal histogram of characters (PHOC) representation predicted by a PHOCNet trained on synthetic data. We show that the PHOC representation can help to reduce the amount of required training samples by up to 69% depending on the dataset, if it is learned iteratively in an active learning like fashion. While this works for larger datasets containing about $$1\,700$$ images, for smaller datasets with 100 images, we find that the temporal pyramid and the graph representation perform better.
A Benchmark System for Indian Language Text Recognition

The performance various academic and commercial text recognition solutions for many languages world-wide has been satisfactory. Many projects now use the ocr as a reliable module. As of now, Indian languages are far away from this state, which is unfortunate. Beyond many challenges due to script and language, this space is adversely affected by the scattered nature of research, lack of systematic evaluation, and poor resource dissemination. In this work, we aim to design and implement a web-based system that could indirectly address some of these aspects that hinder the development of ocr for Indian languages. We hope that such an attempt will help in (i) providing and establishing a consolidated view of state-of-the-art performances for character and word recognition at one place (ii) sharing resources and practices (iii) establishing standard benchmarks that clearly explain the capabilities and limitations of the recognition methods (iv) bringing research attempts from a wide variety of languages, scripts, and modalities into a common forum. We believe the proposed system will play a critical role in further promoting the research in the Indian language text recognition domain.
An Improved LR Algorithm for Image Deblurring

Due to the limitations of the device itself or other conditions, images captured by the camera are often no clear with incomplete data. In particular, the problem of blurred images plagues the users most. This paper, taking the de-blurring issue as the main focus, proposes a new assessment index for image quality; based on Lucy-Richardson (LR) algorithm, designs a method for self-adaptive image de-blurring. Through experimental verification and comparison with other methods, the method adopted in this paper achieves a better de-blurring effect.
Dynamic k-NN Classification Based on Region Homogeneity

The effectiveness of the k-NN classifier is highly dependent on the value of the parameter k that is chosen in advance and is fixed during classification. Different values are appropriate for different datasets and parameter tuning is usually inevitable. A dataset may include simultaneously well-separated and not well-separated classes as well as noise in certain regions of the metric space. Thus, a different k value should be employed depending on the region where the unclassified instance lies. The paper proposes a new algorithm with five heuristics for dynamic k determination. The heuristics are based on a fast clustering pre-processing procedure that builds an auxiliary data structure. The latter provides information about the region where the unclassified instance lies. The heuristics exploit the information and dynamically determine how many neighbours will be examined. The data structure construction and the heuristics do not involve any input parameters. The proposed heuristics are tested on several datasets. The experimental results illustrate that in many cases they can achieve higher classification accuracy than the k-NN classifier that uses the best tuned k value.
Temporal Logic Based Motion Planning with Infeasible LTL Specification

In this paper, we consider the problem of robot motion planning with the given LTL specification. Informally, the general algorithm to such problem operates in two phases: (i) the high level synthesis phase constructs an available discrete planning; (ii) the low level synthesis phase designs the control inputs of the robot associated with the transitions along the discrete planning. However, when phase (i) fails, that is, the LTL specification is infeasible in the current environment, it is desired that the system can provide a solution to such cases. In this paper, we utilize the relaxed product automaton to obtain a feasible specification that violates the original specification to a minimum extent and construct an implementable motion planning. The specification automaton is revised by adding the minimum number of transition relations that cannot be satisfied. The overall scheme is demonstrated by a case study.