Reviewerly/exHarmony huggingface dataset:
Precision Position Measurement of PMSLM Based on ApFFT and Temporal Sinusoidal Fringe Pattern Phase Retrieval

To improve the accuracy of the mover position detection with longer measurement range, a linear motor displacement measurement method is proposed based on digital image measurement and temporal sinusoidal fringe pattern phase retrieval. A simple computer-generated sinusoidal fringe pattern is designed as the target image, and sequences of fringe signals are recorded by a line scan camera that moves with the linear motor. Fourier phase analysis is employed to extract the initial phase of each signal curve, and then temporal phase unwrapping is applied to reconstruct the mover displacement evolution according to the system magnification parameter and fringe width. This method removes the phase ambiguity problem of traditional temporal phase analysis, and can be used in complex motion of permanent magnet synchronous linear motors. To suppress the spectrum leakage of fast Fourier transform (FFT), all-phase FFT with double Hanning windows is used to improve the detection accuracy of initial phase and fringe width. Simulation and experimental results demonstrate that the proposed method can estimate the mover position with longer measurement range precisely and robustly under different working conditions.
Dynamic phase measuring profilometry for rigid objects based on simulated annealing

This paper presents a dynamic phase measurement profilometry (PMP) method based on the simulated annealing algorithm. In dynamic PMP for rigid objects, pixel matching is an effective method to make one-to-one pixel correspondence in each captured pattern. However, pixel matching by the global traversing algorithm takes up most of the time in the whole reconstruction process. For the purpose of optimizing pixel matching and enhancing performance in dynamic PMP, the simulated annealing algorithm is introduced. By generating a random path based on the simulated annealing algorithm, it is sufficient to locate the approximate area of the measured object. Then the accurate position can be calculated by combining it with a partial traversing algorithm. The proposed method can reduce pixel matching time by 63% and increase reconstruction efficiency by 58%. Simulations and experiments prove feasibility and precision.
Parallel phase shifting radial shear interferometry with complex fringes and unknown phase shift

We present an interferometric method to analyze transparent samples using complex fringes generated by a parallel phase shifting radial shear interferometer using two coupled interferometers. Parallel interferograms are generated using two interferometers: the first one generates the polarized base pattern, and the second system is used to generate parallel interferograms allowing the adjustment of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>x</mml:mi> <mml:mo>−<!-- − --></mml:mo> <mml:mi>y</mml:mi> </mml:math> positions of the parallel interferograms. To obtain the optical phase map, parallel phase shift is generated by collocating polarizing filters at the output of the system; the polarizers are placed at arbitrary angles since they do not require adjustment because of the phase-recovery algorithm. The optical phase was processed using a two-step algorithm based on a modified Gram–Schmidt orthogonalization method. Such an algorithm has the advantage of not being iterative and is robust to amplitude modulation. The proposed method reduces the number of captures needed in phase-shifting interferometry. We applied the developed system to examine static and dynamics phase objects.
A Comparative Study of Vehicle Detection Methods in a Video Sequence

Vehicle detection plays a significant role in traffic monitoring. Vehicle detection approaches can be used for vehicle tracking, vehicle classification and traffic analysis. However, numerous attributes like shape, intensity, size, pose, illumination, shadows, occlusion, velocity of vehicles and environmental conditions, provide different challenges for the detection step. With an appropriate vehicle detection technique, we are able to extract valuable knowledge from video sequences, regardless these diverse factors. Since the vehicle detection method choice has a deep impact on this step and the whole traffic monitoring system performances, our objective in this study is to investigate different methods for vehicle detection. Comparison is made on the basis of different metrics such as recall, precision and detection accuracy. These approaches have been tested under different weather conditions (rainy, sunny) and various traffic conditions (light, medium, heavy).
Problem of Resistance to the Introduction of Distance Learning Models of Training in the Vocational Training of Educators

This study seeks to specify the factors preventing end-users (learners) from doing the distance learning courses successfully and university management from introducing this mode of study into vocational training of educators in Ukraine. It employed a non-experimental, descriptive study design performed through online and offline surveys. The preliminary data were collected through a self-completion Google Forms-based questionnaire (Course Satisfaction Questionnaire) for the students used at the first stage of our research followed by an interview questionnaire used with a focus group at the subsequent one. The latter developed the evaluation scale and made all necessary adjustments so that the validity of the study was ensured. The Chi-Square method was used to determine whether there were any correlations between internal and external factors of resistance. This study proved that the issue of resistance to the introduction of distance-learning models of training in the vocational training of educators is mostly a personal perception factor. It raised the issue of training and assisting lecturers in using distance education tools and shifting their role from being a source of knowledge to being a facilitator of learning. https://doi.org/10.26803/ijlter.19.2.1
An Automated Method for Selecting GoF Design Patterns

Nowadays, an increase in the growth of software systems has risen the importance of the design phase. So far, developers have introduced numerous software design patterns. This study presents a new method to select the Gang of Four (GoF) design patterns. The proposed method is implemented based on the vector space model (VSM). In this method, the Term Frequency-Inverse Document Frequency (TF-IDF) weighting algorithm has been improved to determine the similarity between two texts, more accurately. Also, we used a set of hyponyms and synonyms of the words in weighting. We evaluated the proposed method with 23 design patterns, 29 object-oriented related design problems, and nine real-world problems. Finally, we observed promising results compared to other methods. We found 8.5%, 1.2%, and 5.2% improvement in terms of precision, recall, and accuracy of the proposed method as compared to other methods.
An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation

Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen’s kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen’s kappa values were calculated.
A Blockchain-Based Architecture for Smart Healthcare System: A Case Study of Saudi Arabia

eHealth is the use of Information and Communication Technologies (ICT) to enhance health quality access.Health care innovations are an essential element of Vision 2030 and National Transformation Program (NTP) 2020 operational plan that will lead to an improvement in the quality of health care in Saudi Arabia.The objective of this paper is to address the healthcare system limitations in the Kingdom of Saudi Arabia in general and the Qassim region in particular to global standards.In practice, the creation of an infrastructure for storing and seamless sharing of health data between different entities is studied.Furthermore, an in-depth analysis of current practices w.r.t. the health data, as well as finding similarities between NTP 2020 and Health 2020 (European Union) has been performed.A multi-level blockchain eHealth system is proposed to provide seamless electronic health records to the patients.Moreover, the current practices being employed in Qassim province, KSA has been analyzed.It was found that private hospitals give access to medical reports to their patients besides allowing them to manage their appointments.On the other hand, access to the government hospitals' medical records is minimal.
DFKI Cabin Simulator: A Test Platform for Visual In-Cabin Monitoring Functions

We present a test platform for visual in-cabin scene analysis and occupant monitoring functions. The test platform is based on a driving simulator developed at the DFKI, consisting of a realistic in-cabin mock-up and a wide-angle projection system for a realistic driving experience. The platform has been equipped with a wide-angle 2D/3D camera system monitoring the entire interior of the vehicle mock-up of the simulator. It is also supplemented with a ground truth reference sensor system that allows to track and record the occupant’s body movements synchronously with the 2D and 3D video streams of the camera. Thus, the resulting test platform will serve as a basis to validate numerous incabin monitoring functions, which are important for the realization of novel human-vehicle interfaces, advanced driver assistant systems, and automated driving. Among the considered functions are occupant presence detection, size and 3D-pose estimation, and driver intention recognition. In addition, our platform will be the basis for the creation of large-scale in-cabin benchmark datasets.
Describing the Pearson 𝑅 distribution of aggregate data

Ecological studies and epidemiology need to use group averaged data to make inferences about individual patterns. However, using correlations based on averages to estimate correlations of individual scores is subject to an "ecological fallacy". The purpose of this article is to create distributions of Pearson R correlation values computed from grouped averaged or aggregate data using Monte Carlo simulations and random sampling. We show that, as the group size increases, the distributions can be approximated by a generalized hypergeometric distribution. The expectation of the constructed distribution slightly underestimates the individual Pearson R value, but the difference becomes smaller as the number of groups increases. The approximate normal distribution resulting from Fisher's transformation can be used to build confidence intervals to approximate the Pearson R value based on individual scores from the Pearson R value based on the aggregated scores.
A Stochastic Max Pooling Strategy for Convolutional Neural Network Trained by Noisy Samples

The deep convolutional neural network (CNN) has made remarkable progress in image classification. However, this network performs poorly and even cannot converge in many actual applications, where the training and test samples contain lots of noises. To solve the problems, this paper puts forward a network training strategy based on stochastic max pooling. Unlike the traditional max pooling, the proposed strategy first ranks all the values in each receptive field, and then selects a random value from the top-n values as the pooling result. Compared with common pooling methods, stochastic max pooling can limit the pooling selection to a larger value that represents the main information of the pooling area which reduces the chance of introducing noises into the network, and enhances the robustness of extracting noisy image features. Experimental results show that the CNN used stochastic max pooling Strategy can converge better than traditional CNN and classified noisy images much more accurately than traditional pooling methods.
AI—Assisted Chatbot for E-Commerce to Address Selection of Products from Multiple Products

Artificial Intelligence has been used nowadays increasingly to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more. Machine Learning is a subset of AI techniques which includes various algorithms that gives machines the ability to learn from data or while interacting with the world without being explicitly programmed. E-commerce websites are trending nowadays due to online shopping makes customer’s life easier. Similar to this, Chatter Robots i.e. Chatbots are providing better customer service through Internet. A chatbot is a computer or software program for simulating intelligent conversations with human using rules or artificial intelligence for machine. Users can interact with the chatbot program via conversational interface through written or spoken text as input. With the help of E-commerce website sellers can reach to larger audience and with the help of chatbots, sales can be increased by personal interaction with the users. Chatbots will welcome a user to conversation, guide to customer to make purchase which will reduce customer’s struggle. Chatbots will ask customers all the relevant questions to find the perfect fit, style, and color for them. Chatbots are the future of marketing and customer support. Chatbots are one such means of technology which helps humans in a lot of ways, by helping them in e-commerce to increase sales whilst providing great customer satisfaction.
Research and implementation of intelligent vehicle path planning based on four-layer neural network

In order to solve the problem of path planning and roadblock avoidance in the process of smart car walking, this paper proposes a path planning algorithm based on neural network to realize the planning of the collision path of smart car in unknown environment. Since the optimal path planning of the smart car is to automatically find a collision-free path from the initial state to the target state when it moves in an environment with obstacles. The algorithm is a neural network algorithm using a four-layer network structure and the energy function is designed as an evaluation function of the network. By looking for the extremum of the energy function, the AGV smart car adjusts the trolley according to the trend of the path point set. Move to complete the path planning task. MATLAB simulation can be used to verify the accuracy of the algorithm. The results of the obstacle avoidance planning algorithm, a smooth path and speed in the ideal state. It provides a certain reference for mobile robots, unmanned vehicles and other applications in cargo transportation, intelligent driving and military applications.
Named Entity Recognition: A Survey for Indian Languages

Named Entity Recognition (NER) is a tool based on principles of Artificial Intelligence (AI) and Natural Language Processing (NLP) for automatically tagging Named Entities from unstructured text. Named Entities, which are generally proper nouns, can be the name of a person, organization, location etc. Named Entity Recognition is a very crucial tool of Natural Language Processing. Some of the application areas of NER include- Information Extraction and Retrieval, Machine Translation, Text Summarization etc. This paper analyses different approaches used in the NER of Indian languages, with emphasis on Hindi. The study compares the different approaches for NER viz. Machine Learning (ML), Rule-based and Hybrid. This study is orchestrated to provide gap analysis in available NER systems for Indian Languages, especially Hindi. It is because existing NER systems are evaluated for accuracies based on predefined datasets, which are not providing universal results on any dataset. Also, there is no standard dataset available for fair comparison of accuracies of Indian Languages. Furthermore, we concentrated our scope to Hindi as it is the official language of India and representative to other Indo-Aryan languages having similar structure, making it a generalized solution. Moreover, the scope of Named Entity tags are limited. Whereas, there is a scope of further classification of Named Entities. As future work, we would like to develop an efficient system, in terms of accuracy, and catering to more Named Entity tags than available presently in Hindi NER tools.
A formal model for proving hardware timing properties and identifying timing channels

Timing channels are becoming a critical threat to hardware security. When exploited, secret information can be revealed by analyzing the execution time statistically. There are a variety of methods for detecting timing channels such as statistical analysis, testing and formal verification. However, existing methods cannot guarantee that the timing channels can be identified due to limited test coverage or high performance overhead. In this work, we introduce a novel model for evaluating timing variations of the hardware design. Furthermore, we propose a systematical solution that integrates time label enhanced tracking logic and formally verifies the timing invariant property of hardware designs in order to identify hardware timing channels. We demonstrate our solution on several hardware implementations, including arithmetic units, cryptographic cores and cache. The proof results show that our solution can detect hardware timing channels effectively.
A Proactive Approach to Resistant UAV Mission Planning

Proactive mission planning for a fleet of Unmanned Aerial Vehicles (UAVs) can be seen as a sequence of routing problems mathematically formalized as 0-1 knapsack problems. Taking into account the fact that weather conditions change during a mission, the time horizon of the planned mission is subdivided into time windows corresponding to periods of stable weather. Also, keeping in mind fuel constraints, each knapsack problem is formulated as follows: Given is the fleet size and a set of spatially dispersed target points specified by the volume of expected deliveries and the coordinates of their location, which allow to determine the amount of fuel consumed during flight along a particular route segment/from one drop-off point to another. Determine a subset of locations so that the total fuel required to cover the total distance traveled by the UAV fleet is less than or equal to the given limit e.g. determined by battery capacity, and the total volume of deliveries is as large as possible. In this context, policies aimed at minimizing the total travel time and/or the total distance traveled are considered. Some potential directions of future research on resistant UAV mission planning are discussed.
Semi-Automatic Dataset Generation for Object Detection and Recognition and its Evaluation on Domestic Service Robots

This paper proposes a method for the semi-automatic generation of a dataset for deep neural networks to perform end-to-end object detection and classification from images, which is expected to be applied to domestic service robots. In the proposed method, the background image of the floor or furniture is first captured. Subsequently, objects are captured from various viewpoints. Then, the background image and the object images are composited by the system (software) to generate images of the virtual scenes expected to be encountered by the robot. At this point, the annotation files, which will be used as teaching signals by the deep neural network, are automatically generated, as the region and category of the object composited with the background image are known. This reduces the human workload for dataset generation. Experiment results showed that the proposed method reduced the time taken to generate a data unit from 167 s, when performed manually, to 0.58 s, i.e., by a factor of approximately 1/287. The dataset generated using the proposed method was used to train a deep neural network, which was then applied to a domestic service robot for evaluation. The robot was entered into the World Robot Challenge, in which, out of ten trials, it succeeded in touching the target object eight times and grasping it four times.
Efficient high capacity technique to embed EPR information and to detect tampering in medical images

Reversible data hiding (RDH) is one of the well-known and highly recommended method to enhance medical data security or electronic patient record (EPR), privacy and truthfulness. In clinical images, while hiding the data, care must be taken to maintain the confidentiality of the information that is to be hidden within the clinical images because the hidden information should not bring down the original quality of the image. Mostly, the investigations carried out for medical diagnosis are usually constructed on the basis of investigation systems used for diagnosis and are grounded on the images usually used for medical study. Magnetic resonance image (MRI), CR and CT, digital format images which are acquired are related to the patient’s data and information regarding their diagnosis study. In this presented approach, we introduce an RDH scheme that can also be combined with encryption and reversible watermarking techniques. Here, we use the data related to the patient diagnosis as the secret information to embed in medical images. Quality of the marked image after embedding the data is usually measured with metrics that are usually used in image processing such as PSNR and SSIM.
Image Retargetability

Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions while preserving its visually and semantically important content. However, not all images can be equally processed. This study introduces the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability, in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates the joint learning of meaningful photographic attributes and image content information, which can facilitate the regularization of the complicated retargetability rating problem. To train and analyze this model, we collect a dataset that contains retargetability scores and meaningful image attributes assigned by six expert raters. The experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show the applications of image retargetability in retargeting method selection, retargeting method assessment and generating a photo collage.