Reviewerly/exHarmony huggingface dataset:
Experimental Study on the Effect of Zooming Feature on Visual Perception

With the help of overall image zooming, the limited interface space can carry more information, but too much zooming will also bring some cognitive costs. In this paper, we quantified the effect of scaling characteristics on visual perception through a set of visual search experiments. The results showed that visual perception was not affected when the scaling ratio was no greater than 2:1. Besides, we found that the higher the scale, the faster the visual search efficiency decreases with the number of items in the display (set size). We also analyzed the influence of different positions of the target on visual perception and found that the reaction time (RT) of the target in the central area was significantly shorter, while the reaction time of the target in the lower right part of the view was the longest. These results may guide interface space scaling transformation and interface layout design.
Zeroth-order optimizer benchmarking for 3D performance capture

In the field of 3D Human Performance Capture, a high-quality 3D scan of the performer is rigged and skinned to an animatable 3D template mesh that is subsequently fitted to the captured performance's RGB-D data. Template fitting is accomplished via solving for the template's pose parameters that better explain the performance data at each recorded frame. In this paper, we challenge open implementations of zeroth-order optimizers to solve the template fitting problem in a human performance capture dataset. The objective function that we employ approximates, the otherwise costly to evaluate, 3D RMS hausdorff distance between the animated template and the 3D mesh reconstructed from the depth data (target mesh) at an individual recorded frame. We distinguish and benchmark the optimizers, in three different real-world scenarios, two of which are based on the geometric proximity of the template to the target in individual frames, while in the third one we fit the template sequentially to all target frames of the recorded sequence. Conclusions of this work can serve as a reference for future optimizer implementations and our findings can server as a baseline for future multi-objective optimization approaches. We make part of our benchmark and experiment setup publicly available (https://github.com/VCL3D/nevergrad, https://github.com/VCL3D/PerformanceCapture/releases/).
Monitoring and Prediction Mechanism of Artificial Intelligence Technology in Online Language Violence

Online language violence is a new product of the Internet’s information exchange function. It refers to the influence of violent language, including the most serious judgmental language, profanity and defamatory language on the Internet, gossip, human flesh search research, etc. People who are experiencing online language violence will be mentally hurt. At the same time, as artificial intelligence technology continues to be adopted and researched by everyone, artificial intelligence technology is inevitably selected for the research on the monitoring and prediction mechanism of online language violence. This paper mainly adopts the literature reference research method, the analytic hierarchy process and the investigation and research method, uses artificial intelligence technology to study the monitoring and prediction mechanism of online language violence, establishes a mathematical model, uses the analytic hierarchy process to solve it, and monitors online language violence Evaluate the research status of the prediction mechanism, and use historical data to revise the model to improve the accuracy of the monitoring of online language violence and the evaluation of the prediction mechanism research status. The experimental results show that the analytic hierarchy process has increased the research efficiency of the monitoring and prediction mechanism of online language violence by 23%, and reduced the rate of false positives and false positives. Finally, by comparing the social harm analysis of online language violence and the analysis of the evaluation index system of online language violence, it illustrates the influence of artificial intelligence technology on the research of the monitoring and prediction mechanism of online language violence.
A Role-Based Prototyping Approach for Human-Centered Design in Industry

In previous work, the so-called role-based prototyping approach (RBPA) was proposed. It is for interdisciplinary student teams and puts strong emphasis on autonomy and proactivity. Progress in the project is measured by the three lenses of human-centered design, feasibility, viability, and desirability. It guides the students on important aspects of fuzzy-front product development by providing a template of the overall process and specific tools, e.g., an adapted Kanban board. This approach was limited to academia. In this paper, an application of the RBPA in an industry setting is discussed. Industry settings are different from classical fuzzy-front end scenarios because a context is given. To address this, the roles of the proposed approach were refined by assigning specific tasks to incorporate customer requirements. The result was deployed in a university course where students were given design tasks from industry. Each team is guided through this process with the help of daily coaching sessions and their progress is tracked, recorded, and evaluated. The results are then discussed in detail with an outlook for the future.
An Anatomization Model for Farmer Data Collections

Smart farming is one of the big concepts that are regarding in the world. It is a development technique for modern agricultures. It generally emphasizes the use of data sensors and data communication technologies in conjunction with an appropriate data analysis technique. Aside from data sensors and data communication technologies, survey questionnaires can also be used to collect the data for developing the smart farming. With survey questionnaires, they are generally proposed to collect the answer about the farmer’s opinion to the specified questions and his/her history data. For this reason, the data sets of smart farming are often the high dimension of quasi-identifier and more sensitive attributes. Moreover, the sensitive attributes of farmer data sets are multiple groups. Thus, privacy preservation models could be insufficient to address privacy violation issues in farmer data sets when they are released for public use. For this reason, an appropriate privacy preservation model for farmer data sets is proposed in this work. Furthermore, the proposed model is evaluated by using extensive experiments. From the results, we found that the proposed model is highly effective than the compared privacy preservation models.
Client-Side Metering and Monitoring of Service Level Agreement for Green Cloud Services

Substantial development of Information & Communication Technologies (ICTs) has imprinted a great role for our lives and lifestyles. The ICTs provide a convenient environment for the customer for computing, communication, and collaboration. The Cloud Service Providers (CSPs) offer several types of IT resources/services via the internet. These are provided based on a service level agreement (SLA) signed by both the parties i.e. CSPs and Customers/Clients. As a matter of fact, several trust violations and lack of transparency have been observed in terms of metering of consumed cloud services along with nonfunctional requirements promised (i.e. quality, availability/uptime and greenness) in products/services provided by CSPs over the cloud. In order to establish a satisfactory trust and transparency in billing & metering of services; a trusted environment is needed. As a primary investigation, several discrepancies are observed in billing because SLAs are designed and used by CSPs side only for measurement and billing along with the Quality assurance of Services (QoS). It implies that CSPs have no transparency and monopoly on the measurement and billing systems. This study is an effort to empower the cloud clients/customers through introducing a new client-side metering and billing system model for consumed cloud services. This system can help the customer to cross verify the measured/metered and billed services with promised quality like attributes like greenness. It can assure better transparency and trusted billing. This paper considers the performance, service outage, performance satisfaction, update, violation, and greenness etc. Finally, a Simulated Model for Client-Side Metering and Monitoring of SLA termed as DPS-Yemane-Shareme CSMM Model is developed and validated using E-Draw Max, AppNeta and 42U.
Application of Virtual Simulation Experimental System of Curtain Wall Based on Educational Transformation and Upgrading

The development of experimental course on curtain wall design in universities faced with such difficulties as high construction cost of teaching laboratory, lack of teaching security and poor experiment efficiency. In order to improve the teaching reform of experimental course on curtain wall design and strengthen the cultivation of students' specialty and Information Literacy, the team applied virtual simulation technology to curtain wall experiment teaching. Through the “1+3+1” step-by-step innovative teaching model, a progressive experimental system integrating learning, practice and examination is formed. The results show that the virtual simulation experiment system is superior to the traditional curtain wall design laboratory in terms of experiment efficiency, field scope, experiment completion rate, experiment security and cost control. The combination of Virtual Reality Technology and curtain wall experiment course can improve the teaching effect significantly.
Novel air quality measure method based on image blur metric

A new method to measure air quality using image blur metric was proposed. The classification of blur degree based on local image blocks helps to achieve autonomy selection of the visual attention region. Then the relationship between image blur degree and the concentration of air particulate matters was analyzed. The results show that the correlation between the image blur degree and the PM2.5 concentration is the best, and the correlation coefficient reached 0.76. Therefore the image blur degree evaluation can be used as a measure of air quality.
Research on Critical Illness Fundraising Methods Based on Blockchain and Smart Contracts

At present, with the development of science and medical level, more and more diseases can be cured. However, the cost of treating some diseases is high, and for many families with limited financial resources, it is difficult to spend so much money on treatment in a short period of time. As a result, more and more people are launching fundraising projects through third-party platforms such as Water Drops and Easy Fundraising, using the money raised to treat diseases. However, these third-party platforms have many problems, such as easy to disclose the private information of people who seeking help, the fundraising process is complex and slow, the use of project funds is difficult to timely monitor, the release of false fundraising projects for fundraising fraud, and so on. For these reasons, fewer members of the public will believe the authenticity of fundraising projects, so that it is not easy to donate to fundraising projects, which also makes it difficult for people who really need to raise funds to treat diseases. In view of this situation, this paper puts forward a blockchain and smart contract-based fundraising system for critical diseases, which can solve the problems existing in third-party platforms, and can help helpers quickly raise funds to cure diseases, it also can avoid the abuse of fundraising funds, false fundraising and other acts. Not only improve the transparency of fundraising behavior, but also enhance the enthusiasm of public participation in donations.
Neural Utility Functions

Current neural network architectures have no mechanism for explicitly reasoning about item trade-offs. Such trade-offs are important for popular tasks such as recommendation. The main idea of this work is to give neural networks inductive biases that are inspired by economic theories. To this end, we propose Neural Utility Functions, which directly optimize the gradients of a neural network so that they are more consistent with utility theory, a mathematical framework for modeling choice among items. We demonstrate that Neural Utility Functions can recover theoretical item relationships better than vanilla neural networks, analytically show existing neural networks are not quasi-concave and do not inherently reason about trade-offs, and that augmenting existing models with a utility loss function improves recommendation results. The Neural Utility Functions we propose are theoretically motivated, and yield strong empirical results.
Fast Run-Based Connected Components Labeling for Bitonal Images

Connected Components Labeling (CCL) is a fundamental task in binary image processing. Since its introduction in the sixties, several algorithmic strategies have been proposed to optimize its execution time. Most CCL algorithms in literature, including the current state-of-the-art, are designed to work on an input stored with 1-byte per pixel, even if the most memory-efficient format for a binary input only uses 1-bit per pixel. This paper deals with connected components labeling on 1-bit per pixel images, also known as 1bpp or bitonal images. An existing run-based CCL strategy is adapted to this input format, and optimized with Find First Set hardware operations and a smart management of provisional labels, giving birth to an efficient solution called Bit-Run Two Scan (BRTS). Then, BRTS is further optimized by merging pairs of consecutive lines through bitwise OR, and finding runs on this reduced data. This modification is the basis for another new algorithm on bitonal images, Bit-Merge-Run Scan (BMRS). When evaluated on a public benchmark, the two proposals outperform all the fastest competitors in literature, and therefore represent the new state-of-the-art for connected components labeling on bitonal images.
Research on a method of digital map generation for tracked equipment maneuvering on soft ground

Abstract The mobility of equipment refers to the ability of fast driving under various road, ground and terrain conditions that may be encountered in the tactical background. In recent years, with the rapid development of national defense and aerospace, tracked equipment is required to reach more and more areas. However, due to its low bearing capacity and low adhesion capacity, soft roads such as sandy land, swamp, farmland, tidal flat and lunar soil are easy to cause subsidence, resulting in low driving efficiency or even inability to drive, which seriously restricts the feasibility of driving equipment. In this paper, considering the characteristics of tracked equipment, through the superposition of multi-layer spatial information such as slope, aspect, soil and elevation data, based on equipment dynamics, fuzzy theory and ant colony algorithm, the generation of mobile digital map and mobile path planning of tracked equipment under soft road and complex terrain are studied, which has important practical significance.
Feature Extraction Efficient for Face Verification Based on Residual Network Architecture

Face verification systems have many challenges to address because human images are obtained in extensively variable conditions and in unconstrained environments. Problem occurs when capturing the human face in low light conditions, at low resolution, when occlusions are present, and even different orientations. This paper proposes a face verification system that combines the convolutional neural network and max-margin object detection called MMOD + CNN, for robust face detection and a residual network with 50 layers called ResNet-50 architecture to extract the deep feature from face images. First, we experimented with the face detection method on two face databases, LFW and BioID, to detect human faces from an unconstrained environment. We obtained face detection accuracy > 99.5% on the LFW and BioID databases. For deep feature extraction, we used the ResNet-50 architecture to extract 2,048 deep features from the human face. Second, we compared the query face image with the face images from the database using the cosine similarity function. Only similarity values higher than 0.85 were considered. Finally, the top-1 accuracy was used to evaluate the face verification. We achieved an accuracy of 100% and 99.46% on IMM frontal face and IMM face databases, respectively.
Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

We present an algorithm to estimate and quantify the uncertainty of the accelerometers' relative geometry in an inertial sensor array. We formulate the calibration problem as a Bayesian estimation problem and propose an algorithm that samples the accelerometer positions' posterior distribution using Markov chain Monte Carlo. By identifying linear substructures of the measurement model, the unknown linear motion parameters are analytically marginalized, and the remaining non-linear motion parameters are numerically marginalized. The numerical marginalization occurs in a low dimensional space where the gyroscopes give information about the motion. This combination of information from gyroscopes and analytical marginalization allows the user to make no assumptions of the motion before the calibration. It thus enables the user to estimate the accelerometer positions' relative geometry by simply exposing the array to arbitrary twisting motion. We show that the calibration algorithm gives good results on both simulated and experimental data, despite sampling a high dimensional space.
Phonetic Feature Extraction and Recognition Model in Japanese Pronunciation Practice

In the initial stage of learning Japanese, the most common problem is a variety of mistakes in pronunciation. The main reason for these errors is the difference between Chinese and Japanese in pronunciation position and language system. Language learning should combine theory with practice and spend more time on practice with students. Practice has proved that language mastery requires a lot of practical practice, and too much theory may hinder students' flexible mastery of the language. Some media data, such as audio data, can be converted into time series for research. The similarity measurement (pattern matching) of the converted speech time series can find the similar speech signals, which can also be called speech recognition technology. With the rapid development of intelligent control technology, speech signal processing has attracted extensive attention and high attention of researchers.
Modern trends of electronic educational resources development at the university

The relevance of the development of electronic educational systems has long been one of the main topics for discussion not only at the level of universities, but also at secondary and basic education. Many higher educational institutions in Russia are actively using distance-learning technologies, which bring them to a qualitatively new level of interaction between the teacher and students. The purpose of the study is to analyze the effectiveness of the process of informatization of educational resources in the discipline “Physical culture and sports” at the university for further substantiation of the path of development of the digital environment in this discipline, regardless of the health level of students and their place of stay. The issues of the effectiveness of electronic educational resources in organizational and management activities in the field of physical culture and sports are analyzed. The directions of creation of a unified educational information system for universities with the possibility of maximum use of information resources in pedagogical activity, for specialists in physical culture and sports are considered. Thus, the main challenge for modern education in the field of physical culture and sports is the construction of an adaptive educational system that responds to the transformation of the environment, and the creation of conditions for the implementation of individual (personalized) educational trajectories using modern educational technologies.
A Survey of Ship Detection and Classification Techniques

Ship detection in Synthetic Aperture Radar (SAR) images may be a popular branch of SAR interpretation. A growing number of researchers are devoting themselves to applying Convolutional Neural Network (CNN) to ship detection in Synthetic Aperture Radar. Synthetic Aperture Radar (SAR). Imagery has proven to be a promising data source for the surveillance of maritime activity. In this survey, a detailed review of various deep learning models utilized in ship detection in SAR images was done. Methods are classified consistent with different deep learning architectures. This survey starts with an introduction to ship detection in synthetic aperture radar images and describes and compares various models like CNN, R-CNN, G-CNN, RESNET, etc. Finally it ends with a conclusion and discussion.
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations

Recently introduced self-supervised methods for image representation learning provide on par or superior results to their fully supervised competitors, yet the corresponding efforts to explain the self-supervised approaches lag behind. Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing. The probing tasks require knowledge about semantic relationships between image parts. Hence, we propose a systematic approach to obtain analogs of natural language in vision, such as visual words, context, and taxonomy. Our proposal is grounded in Marr's computational theory of vision and concerns features like textures, shapes, and lines. We show the effectiveness and applicability of those analogs in the context of explaining self-supervised representations. Our key findings emphasize that relations between language and vision can serve as an effective yet intuitive tool for discovering how machine learning models work, independently of data modality. Our work opens a plethora of research pathways towards more explainable and transparent AI.
Depth Estimation Based on Optical Flow and Depth Prediction

Depth sensing is essential for 3D reconstruction and scene understanding. Depth sensors can support dense depth results. But often face the difficult of environmental interference such as the restricted operating range, low spatial resolution, interference from other sensor, and the high cost of power. In this paper, we try to estimate per-pixel depth and its uncertainty continuously from video stream and the optical flow based on deep learning (DL) method. This can turn the RGB video stream to RGB-D video stream. It is different from normal deep learning methods as we try to get the depth probability distribution for each pixel rather than a single depth value of the graph. These depth probability volumes are amassed over time under a Bayesian filtering framework as more incoming frames and optical flow graph are processed sequentially. This effectively reduces depth uncertainty and improves accuracy, robustness, and temporal stability. Compared to prior work, the proposed approach achieves more accurate and stable results, and generalizes better to new datasets.