Reviewerly/exHarmony huggingface dataset:
Enhanced three‐dimensional model reconstruction based on local ternary pattern‐guided fusion of multi‐exposure images

Abstract Computer vision applications usually rely on the features extracted from input images with good visibility. Image acquisition systems may produce degraded images with low contrast or distorted colours. For instance, bad weather (haze, fog) can cause images captured outdoor with low visibility. Image processing algorithms generally assume that the input image is the scene radiance. Haze removal, with the recovery of image radiance, ensures reliable features extracted from images and the image processing algorithm can achieve optimal performance. Inspired by the concept of image dehazing, the authors propose an image enhancement method that can be used to improve the visibility of the images. Each original image is first transformed into multiple exposure images by means of gamma‐correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local ternary pattern. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature. The authors evaluate their proposed method on four benchmark image dehazing datasets. The quantitative results show that our method outperforms many deterministic algorithms and deep learning models. Moreover, the authors investigate the impact of image enhancement on a practical image‐based application—the reconstruction of three‐dimensional (3D) model of survey scene. Accurate 3D model reconstruction depends on high‐quality images. Degraded images will result in large errors in the reconstructed 3D model. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that when fed into the photogrammetry software, the images enhanced by the authors’ method can reconstruct 3D scene models with sub‐millimetre mean errors, which are much better than those with the original images. As shown in the visual and quantitative results of 3D model reconstruction, the authors’ proposed method also outperforms other image enhancement methods.
Implementation of Dynamic Gesture Interpretation of Sign Language for Impact on Hearing and Speech Impairment

Expression of languages is a basic survival skill to make conversations in this world. To convey thoughts and express themselves vocally and nonverbally, mankind relies on a variety of languages. Hearing-impaired people, on the other hand, are unable to communicate verbally with others. Because sign language communicates one's message through signs using fingers, arms, head, and body, as well as mannerisms, it has become the major means of nonverbal communication for the deaf. Sign recognition is an ideal step in communicating in this non-verbal communication. Much research on languages that signers use and study have been conducted based on their respective regional sign languages such as the American (ASL) and Indian (ISL). Identifying patterns in the nuances of these different non-verbal languages helps us understand what a person is trying to communicate via signs. Since not all gesticulations are universally centralized, a few of them are single-handed; a few others, particularly the majority, use both the hands (or a mix of both).
On the correspondence between granular polytomous spaces and polytomous surmising functions

By modifying the concept of polytomous surmise functions, this paper introduces polytomous surmising functions. Then, it is shown that there is a one-to-one correspondence f between granular polytomous spaces and polytomous surmising functions where polytomous surmising functions cannot be replaced with polytomous surmise functions. This result gives a correction for a correspondence between granular polytomous spaces and polytomous surmise functions. As an application of the correspondence f, this paper demonstrates that the pair (f,f−1) of mappings forms a Galois connection where all granular polytomous spaces and all polytomous surmising functions are closed elements of this Galois connection.
Artificial Cognition of Temporal Events Using Recurrent Point Process Networks

Data generated by real-world events are usually obtained by condensing events into continuous signals at specific intervals, which are handled by mechanisms that make this data, a part of a continuous time function. Past models have used a parametric function that relates chronological data with event sequences, which are elicited through conditional intensity functions in order to quantify the frequency of event occurrence. Thus, the resulting data is devoid of a relation between present and past event occurrences, which is more often than not, a specific task that has been determined based on prior knowledge. This particular limitation is overcome using point process networks in Recurrent Neural Network (RNN) to which this paper on the said subject will enumerate a theoretical distinction along with practically implemented instances of RNNs where one RNN is concerned with capturing the relationship among the event sequences obtained as a result of the processes described earlier, while another is employed to update the intensity function based on time series. Finally, in order to prove the relation among data in a regular interval and in a random event an attempt to discover any anomaly or deviation from prediction is also implemented.
ADS-B Anomaly Detection Algorithm Based on LSTM-ED and SVDD

With the rapid development of radio broadcasting technology and information technology, the possibility of ADS-B surveillance system suffering from radio frequency interference and network attack is increasing. Firstly, the types of attacks that ADS-B system is vulnerable to are analyzed, and the abnormal data of the corresponding state are simulated. Then, in order to detect ADS-B data attacks accurately, an anomaly detection algorithm based on Long Short-Term Memory Encoder–Decoder (LSTM-ED) and hypersphere classifier is proposed. Using the normal historical data to train the LSTM-ED model so that the reconstructed values can be obtained. Then, the difference values between the actual values and the reconstructed values are put into SVDD (support vector data description) for training, and a hypersphere classifier that can detect abnormal data is obtained. It is judged whether there is an abnormality according to the set threshold. Experiments show that the LSTM-ED-SVDD model can detect abnormal ADS-B data generated by various attacks and has high accuracy.
A Machine Learning Approach for Detection of Breast Cancer in Women Using Advanced GLCM

Breast cancer (BC) is a very prevalent disease among Indian women. The simple and the more effective modality is used for screening the mammograms. The proposed methodology is a 3-step procedure that contains image acquisition, feature extraction, and classification. Our methodology is to retrieve better features from mammograms. These features are classified using a classifier that classifies the images into normal or abnormal. To extract the features from mammograms, an advanced gray-level co-occurrence matrix (AGLCM) technique is used. This extracts texture-, intensity-, and shape-based features. These are fed to several classifiers and achieved better results for artificial neural networks (ANN) classifier. The performance is measured using confusion matrix parameters. The experiments are done on the Mammogram Image Analysis Society (MIAS) dataset. It is observed that AGLCM features with ANN classifier achieve better results compared to other classifiers. The results concluded that our methodology is more effective in classifying mammogram images into normal or abnormal.
Improved Security on Mobile Payments Using IMEI Verification

The conventional financial frameworks such as the exchanges over the bank counter with a passbook are upgraded by adding the components of electronic banking, all the bank exchanges/transaction are made possible over the Internet with the progression of data and the corresponding innovation. Nonetheless, there are significant risks associated with this innovation, such as banking fraud. Hence, to fabricate trust among banking clients, it is vital to build security systems. In electronic banking, security systems basically centre on demonstrating a solid framework for the web-based exchange, particularly client validation. Numerous experts have proposed various models for web-based client access validation in the financial industry. A large portion of the research depends on the conventional type of username and secret key, yet with the differing systems of secret key structures. For example, secret phrase in erratic worth, secret key in erratic worth via obtained channel, and biometric preferred finger impression acknowledgement, voice acknowledgement, or retina acknowledgement. This paper has examined the existing client access for Internet banking based on mobile phones. The elements of the SIM card and the attributes portable of the SIM card are portrayed. Cloning, a danger to the SIM card is additionally portrayed. Discoveries show that all the security models for online client access contain secret words such as worth, biometric, or PIN. In this manner, none of the current client accesses proposed the possibility of the client access in the light of the International Mobile Equipment Identity (IMEI) number to strengthen the security of the client access. The proposed research work is more focused on security the mobile payment by utilizing the IMEI.
SMS Spam Classification Using PSO-C4.5

In this modern era, with the increased use of mobile communication, spam messages have also become common. SMS spam was sent for commercial benefits thus distributing spam links and unnecessary messages. Even now, few are seen as the victim of Fraudulent SMS. The proposed study identifies spam in SMS. The proposed method identified optimized features using a particle swarm optimization algorithm and classified them using a decision tree algorithm C4.5. This combination of PSO-C4.5 produced better performance when compared with the conventional algorithms.
A Comprehensive Understanding of Text Region Identification and Localization in Scene Imagery Using DL Practices

Semantic interpretation of scene images in our surroundings is an intriguing research domain in the realm of machine vision and pattern recognition. One of the most demanding tasks in this discipline is identifying and localizing textual information in scene images. Deep learning-based algorithms have improved the accuracy and efficiency of the scene text detection (STD) system in recent years, allowing researchers to concentrate on more target-specific concerns such as multi-oriented, arbitrarily generated, and coherent text instances. The study aims to examine and summarize recent research breakthroughs made with deep learning-based approaches. We have discussed the problem area and the various roadblocks that it entails. The reported techniques based on segmentation and regression schemes are comprehensively studied and analyzed in this work. The feature extraction and prediction frameworks used in the research are then briefly discussed. The categorical study of publically accessible datasets and evaluation procedures for scene images is specified. Finally, we discussed the domain’s possible scopes, which may pique the curiosity of future academics.
An Artificial Intelligence-Based Approach to Recognize Faces: A Perspective from Pandemic

Face recognition technology comprises methods that analyze human faces to determine their identity. Like other biometric systems, it uses for surveillance connected with public cameras without anyone’s consent. Nowadays, numerous challenges associated with recognizing faces, masked faces have also been added due to pandemic situation. Hence, in this paper, the authors concentrated on the problems of detecting masked and unmasked faces with the help of artificial intelligences-based technique, i.e., convolution neural network. It comprises methodology to develop an AI-based face recognition model using deep neural network concept. The proposed methodology includes several steps of capturing both image and video using computer vision model, i.e., OpenCV with the help of training and testing datasets. This research work helps to identify an individual regardless of masked and unmasked faces based on the recognition percentage.
Course Difficulty Estimation Based on Mapping of Bloom’s Taxonomy and ABET Criteria

The performance of a student is assessed in the present educational system using grades or marks they secure in various levels of examinations. This scoring of marks or grades that a students’ scores as part of his/her course depend on diverse parameters. Among those, the difficulty level of a course is identified to be the main parameter considering the history of scoring. Hence, priori computation of the difficulty level for a course may help the both the student and teacher community to decide on the amount of training required for the effective completion of the course. In this work, we have proposed a methodology to estimate the optimal difficulty level of a course by mapping three main components including action words in Bloom’s Taxonomy, criteria stated in Accreditation Board for Engineering and Technology (ABET), and the student learning outcomes defined in the course. The difficulty level estimated using the proposed model is validated using the history of grades secured by the students of our university and is found to be promising.
Dense Disparity Map Generation for Images Captured by Lunar Rover Navigation Camera

Perception of depth is required for automatic path planning for rover navigation on the surface of a celestial body. The current method of generating disparity maps involves offline computation as the rover has low computational power. This work proposes a novel method to estimate a good quality disparity map for regions where the surface is similar to that of the moon. The surface of the moon is covered by craters distributed unevenly throughout. Regions with little to no texture pose a major challenge in stereo matching. In the proposed method the processing pipeline involves image rectification using camera calibration, disparity map generation, disparity map refinement. The proposed method generates accurate disparity maps from the Lunar Navigation Camera(Navcam) images under lunar-like illumination conditions. In this method, occlusion handling is also proposed to improve the quality of generated disparity maps. The proposed method outperforms other previously established methods with the experimental data.
Control of Ship’ Course in Waves Based on Particle Filter Algorithm

This paper proposes a filtering algorithm for the ship state variable, which uses particle swarms with inertia weights to estimates the value of state variables. Based on Bayesian estimation theory, the distribution of particles in space is used to describe the prior probability distribution of ship state variables. The sum of weighted random particles is used to approximate the real state variables. Furthermore, Monte Carlo sampling method is used to realize this state estimation process. The estimated state variable values are entered into the heading control system and the heading motion of the vessel is controlled.
Online vs. Offline Adaptive Domain Randomization Benchmark

Physics simulators have shown great promise for conveniently learning reinforcement learning policies in safe, unconstrained environments. However, transferring the acquired knowledge to the real world can be challenging due to the reality gap. To this end, several methods have been recently proposed to automatically tune simulator parameters with posterior distributions given real data, for use with domain randomization at training time. These approaches have been shown to work for various robotic tasks under different settings and assumptions. Nevertheless, existing literature lacks a thorough comparison of existing adaptive domain randomization methods with respect to transfer performance and real-data efficiency. This work presents an open benchmark for both offline and online methods (SimOpt, BayRn, DROID, DROPO), to investigate current limitations on multiple settings and tasks. We found that online methods are limited by the quality of the currently learned policy for the next iteration, while offline methods may sometimes fail when replaying trajectories in simulation with open-loop commands. The code used is publicly available at https://github.com/gabrieletiboni/adr-benchmark .
A Survey on Mobile Application Development Models

Nowadays, due to the advancement in technology of mobile applications, smart phones/mobiles phones became part of our day-to-day life. People are using smart phones for their day-to-day activities like to read news, to play games, for shopping, browsing, etc. This increases demand for development of mobile applications. The developers have to choose appropriate application development model and develop high quality software’s on time with less budget. For this, knowledge of app development model plays an important role. The mobile applications are different from web applications in the amount of resource utilization, because the number of resources available in mobile phones will be less compared to computers. In this paper, a review is conducted to find, analyze, compare and contrast various mobile app development models and the software development methodology needs to be followed while developing mobile applications.
Deep Learning-Based Haze Removal System

Machine learning and computer vision are some of the booming areas of applied artificial intelligence (AI). Through machine learning, we give machines the ability to make decisions on their own. Computer vision is a subset of AI where we give these machines the ability to see and we give these machines the ability to make sense of images and understand crucial parts of the images. Often these images get corrupted because of noise and atmospheric disturbances, and these noises and disturbances are identified as haze or atmospheric haze. The presence of haze in the image hides the key elements/subjects in the image, so the AI algorithm finds it difficult to identify the subjects in the image. In this paper, we propose to use a deep learning-based haze removal system that will prove to be one of the most important preprocessing steps before giving the data to your AI system.
ADASemSeg: An Active Learning Based Data Adaptation Strategy for Improving Cross Dataset Breast Tumor Segmentation

Highly efficient breast ultrasound (BUS) segmentation models trained and tested on samples from one dataset do not usually have a high performance when inference is done on samples from other datasets. The solution to this data adaptation problem is crucial when it comes to deploying a trained model in a new diagnostic center. In this work, a novel active learning-based strategy ADASemSeg is proposed for dealing with this problem, where a model adapts itself from a source dataset to a target dataset via active interactions with an Expert/Oracle. Experimental analysis on two publicly available datasets suggests that the proposed approach can achieve (12–25)% improvements over baselines. Moreover, ADASemSeg can ensure a quick adaptation to the target dataset while requiring a very small amount of Oracle feedback. With such a generic approach, any segmentation model can be effectively adapted to new data distributions, improving cross-center BUS tumor segmentation performances.
Preliminary Conceptions of a Remote Incoercible E-Voting Scheme

A new remote incoercible e-voting scheme is being developed. While developing the scheme, it exploits secret key anonymous tag-based credentials, check codes, revised simplified verifiable re-encryption mix-net (R-SVRM) and confirmation numbers (CNs) as the major cryptographic building blocks. The scheme accepts remote voters and freely chosen write-in ballots. Thereby, voters can submit their votes even from remote sites as well as flexibly can choose candidates (i.e. either pre-specified or unspecified ones) as their votes, respectively. In addition, it improves efficiencies for satisfying essential requirements about accuracy, privacy, coercion resistance, fairness and robustness of e-voting systems. This paper is a preliminary report of the scheme.
A New Image Encryption Technique Built on a TPM-Based Secret Key Generation

Present communication trend involves the exchange of information using multimedia data. Images are predominantly used as a primary source of information. The evolution of new technology brings the burden of an enhanced threat landscape. Increasing data breach incidences emphasize the need for a robust and reliable mechanism to secure the information. In our work, we propose a new image encryption algorithm that uses a shared secret key to generate an actual key of encryption. The secret key used in symmetric key encryption can be generated using a neural network. The exchange of a secret key over public channels built on neural synchronization using different learning rules provides an acceptable substitute to number theory-based cryptography algorithms. Neural networks with tree topologies are classified as the most secure and reliable network models. In the neural key exchange protocol, the two tree parity machines synchronize homogeneous weights and these weights are used to establish a key exchange protocol over a public channel for the secure transmission of confidential information. To improve the security of keys generated using neural key exchange protocol, in our work we propose to use two keys: one is a shared key between two communicating entities and the second one is generated using the first key. The generated new key is used for image encryption through the AES algorithm. To understand the strength of the new key and to evaluate the quality of encryption, we have carried out statistical and differential attack analyses. The comparative study shows that the proposed method accomplishes improved results in terms of efficiency and security and helps us achieve better integrity and privacy.
Effectiveness of Ensemble Classifier Over State-Of-Art Machine Learning Classifiers for Predicting Software Faults in Software Modules

Software fault prediction (SFP) is the process of creating a model that software practitioners may use to identify defective classes/modules prior to the testing phase. The software development team leader will be able to better manage resources and decrease the testing effort if faulty modules can be predicted prior to the testing phase. Experiments and studies are being carried out in order to develop a reliable model. The software engineering process relies heavily on classification techniques that help in the prediction of software faults. According to several research, utilizing ensembles is typically more accurate than using single classifiers. Using ensemble learning approaches, this study offers a comparative analysis in software fault prediction. In this paper, state-of-art classifiers (Naive Bayes, Decision Table, Random Tree, and J48) as well as ensemble classifiers (AdaBoost, Gradient Boosting Machine, and XGBoost) are tested for SFP. The experiments were performed using the three versions of Eclipse Dataset (Eclipse 2.0, Eclipse 2.1, and Eclipse 3.0). Accuracy, F1 scores, precision, and recall are four frequently used measures for performance assessment. The results demonstrate that ensemble techniques can improve classification outcomes in SFP, with the XGBoost method providing the best results.