Reviewerly/exHarmony huggingface dataset:
Simultaneous 3D surface profile and pressure measurement using phase-shift profilometry and pressure-sensitive paint

A simultaneous three-dimensional (3D) surface profile and pressure measurement method that integrates phase-shift profilometry and pressure-sensitive paint (PSP2) is proposed. The advantages of this novel technique over previous 3D pressure-sensitive paint (3D-PSP) techniques include a simplified system with low cost, no interference with PSP coatings, high spatial resolution, and high accuracy. A modified digital light-processing (DLP) projector-structured light generator is used to encode ultraviolet light and generate fringe projection to excite the pressure-sensitive paint. The 3D profile is reconstructed using four phase-shifting emission images. Meanwhile, the surface intensity ratio distribution is obtained. The PSP2 method is applied to a nitrogen jet impingement experiment onto a spherical model. The intensity ratio results obtained using the PSP2 method differ little from the conventional PSP results obtained using uniform excitation. The phase distortion due to the emission intensity fluctuation leads to errors in surface profile measurement, and the fringe projection with high contrast improves surface profile measurement accuracy. In most of the final results, the average total errors between the reconstructed 3D surface and the CAD geometry are less than 0.1 mm.
Health Recommender System for Cervical Cancer Prognosis in Women

The large amount of digital data of patients are available for health domain to be used successfully for extracting information and aid disease prediction. Therefore, the available digital information for patient-oriented decision-making substantially expanded. Recommender systems may offer more laymen-friendly knowledge to patients in this sense, helping to better understand their clinical status as reflected by their reports. Health Recommender Systems (HRSs) are a viable solution when it comes to offering resources to support clinicians in the diagnosis of illnesses, as well as supporting people with guidance on how to preserve their health. This study has proposed a Health Recommender System using a feature selection method based on the Multi Objective Genetic Algorithm (MOGA) to help women by providing information on the features responsible for prognosis of Cervical Cancer in women. It also recommends some prediction models for Cervical Cancer prediction with high accuracy. It has used Cervical Cancer Risk Classification dataset for implementation and accuracy as the evaluation parameter.
MapFusion: A General Framework for 3D Object Detection with HDMaps

3D object detection is a key perception component in autonomous driving. Most recent approaches are based on LiDAR sensors only or fused with cameras. Maps (e.g., High Definition Maps), a basic infrastructure for intelligent vehicles, however, have not been well exploited for boosting object detection tasks. In this paper, we propose a simple but effective framework - MapFusion to integrate the map information into modern 3D object detector pipelines. In particular, we design a FeatureAgg module for HD Map feature extraction and fusion, and a MapSeg module as an auxiliary segmentation head for the detection backbone. Our proposed MapFusion is detector independent and can be easily integrated into different detectors. The experimental results of three different baselines on large public autonomous driving dataset demonstrate the superiority of the proposed framework. By fusing the map information, we can achieve 1.27 to 2.79 points improvements for mean Average Precision (mAP) on three strong 3D object detection baselines.
Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics

Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance.
Efficient Post-quantum Identity-based Encryption with Equality Test

Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.'s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.
Membership-Function-Dependent Control Design and Stability Analysis of Interval Type-2 Sampled-Data Fuzzy-Model-Based Control System

This article investigates the design and stability analysis of the interval type-2 (IT2) sampled-data (SD) fuzzy-model-based (FMB) control system with the optimal guaranteed cost performance. An IT2 Takagi–Sugeno (T-S) fuzzy model is applied to describe the dynamics of the nonlinear systems where the parameter uncertainties are captured by the lower and upper membership functions. To conduct the stability analysis for the SD FMB control system, a looped-functional approach taking the advantage of the information about the sampling periods is employed. Because of the SD control strategy, the state will be sampled at each sampling instant and the control signal generated by the IT2SD fuzzy controller will be kept by the zero-order holder during the sampling period, which will result in mismatched membership grades between IT2 T-S fuzzy model and IT2SD fuzzy controller that leads to the complexity in carrying out stability analysis. Thanks to the imperfect premise matching concept, which allows the difference on the number of rules and the premise membership functions between model and controller, the design of the IT2SD fuzzy controller can be more flexible. To further relax the stability conditions and minimize the upper bound of the guaranteed cost index, the membership-function-dependent stability analysis approach which can make use of the features of the IT2 membership functions is adopted. The performance of the control system can also be adjusted through the choice of the weighting matrices in the cost function. The stability conditions building on the Lyapunov stability theory and the performance conditions building on the concept of the guaranteed cost control in the shape of linear matrix inequalities are established to assure the system stability and acquire the optimal guaranteed cost performance. The proposed IT2SDFMB control design is tested on the inverted pendulum system and the simulation results verify the effectiveness of the proposed approach.
Spectral Analysis of Electroencephalographic Data in Serious Games

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.
Hand Gesture Recognition for Bangla Sign Language Using Deep Convolution Neural Network

Around the world, deaf and dumb people are sufferers of all kinds of activities due to a lack of proper sign language interpreters. Our research paper proposes a new hand gesture recognition framework toward Bangla sign language to eliminate the significant communication gap between deaf and non-sign language users. The hand was detected practicing HSV and YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> color space. In total thirty-seven (37) characters (8 vowels and 29 consonants) are recognized by deep convolution neural networks. We take 37 classes for 37 alphabets from Bangla sign language. Our framework also aided to gesture recognition system by a new dataset for the Bangla sign language. Our dataset consists of 3219 images from six different people. This new dataset facilitates us to gain an accuracy of 99.22%.
Vehicle-mounted far-infrared pedestrian detection using multi-object tracking

Deep learning-based object detection has achieved great success on visible light images. However, it still faces various challenges in vehicle-mounted far-infrared (FIR) pedestrian detection, such as excessive false positives and false negatives due to the low contrast and small intra-class difference in FIR images, which hinder it applying to real-world. In this work, we propose a novel framework applying multi-object tracking (MOT) for detecting. Specifically, Faster R-CNN is used to obtain detected objects and multiple KCF trackers is used to track every object trajectory. Hungarian algorithm is used for bounding box matching to get historical information of trajectories to improve detection performance. To avoid KCF drift, we filter results with tracking confidence in KCF and only use the tracking bounding boxes with high confidence for matching. Besides, the KCF tracker only continues tracking when a trajectory obtains high detection score in the latest frame. Considering small intra-class difference in FIR images, IOU is combined with moving direction information for Hungarian matching. Finally, we propose a SCUT-MOT dataset to verify our method. Compared with Faster R-CNN and SORT on this dataset, our method achieves 8.9% and 2.4% precision improvement respectively.
Compare and contrast: Detecting mammographic soft-tissue lesions with C<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup></mml:math>-Net

Detecting breast soft-tissue lesions including masses, structural distortions and asymmetries is of great importance due to the high risk leading to breast cancer. Most existing deep learning based approaches detect lesions with only unilateral images. However, multi-view mammogram images provide highly related and complementary information which helps to make the clinical analysis more comprehensive and reliable. In this paper, we propose a multi-view network for breast soft-tissue lesion detection called C2-Net (Compare and Contrast, C2) that fuses information across different views. The proposed model contains the following three modules. The spatial context enhancing (SCE) module compares ipsilateral views and extracts complementary features to model lesion inherent 3D structure. The multi-scale kernel pooling (MKP) module contrasts contralateral views with added misalignment tolerance. Finally, the logic guided fusion (LGF) module fuses multi-view features by enhancing logic modeling capacity. Experimental results on both the public DDSM dataset and the in-house multi-center dataset demonstrate that the proposed method has achieved state-of-the-art performance.
An Approach to Better Portable Graphics (BPG) Compression with Providing a Desired Quality

Image compression is getting more importance for numerous applications like high resolution cameras, computer vision devices, portable instruments, remote sensing imagers, etc. To be useful for practice, an image compression method has to meet several requirements including high compression rate and visual quality, speed of compression and decompression, etc. A novel perspective approach called Better Portable Graphics (BPG) has been proposed recently. Although it aims at providing a desired visual quality using a parameter Q, accuracy of producing this quality is still worth improving. An approach proposed in this paper employs a two-step procedure based on averaged characteristics of the BPG image compression encoder. Experiments have been carried out for a set of different grayscale images. The experimental results show that the proposed approach is able to quickly and efficiently provide a desired visual quality of BPG compressed images with considerably improved accuracy.
Transformer-based Empathetic Response Generation Using Dialogue Situation and Advanced-Level Definition of Empathy

This study proposes an approach to transformer-based empathetic response generation using dialogue situation and advanced-level definition of empathy. First, SBERT is adopted to extract the dialog situation vector from the user's historical sentences. A BERT-based emotion detector, a topic detector and an information estimator are constructed for empathy-related feature extraction. The change of the emotional valance and the textual information gain, obtained from the emotion detector and the information estimator, are used for adversarial training of the transformer-based empathetic response generator. The loss function of the transformer is defined to measure how good the expected response in terms of fluency and empathy. The EmpatheticDialogues corpus was adopted for system training and evaluation on empathetic response generation. According to the experimental results, the BLEU score was increased to 7.821 after considering the dialogue situation feature and empathy definition, outperforming the comparison models. In terms of human subjective evaluation, three evaluation results of empathy, relevance and fluency for the proposed system are better than that for the baseline model.
A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images

Braille-assistive technologies have helped blind people to write, read, learn, and communicate with sighted individuals for many years.These technologies enable blind people to engage with society and help break down communication barriers in their lives.The Optical Braille Recognition (OBR) system is one example of these technologies.It plays an important role in facilitating communication between sighted and blind people and assists sighted individuals in the reading and understanding of the documents of Braille cells.However, a clear gap exists in current OBR systems regarding asymmetric multilingual conversion of Braille documents.Few systems allow sighted people to read and understand Braille documents for self-learning applications.In this study, we propose a deep learning-based approach to convert Braille images into multilingual texts.This is achieved through a set of effective steps that start with image acquisition and preprocessing and end with a Braille multilingual mapping step.We develop a deep convolutional neural network (DCNN) model that takes its inputs from the second step of the approach for recognizing Braille cells.Several experiments are conducted on two datasets of Braille images to evaluate the performance of the DCNN model.The rst dataset contains 1,404 labeled images of 27 Braille symbols representing the alphabet characters.The second dataset consists of 5,420 labeled images of 37 Braille symbols that represent alphabet characters, numbers, and punctuation.The proposed model achieved a classi cation accuracy of 99.28% on the test set of the rst dataset and 98.99% on the test set of the second dataset.These results con rm the applicability of the DCNN model used in our proposed approach for multilingual Braille conversion in communicating with sighted people.
Arabic Text Generation: Deep Learning for Poetry Synthesis

Text Generation, especially poetry synthesis, is a promising and challenging AI task. We have used LSTM and word2vec methods to explore this area. We do forward and backward word training with different word sequences lengths. Two Datasets of Arabic poems were used. Preprocessing for unification and cleaning was done too, but the Data size was big and required very high memory and processing, so we used a sub-Datasets for training; this affected our experiments since the model is trained on fewer data. A user-supplied keyword was implemented. We have found the shorter training sequence models were better in generating more meaningful text, and longer models prefer most frequent words, repeat text, and use small words. Best predicted sentences were selected by measuring each of its words conditional probability and multiply them; this avoids local maxima if we used a greedy method that chooses the best next-word only. Moreover, the AraVecword2vec module was not very helpful since it was provided synonyms much more that related words. Many enhancements can be done in the future, such as Arabic prosody constraints, and overcome the hardware issue.
Multi-Resolution Face Recognition with Drones

Smart cameras have recently seen a large diffusion and represent a low-cost solution for improving public security in many scenarios. Moreover, they are light enough to be lifted by a drone. Face recognition enabled by drones equipped with smart cameras has already been reported in the literature. However, the use of the drone generally imposes tighter constraints than other facial recognition scenarios. First, weather conditions, such as the presence of wind, pose a severe limit on image stability. Moreover, the distance the drones fly is typically much high than fixed ground cameras, which inevitably translates into a degraded resolution of the face images. Furthermore, the drones’ operational altitudes usually require the use of optical zoom, thus amplifying the harmful effects of their movements. For all these reasons, in drone scenarios, image degradation strongly affects the behavior of face detection and recognition systems. In this work, we studied the performance of deep neural networks for face re-identification specifically designed for low-quality images and applied them to a drone scenario using a publicly available dataset known as DroneSURF.
Gangs and social media: A systematic literature review and an identification of future challenges, risks and recommendations

Gang literature increasingly reflects the importance of social media in gang lifestyle, as gang members adopt new communicative practices. Yet, because of the multifaceted nature of online gang activity and the diversity of methodologies employed, a general overview of research outcomes is not easily achieved. This article seeks to remedy this by analysing academic studies of gang use of social media. A systematic literature review was conducted in Scopus and Google Scholar databases, which led to the identification of 73 publications. We then undertook a content analysis of each publication using an exhaustive evaluation model, comprising 20 variables and 71 categories. A bibliometric analysis was also performed to determine the structural characteristics of the research community that generates these publications. Our results point to an emerging universe of publications with different themes, methods, samples and ethical protocols. The challenges, risks and recommendations for future social media research with youth street groups are identified.
Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning

Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and Machine Learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.
Cluster-based data filtering for manufacturing big data systems

A manufacturing system collects big and heterogeneous data for tasks such as product quality modeling and data-driven decision-making. However, as the size of data grows, timely and effective data utilization becomes challenging. We propose an unsupervised data filtering method to reduce manufacturing big data sets with multi-variate continuous variables into informative small data sets. Furthermore, to determine the appropriate proportion of data to be filtered, we propose a filtering information criterion (FIC) to balance the tradeoff between the filtered data size and the information preserved. The case study of a babycare manufacturing and a simulation study have shown the effectiveness of the proposed method.
A novel cost‐sensitive algorithm and new evaluation strategies for regression in imbalanced domains

Abstract Many real‐world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class‐imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost‐sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error ( GME ) and Class‐Weighted Error ( CWE ), as well as new graphical‐based measures, namely REC TPR , REC TNR , REC G − Mean and REC CWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events.