Reviewerly/exHarmony huggingface dataset:
Eye Tracking to Evaluate the Usability of User Interfaces

User Interface is that part of the equipment with which the user communicates with the product. These user interfaces are the buttons, touchscreen, knobs etc. These should be designed in a manner that every user can use it efficiently. But in reality, the users are having a fear to use it. In the present study it was found that among all the equipment’s respondents were having a fear to use the microwave oven because of it complicated user interfaces. While using the microwave oven the users are facing lot of problems which are related to buttons, knobs, switches, touch screen, etc. of user interface. 90% of the respondents stated that there are too many buttons in the interface and 100% reported that the buttons are a waste of space. 45% of the respondents reported that there is a bad contrast between the keys and the equipment same percent stated that the font size is not legible and 90% reported that the distance between the keys are not sufficient to operate it smoothly. Thus, the usability is dependent on text style, location, size, colour and visual information of the components in the user interface. When these attributes are used effectively users use the product without any problem. The need of the hour is to study the usability problems while using the products. In usability evaluation, today eye tracking methodology is mostly used which can be a guide to improvement in design of user interface.
The Influence of Active Projection Speckle Patterns on Underwater Binocular Stereo Vision 3d Imaging

Due to the phenomena of refraction, absorption and scattering, underwater stereo vision faces more difficulties than in air. These phenomena will bring various problems such as blurred details, reduced contrast and brightness to underwater images, which will affect the accuracy and reliability of stereo matching and increase the difficulty of stereo matching. The method based on active speckle pattern is a more simple and reliable method to enhance the surface texture of underwater objects. A good-quality speckle pattern can provide more robust and accurate results for matching the corresponding points of the left and right images. This is a more important research problem in water than in air. In this paper, the row entropy of speckle pattern is adopted to quantify the quality of the projection speckle pattern suitable for underwater stereo vision, and the relevant experiment and analysis are carried out to prove the correctness and effectiveness of new parameters. Further, the influence of different projection speckle patterns on underwater 3D imaging is researched. The actual underwater experiment indicates that the speckle pattern exerts a great influence on the accuracy of underwater 3D imaging based on the binocular stereo vision, and the row entropy of higher-quality projection speckle pattern should be as small as possible. This work can give a reference for the selection of projection speckle patterns in underwater binocular stereo vision. An appropriate speckle pattern can effectively improve the accuracy of underwater 3D measurement.
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes

Spectral mixture kernels comprise a powerful class of optimal kernels for Gaussian processes (GPs) to describe complex patterns. This paper introduces model compression and time- and phase (TP) modulated dependency structures to the original spectral mixture (SM) kernel for improved GP model expressiveness. Specifically, by adopting BienaymÂ´es identity, we generalize the dependency structure through cross-covariance between the SM components. Then, we propose a novel SM kernel with a dependency structure (SMD) by using crossconvolution between the SM components. Furthermore, we ameliorate the expressiveness of the dependency structure by parameterizing it in terms of time and phase arguments. The dependency structure has clear interpretations in terms of spectral density, covariance behavior, and sampling path. To obtain an SMD with effective hyperparameter initialization, compressible SM kernel components, and sparse dependency structures, we introduce a novel structure adaptation (SA) algorithm in the end. A thorough comparative analysis of the SMD on both synthetic and real-life applications corroborates the efficacy of the new kernel.
An Empirical Study on Cognitive Impartment of Knowledge in Children Through Augmented Reality

Utilization and implementation of modern technology have become a necessity in various domains of society for proper and updated growth in that particular sector. Children are most influenced by this wake of modern technology in their daily life. This paper aims towards investigating the amount of interest generated among children through their present academic course curriculum and proposal of a design training module for children. The module would essentially deal with the comprehensive representation of educational information through AR. The advantageous aspects and challenges of using AR in the education sector, were tallied by conducting a survey among children and their parents using module prototype and questionnaire. An in-depth analysis of the subject matter was done about proper utilization and implementation of the proposed aspect. Augmented Reality (AR) being an advanced technology for a big chunk of the masses, especially children, could prove to be an interesting element to Indian education scenario. The study ventures through the possible future scope of the module into generating cognitive impartment of knowledge while also introducing fun and engagement into study materials through Augmented Reality.
Information Society and the Development of the Educational Space

The twenty-first century could be rightfully considered as an era of information accompanied by the global informatization of the modern society. The globalization of the information society in this research is interpreted as a macroscopic, multi-faceted and internally contradictory process of the growth of similarity in the world systems: economic, political (Rhodes, 1997), educational, social and legal. The current dependence of human civilization on its information component has made it much more vulnerable in this regard. In addition, such vulnerability is connected with the fact that our society today is a fundamentally open structure (Grossman, 1995). The most important result of the formation of information society was the emergence of the so-called global information space (Martin, 2014). The authors of this article seek to answer the following questions: how does the sphere of education develop in the context of global information society and what are the key trends in the process of informatization of the educational space. Information resources and structures have become a tool for achieving a strategic advantage. The authenticity, credibility, as well as an adequate reflection of information realities represent the key challenges for the communication society. It has become clear that to live in a more complex, interconnected and detailed world the mankind needs to be well-equipped with tools for understanding its own behavioral patterns and their impact on the new social dynamics.
Enabling Sign Language Recognition Feature in Video Conferencing

Pandemic situation has a major impact upon students in the mode of education. Students get back their quality of education through online mode supported by various platforms like Google Meet, Zoom etc. Still there exists a social group of students with oral and aural difficulties who suffer to get quality education online which is really pathetic to hear. Our paper aims to develop a special educational platform for the differently-abled students in order to make their conversation easier with a normal teacher through video conference (converting sign language into caption) as well as converting the voice of normal teacher into captions for easy understanding of differently-abled students (converting voice to caption).
Convolution Neural Network Based Degenerated Mode Decomposition Via Near-Field Images from Linear and Circular Polarizers

A mode decomposition (MD) method for degenerated modes has been studied. Convolution neural network (CNN) has been applied for image training and predicting the mode coefficients. Four-fold degenerated LP11 series has been the target to be decomposed. Total of seven different images, including the full original near-field image, and images after linear polarizers of four directions (0°, 45°, 90°, and 135°), and images after two circular polarizers (right-handed and left-handed) has been considered for training, validation, and test. The output label of the model has been chosen as the real and imaginary components of the mode coefficient, and the loss function has been selected to be the root-mean-square (RMS) of the labels. The RMS and mean-absolute-error (MAE) of the label, intensity, phase, and field correlation between the actual and predicted values have been selected to be the metrics to evaluate the CNN model. The CNN model has been trained with 100,000 three-dimensional images with depths of three, four, and seven. The performance of the trained model was evaluated via 10,000 test samples with four sets of images - images after three linear polarizers (0°, 45°, 90°) and image after right-handed circular polarizer - showed 0.0634 of label RMS, 0.0292 of intensity RMS, 0.1867 rad of phase MAE, and 0.9978 of average field correlation. The performance of 4 image sets showed at least 50.68% of performance enhancement compared to models considering only images after linear polarizers.
Combined Use of Selected UX Research Techniques and Creation of User Persona for Design and Evaluation of Sustainable e-Commerce Apps—A Case Study

The advent of digital economy, the expansion of Internet user base, the surge in the number of smartphone users, and the increasing interconnectedness of man, machines and organizations have fuelled a phenomenal growth in the market penetration of e-commerce worldwide. The disruption of physical shopping by COVID-19 outbreak and the consequent change in consumer behaviour, leaning more towards online shopping, has also significantly pushed up that growth trajectory. The proliferation of e-commerce-apps is a complementary component of this phenomenon, and the competition for sustainability and growth of these apps and the associated companies is ever-increasing. The above developments have necessitated the according of heightened attention to User Experience, i.e., UX, and cognitive response, manifested in consumers’ need, preference, attitude, behaviour and comfort, in designing new e-commerce apps and continually improving the existing ones. For meeting this need, a plethora of tools and techniques are available for informing the design process from the perspectives of end-users for enhancing user-centric design efficacy and productivity of the apps. In this paper, a methodology of combining six UX research techniques has been deployed and a user persona has been created in a novel framework by selecting an existing e-commerce App and engaging a consumer newly opting for online shopping. Pain points have been explored and scopes of improving the App identified. This methodology of bettering usability and user experience would be useful for the design and testing of not only e-commerce apps but also of apps developed for numerous other fields of applications.
To Explore an Innovative Process to Offer Personalized Learning by Providing Localized Accessibility for Both Teachers and Students with the Help of a Mobile Application

In India, there exists a system of private or personalized learning at home in parallel to the regular academic classes offered at schools to overcome inadequacies in the learning process. The system is commonly termed tuition in India, which many school-going kids used to follow. Moreover, students who desire to excel in academics invariably also follow personalized coaching for their studies. Though various online coaching firms like Byju’s, Vedantu, etc. are available nowadays, no such online platform is used to offer tutors who would offer service for both offline and online mode. Hence, an alternative tutoring platform may be desired that can address the limitations of personalized coaching by offering both offline and online modes with quality services. This study was particularly carried out to conceptualize such an online platform by creating an application for digital devices which may address the necessity of personalized coaching with exclusive offerings. The app concept has been designed to help students and their parents to search for tutors of different subjects and getting in touch with them physically and virtually both for personalized coaching. The app further explores connecting people of quality teaching capability from varied fields to a wide range of students from different places for knowledge exchange and learning. The primary focus has been put on content interactivity and smooth flow of information and navigation style for the users to make the application efficient and effective.
Information Society and Informatization of the Educational Space

The twenty-first century could be rightfully considered as an era of information accompanied by the global informatization of the modern society. The globalization of the information society in this research is interpreted as a macroscopic, multi-faceted and internally contradictory process of the growth of similarity in the world systems: economic, political (Rhodes, 1997), educational, social and legal. The current dependence of human civilization on its information component has made it much more vulnerable in this regard. In addition, such vulnerability is connected with the fact that our society today is a fundamentally open structure (Grossman, 1995). The most important result of the formation of information society was the emergence of the so-called global information space (Martin, 2014). The authors of this article seek to answer the following questions: how does the sphere of education develop in the context of global information society and what are the key trends in the process of informatization of the educational space. Information resources and structures have become a tool for achieving a strategic advantage. The authenticity, credibility, as well as an adequate reflection of information realities represent the key challenges for the communication society. It has become clear that to live in a more complex, interconnected and detailed world the mankind needs to be well-equipped with tools for understanding its own behavioral patterns and their impact on the new social dynamics.
An Improved User Interface for Enabling Smart Access Using Low-Cost QR Based Systems

Organizations of today are scrutinized for neglecting safety and healthcare of their employees The entry/exit points witness rigorous human contact and are hotspots of germ transmission by touch. In this paper, we propose an authentication method for physical access control by using a Quick Response (QR) Code Powered Setup. This low-cost method facilitates quick implementation by only requiring the smartphones of its employees. The main motive is to help organizations in tracking and prioritizing the safety of its employees when entering the organization and facilitating seamless human resource management. This system uses an encryption algorithm that assures a secured flow of the programmed events, avoiding any external mal-hindrance. The QR code is transmitted through a carrier network, thus enabling faster operations. The estimated duration between scanning and verification is approximately 2–3 s before the barricades get unlocked. This system is feasible for accessing gadget/tokens in semi-restricted public places and venues demanding a record of individuals using their services. QR code reading is unambiguous, as it only demands proximity of the reader device to the user’s screen, thereby trenching any chances of fallacy. This technology is proficient and expedient in ways such as higher data storage capacity, curtailed implementation cost, technical simplicity, extensive use, and is globally available. Varied compatible sequencers and extraneous features can be accumulated in this ecosystem. The proposed system is demonstrated with a simple UI that can be upgraded in scale when implemented. The validation of the proposed system is measured with the SUS scale among a set of users.
Locating Hidden Sources in Evolutionary Games Based on Fuzzy Cognitive Map

How to identify the enemy’s hidden military power based on limited information is a great challenge in a military confrontation. A military confrontation environment can be naturally modeled as a complex system. Fuzzy cognitive map inherits the main characteristics of fuzzy logic and neural network. Thus, it is widely used to model complex systems and get a weighted directed network from existing data. In terms of great succuss of fuzzy cognitive map for modeling and analyzing complex systems, a hidden node localization strategy is proposed. This algorithm can measure the anomalies between fuzzy cognitive maps obtained from different data segments. The experimental results showed the our approach could effectively identify the enemy’s hidden military power from the observed data. In several case studies, the influence of various parameters on the accuracy of positioning is analyzed through experiments. The framework for detecting hidden nodes is expected to be successfully applied in many fields.
Towards Neural Video Compression: A Rate Distortion Modeling Perspective

A novel trend in video compression is to use end-to-end optimized neural techniques. However, the rate-distortion (R-D) behavior of such scheme remains unexplored. In this paper, we for the first time study the essential characteristics of neural video compression (NVC) by comparatively modeling the R-D behavior of conventional codec and NVC. We give the observation that the proportion of required coding bits for motion field and residual are essentially different between the two kinds of codecs. We also show that improving the efficiency of inter prediction module would be the key factor to shorten the performance gap between NVC and conventional codec. Given such observation, we propose the rate-distortion modeling inspired neural video compression (RD-NVC) framework to increase prediction accuracy and reduce residual coding bits. For the former part, a novel prediction refinement network is proposed to improve predictive coding efficiency. Regarding the latter aspect, coarse-to-fine (C2F) residual modeling and in-loop restoration are proposed to save the residual coding bits. The proposed framework substantially promotes the R-D performance of NVC in a comprehensive manner. The experiment demonstrates that our method outperforms the state-of-the-art single reference frame NVC approaches. To the best of our knowledge, the proposed method is the first NVC that shows comparable R-D performance with H.266/VVC in terms of MS-SSIM under same prediction structure.
Density-NMS: Cell Detection and Classification in Microscopy Images

With the development of digital pathology, the automatic detection and classification of microscopy image cells using artificial intelligence technology has become a research hotspot. However, due to the problems of multi-scale cells, unbalanced foreground and background, and cell adhesion in micro images, the existing artificial intelligence object detection algorithms directly used in microscopic images have poor detection results. Therefore, to solve these problems, this paper proposes a region proposal networks based on sample weighting for cell detection and classification in microscopy images. In particular, on the basis of GA-RPN which combines the guided anchoring (GA) and the region proposal network (RPN), the sample weight learning module is added to the network, so that the network can learn the weight through the sample characteristics, which can significantly improve the detection effect. In addition, we propose a new non-maximal suppression algorithm, Density-NMS, which dynamically adjusts the suppression threshold according to the density of cell instances, allowing the model to have good detection in the case of cell adhesions. We test the proposed approach on two open cell detection challenge, Blood Cell Detection (BCCD) and Malaria. Experimental results show that the region proposal network applying sample weighting achieved superior performance on the cell detection and classification in microscopy images when compared with traditional object detection methods. In conclusion, our method can achieve better performance in object detection domain and provide an auxiliary method for pathologists to diagnose patients’ diseases.
Deform-CAM: Self-attention Based on Deformable Convolution for Weakly Supervised Semantic Segmentation

Weakly-supervised semantic segmentation (WSSS) receives increasing attentions from the community in recent years as it leverages the weakly annotated data to solve the problem of lacking of fully annotated data. Among them, the WSSS method based on image-level annotation is the most direct and effective while the image-level annotation is easy to obtain. Most advanced methods use class activation maps (CAM) as initial pseudo-labels, however, they only identify local regions of the target, while ignoring the context information among local regions. To solve this problem, this paper proposes a deformable convolution based self-attention module (DSAM), which introduces a pixel relationship matrix, to learn the contextual information of the image. A regularization loss is introduced to narrow the distance between the DSAM and the CAM. Compared to the base CAM method, our method can identify more target features and robustly improve the performance of WSSS without training the classifier multiple times. Our proposed method achieves the mIoU of 65.5% and 66.8% on the Pascal VOC 2012 val and test sets, respectively, demonstrating the feasibility of the method.
A Fast Line and Ellipse Detection on High Resolution Images

Line and ellipse are important image features in pattern recognition and computer vision. Many methods have been developed to extract line or ellipse in images separately but few try to detect them simultaneously. In this paper, a novel fast line and ellipse detection (FLED) method is proposed to detect line and ellipse simultaneously, even in high resolution images. At first, a detection framework (Pre-SGV) for high detection speed is proposed, which explicitly decomposes the detection into precalculate, segment, grouping, validation phases. Secondly, a simple but efficient algorithm is designed to segment the edges into line or arc candidates. Thirdly, the grouping constraints and fitting methods are further improved. Finally, validation are conducted to exclude erroneous detection. Experiments on synthetic images and real image dataset show that the proposed method, FLED, can robustly detect lines and ellipses fast and efficiently, especially for high resolution image (e.g. remote sensing image, the scanning image).