Reviewerly/exHarmony huggingface dataset:
Designing successive target selection in virtual reality via penetrating the intangible interface with handheld controllers

Empirical research on single target selection in Virtual Reality/Augmented Reality (VR/AR) environments has been studied extensively. However, there is a lack of methods and research on improving the coherence when successively selecting multiple targets. We propose Sewing, a successive target selection method with handheld controllers in VR environments. We leverage real-life sewing as a design metaphor for the selection of multiple targets with one single spatial movement penetrating targets with handheld controllers. We conducted an empirical study to validate the efficiency of selecting multiple targets with Sewing compared with the conventional selection method, which is based on the button pressing metaphor. Results showed that Sewing can provide promising performance and coherence for rapid and accurate target selection. Based on the results, we implemented SewTyping, a Sewing-based VR text entry method to evaluate the applicability of Sewing, and conducted an empirical study to compare the typing performance with two other techniques (Controller Pointing and Drum-like Keyboard). Results showed that 1) SewTyping achieved 26.57 words per minute with a total error rate of 3.68% and 2) users adapted to the sewing-like movement easily and achieved steady performance with essential practice.
Improving LSHADE by means of a pre-screening mechanism

Evolutionary algorithms have proven to be highly effective in continuous optimization, especially when numerous fitness function evaluations (FFEs) are possible. In certain cases, however, an expensive optimization approach (i.e. with relatively low number of FFEs) must be taken, and such a setting is considered in this work. The paper introduces an extension to the well-known LSHADE algorithm in the form of a pre-screening mechanism (psLSHADE). The proposed pre-screening relies on the three following components: a specific initial sampling procedure, an archive of samples, and a global linear meta-model of a fitness function that consists of 6 independent transformations of variables. The pre-screening mechanism preliminary assesses the trial vectors and designates the best one of them for further evaluation with the fitness function. The performance of psLSHADE is evaluated using the CEC2021 benchmark in an expensive scenario with an optimization budget of 10^2-10^4 FFEs per dimension. We compare psLSHADE with the baseline LSHADE method and the MadDE algorithm. The results indicate that with restricted optimization budgets psLSHADE visibly outperforms both competitive algorithms. In addition, the use of the pre-screening mechanism results in faster population convergence of psLSHADE compared to LSHADE.
Digital watermarking method for image feature point extraction and analysis

Multimedia information (e.g., photos) is subject to numerous attacks when extensively dispersed, excellent stealth and robustness play a critical role in information concealment. Next, to further improve the imperceptibility of the generated images, we introduce an information embedding based on feature extraction, since the feature points themselves are robust as strong corner points in the images. We will perform specific practical applications in information-hiding scenarios, such as ticket anticounterfeiting, copyright protection, tampering hints, and covert marking. As a result, while assuring the accuracy of hidden data, assault resistance must also be addressed. In this paper, a strategy for watermark embedding utilizing image feature points is proposed. In principle, the embedding and extraction of digital watermarks are achieved in the transform domain by discrete wavelet transform processing to make digital images more robust than before. The effectiveness of the algorithm is analyzed by experimental data, in which different wavelet bases are used. To check the robustness of the watermarking algorithm, various attacks are performed on watermarked images.
Evaluating augmented reality for ‘real life’ teaching of food portion concepts

Estimation of food portions is a vital skill for dietitians, which is developed during formal nutrition training. Skill development is often accomplished by training with food portion estimation tools. These tools can vary in design but evaluations often reveal them to be limited in their effectiveness and generally impractical for everyday use. The aim of this study was to develop and evaluate an augmented reality (AR) tool for the estimation food portions.An online, quasi-experimental, randomised pre-test post-test study was conducted to evaluate the effectiveness of three food portion tools with nutrition students. These tools consisted of an online, AR, and an infographic tool (control). Students tested 10 different food images and were asked to estimate food portion sizes with and without assistance of a portion tool to determine absolute error, relative error, and overall improvement in estimation.A total of 33 participants enrolled in the study with 26 (72.0%) completing the study. The mean absolute error was lowest in the online group (53.0%), followed by AR (59.5%) and control (64.0%). Relative error scores revealed higher accuracy for the AR group (45.5%) followed by online (43.5%), and control group (29.0%). Overall improvement in estimation was highest in the AR group (+12.2%) followed by the online (+11.6%) tool with a decrease seen for the infographic (-1.7%) tool.The use of technology, notably AR technology, may provide some advantage when training nutrition students in food portion estimation, although further investigation is advised.
Deep Possibilistic <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>C</mi> </math>-means Clustering Algorithm on Medical Datasets

In the past, the possibilistic C-means clustering algorithm (PCM) has proven its superiority on various medical datasets by overcoming the unstable clustering effect caused by both the hard division of traditional hard clustering models and the susceptibility of fuzzy C-means clustering algorithm (FCM) to noise. However, with the deep integration and development of the Internet of Things (IoT) as well as big data with the medical field, the width and height of medical datasets are growing bigger and bigger. In the face of high-dimensional and giant complex datasets, it is challenging for the PCM algorithm based on machine learning to extract valuable features from thousands of dimensions, which increases the computational complexity and useless time consumption and makes it difficult to avoid the quality problem of clustering. To this end, this paper proposes a deep possibilistic C-mean clustering algorithm (DPCM) that combines the traditional PCM algorithm with a special deep network called autoencoder. Taking advantage of the fact that the autoencoder can minimize the reconstruction loss and the PCM uses soft affiliation to facilitate gradient descent, DPCM allows deep neural networks and PCM's clustering centers to be optimized at the same time, so that it effectively improves the clustering efficiency and accuracy. Experiments on medical datasets with various dimensions demonstrate that this method has a better effect than traditional clustering methods, besides being able to overcome the interference of noise better.
Attendance Tracking with Face Recognition Through Hidden Markov Models

Facial recognition is one of the most secure ways to identify a person. Manual attendance in organizations, be it in classrooms or libraries or even attendance for teachers is truly a hassle. Due to the inception of Hidden Markov Model (HMM), they have worked well with image data and it has plethora of facial recognition applications. In this paper, yet another application of face recognition with HMM is explored, where it is integrated with Singular Value Decomposition (SVD) and track the attendance of the students present in a database. HMMs deal with data in the form of states and sequences. Face recognition looked through the lens of HMMs which can be framed in the following manner: a face is split into regions vertically (forehead, chin, etc.) and a particular sequence is always preserved. A rectangular window of fixed size is passed over every test image, and for every vector obtained, the probability of data is calculated. For training, probability computation is done with the help of the Baum Welch algorithm. This whole model is connected to a simple program to keep track of the students leaving and entering the classroom, marking their presence only and updating the same information in the college’s database.
Endoscopy image enhancement method by generalized imaging defect models based adversarial training

Objective.Smoke, uneven lighting, and color deviation are common issues in endoscopic surgery, which have increased the risk of surgery and even lead to failure.Approach.In this study, we present a new physics model driven semi-supervised learning framework for high-quality pixel-wise endoscopic image enhancement, which is generalizable for smoke removal, light adjustment, and color correction. To improve the authenticity of the generated images, and thereby improve the network performance, we integrated specific physical imaging defect models with the CycleGAN framework. No ground-truth data in pairs are required. In addition, we propose a transfer learning framework to address the data scarcity in several endoscope enhancement tasks and improve the network performance.Main results.Qualitative and quantitative studies reveal that the proposed network outperforms the state-of-the-art image enhancement methods. In particular, the proposed method performs much better than the original CycleGAN, for example, the structural similarity improved from 0.7925 to 0.8648, feature similarity for color images from 0.8917 to 0.9283, and quaternion structural similarity from 0.8097 to 0.8800 in the smoke removal task. Experimental results of the proposed transfer learning method also reveal its superior performance when trained with small datasets of target tasks.Significance.Experimental results on endoscopic images prove the effectiveness of the proposed network in smoke removal, light adjustment, and color correction, showing excellent clinical usefulness.
Improved Sentimental Analysis to the Movie Reviews using Naive Bayes Classifier

Sentimental analysis is an understanding and classification process of human sentiments about a particular product/service. It is involved in all departmental field to get the customer opinion towards the particular product/service through online feedback or symbols in social media. The human opinion about the product/ service is reflected as positive or negative or neutral feelings. It is identified as opinion mining gleaning conception among the things connected with human. Sentimental analysis is an interesting research among human view, sentiments, attitudes and emotions, articulated and composed in different language through different multimedia data. It applied to movie review application to monitor and analyze the social occurrences about the movie. It involves collection of data, extraction of data, classification and evaluation of data. The resource provider gets the feedback/opinion about their resource through text analysis. The proposed research work analyzes a remedy in favor of sentiment classification using Naive Bayes Classifier at a fine-grained level. An improved sentiment analysis to movie reviews application is implemented in python using Machine learning and Flask framework.
Reliable detection of doppelgängers based on deep face representations

Doppelgängers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non-mated comparison trials. In this work, the impact of doppelgängers on the HDA Doppelgänger and Disguised Faces in The Wild databases is assessed using a state-of-the-art face recognition system. It is found that doppelgänger image pairs yield very high similarity scores resulting in a significant increase of false match rates. Further, a doppelgänger detection method is proposed, which distinguishes doppelgängers from mated comparison trials by analysing differences in deep representations obtained from face image pairs. The proposed detection system employs a machine learning-based classifier, which is trained with generated doppelgänger image pairs utilising face morphing techniques. Experimental evaluations conducted on the HDA Doppelgänger and Look-Alike Face databases reveal a detection equal error rate of approximately 2.7% for the task of separating mated authentication attempts from doppelgängers.
A note on an enhanced dynamic authentication scheme for mobile satellite communication systems

Summary Recently, Yulei Chen and Jianhua Chen (An enchanced dynamic authentication scheme for mobile satellite communication systems. Int J Satell Commun Netw. 2020; 39(3): 250‐262) presented an enhanced dynamic authentication scheme for mobile satellite communication systems in 2020. The authors found that there is a flaw in the authentication phase of the scheme proposed by Yulei Chen and Jianhua Chen's scheme. Due to this flaw, user is unable to authenticate to NCC (network control center) and also fails to establish the session key with NCC. In this paper, the authors improved the Yulei Chen and Jianhua Chen's scheme. In the proposed scheme, it is shown that user is able to authenticate to NCC (network control center) and also establish the session key with NCC. The authors also demonstrate that the computation cost and communication cost are also decreased.
Generating QA from Rule-based Algorithms

In the education industry answering questions is used as a common parameter to judge one’s understanding of a topic. Taking quizzes on a regular basis helps an individual feel confident and it also helps the professor assess the student’s understanding on a particular topic. Generating question and answer pairs is a time-consuming task. To solve this problem, this paper discusses methods to generate automatic Natural Language Processing models which creates diverse types of question-answer pairs. The model takes an input in the form of text in the English language and produces output as Complex Questions, Multiple Choice Questions with relevant distractors, and Fill in the Blanks type of questions. To generate Complex Questions a Rule-Based Algorithm is used. To generate Multiple Choice Questions and Fill in the Blanks type questions, a Vector Algorithm from the GLoVe Model is used along with Rule-Based Algorithms. This paper also includes a detailed explanation of the analysis of the pattern and rules that are observed in the question-making process. SQuAD dataset is used for this analysis and used the same dataset to train the model. The implementation process of this model focused on generating diverse questions with higher syntactic correctness than the existing models. The approach mentioned in this paper can be used in the fields of education, entertainment, generation of quizzes, virtual learning assistance and to get a deeper insight into any topic.
Touchless Interfaces in the Operating Room: A Study in Gesture Preferences

Touchless interfaces allow surgeons to control medical imaging systems autonomously while maintaining total asepsis in the Operating Room. This is specially relevant as it applies to the recent outbreak of COVID-19 disease. The choice of best gestures/commands for such interfaces is a critical step that determines the overall efficiency of surgeon-computer interaction. In this regard, usability metrics such as task completion time, memorability and error rate have a long-standing as potential entities in determining the best gestures. In addition, previous works concerned with this problem utilized qualitative measures to identify the best gestures. In this work, we hypothesize that there is a correlation between gestures' qualitative properties and their usability metrics. In this regard, we conducted a user experiment with language experts to quantify gestures' properties (v). Next, we developed a gesture-based system that facilitates surgeons to control the medical imaging software in a touchless manner. Next, a usability study was conducted with neurosurgeons and the standard usability metrics (u) were measured in a systematic manner. Lastly, multi-variate correlation analysis was used to find the relations between u and v. Statistical analysis showed that the v scores were significantly correlated with the usability metrics with an R2≈0.40 and p < 0.05. Once the correlation is established, we can utilize either gestures' qualitative properties or usability metrics to identify the best set of gestures.
Wireless Communication for Robotic Process Automation Using Machine Learning Technique

Machine intelligence is what has been generated by programming computers with certain aspects of human intellect, like training, solving problems, and priority setting. A machine can solve a number of complicated issues using these capabilities. In major industries, such as customer support and manufacturing, machine intelligence is now being employed. The growth and quick development of digital technology and artificial intelligence (AI) technologies are becoming more and more difficult. At now, sophisticated manufacturing, the world of invention, and broad acceptance are undergoing a fast transition. Robotics is much more vital as it may now be related to the human brain by the connection between machine and brain, as AI develops. The world’s economy faces substantial difficulties by increasing productivity in the manufacturing industry. This study examines the present progress of robotic communication styles of artificial intelligence (AI). In many specific applications, communication between members of a robotic group or even people becomes vital. The paper solves the problem of implementation of an independent industry mobile robot in all fields in the major business, live interactive, planning, mobile robot technologies, and intending. In order to identify the best solution to this issue, a mixed integer robotic model has been developed.
Combining attention mechanism and Retinex model to enhance low-light images

Low-light image enhancement is challenging due to intractable problems such as color distortion and noise, which hide in the dark. Simply enhancing the brightness of dark areas will inevitably amplify hidden artifacts. We have observed more noise in the underexposed areas of images than in the normally exposed areas. Attention mechanism can be used to emphasize the vital information of the processed object and suppress some irrelevant information. Inspired by these observations, we propose a deep network that Combines Attention mechanism and Retinex (CA&R Net) model to enhance low-light images. Firstly, we develop an attention map to evaluate the degree of image underexposure and guide enhancement in a region-adaptive manner. This way, it can enhance underexposed areas and avoid over-enhancing normally exposed areas. Secondly, we use the reconstructed reflectance and low illumination to predict the illumination layers of the image jointly. This joint prediction utilizes the attention mechanism, making illumination adjustment achieve better results. The quantitative experimental results show that the CA&R Net can successfully handle noise, color distortion, and multiple types of degradation with the power of attention information. Moreover, both SSIM and PSNR are better than other advanced methods.
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks

Adversarial attacks are a major challenge faced by current machine learning research.These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications.Extensive research in computer vision has been carried to develop reliable defense strategies.However, the same issue remains less explored in natural language processing.Our work presents a model-agnostic detector of adversarial text examples.The approach identifies patterns in the logits of the target classifier when perturbing the input text.The proposed detector improves the current state-ofthe-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks.
Power to the people: Applying citizen science and computer vision to home mapping for rural energy access

To implement effective rural electricity access systems, it is fundamental to identify where potential consumers live. Here, we test the suitability of citizen science paired with satellite imagery and computer vision to map remote off-grid homes for electrical system design. A citizen science project called “Power to the People” was completed on the Zooniverse platform to collect home annotations in Uganda, Kenya, and Sierra Leone. Thousands of citizen scientists created a novel dataset of 578,010 home annotations with an average mapping speed of 7 km2/day. These data were post-processed with clustering to determine high-consensus home annotations. The raw annotations achieved a recall of 93% and precision of 49%; clustering the annotations increased precision to 69%. These were used to train a Faster R-CNN object detection model, producing detections useful as a first pass for home-level mapping with a feasible mapping rate of 42,938 km2/day. Detections achieved a precision of 67% and recall of 36%. This research shows citizen science and computer vision to be a promising pipeline for accelerated rural home-level mapping to enable energy system design.
Cloud-Based Android Intelligent Voice and Enterprise Economic Statistics System Design

The analysis of the current economic statistics system shows that the requirements for computing and storage capacity are relatively high. For the previous main controller, it is difficult to meet this basic requirement, but the application of cloud computing technology can solve this problem to a greater extent. Therefore, this paper designs a new economic analysis system based on cloud computing, which can enable small- and medium-sized enterprises to master the business information transmitted by customer economy and improve the quality of monitoring service. Experiments in this paper show that the designed system can reduce the cost of remote communication, interact with SMS, and realize dual channel communication.
Color Demosaicing of RGBW Color Filter Array Based on Laplacian Pyramid

In recent years, red, green, blue, and white (RGBW) color filter arrays (CFAs) have been developed to solve the problem of low-light conditions. In this paper, we propose a new color demosaicing algorithm for RGBW CFAs using a Laplacian pyramid. Because the white channel has a high correlation to the red, green, and blue channels, the white channel is interpolated first using each color difference channel. After we estimate the white channel, the red, green, and blue channels are interpolated using the Laplacian pyramid decomposition of the estimated white channel. Our proposed method using Laplacian pyramid restoration works with Canon-RGBW CFAs and any other periodic CFAs. The experimental results demonstrated that the proposed method shows superior performance compared with other conventional methods in terms of the color peak signal-to-noise ratio, structural similarity index measure, and average execution time.