Reviewerly/exHarmony huggingface dataset:
Robust and High-Precision Vision System for Deflection Measurement of Crane Girder With Camera Shake Reduction

Crane girder is subject to alternating load in long time, which makes it prone to fatigue and fracture. Vision deflection measurement has become an effective method for structural health monitoring. However, crane girder does not always exhibit high contrast feature and artificial markers have to be pasted. Existing artificial markers cannot perform well in poor light conditions and the accuracy is limited. In this paper, we design an anti-interference marker by pasting a film onto a circular surface light source. The marker allows the vision system to work well under different light conditions. In addition, the reduction of camera shake and sub-pixel technique have been studied to improve the accuracy. According to the characteristics of the marker, corresponding image processing algorithms are also studied. Firstly, the designed marker can suppress environmental interference even at long distances with a wireless module and a small aperture. Secondly, the center of the marker in the image can be calculated using a sub-pixel detection technique. Finally, a camera shake reduction method is applied by using multiple reference markers and scale conversion factors are taken into account to get the physical displacement. To verify the measurement results, two experiments were carried out using a digital dial gauge sensor and laser displacement sensor for comparison. The experimental results proved the robustness and high precision of the vision system.
Classification of glomerular spikes using Convolutional Neural Network

Membranous nephropathy (MN) is a common cause of adult nephrotic syndrome. In clinical, MN is diagnosed by pathological biopsy. Under the light microscope, one of the main features is that a large number of immune complex deposits can be seen on the epithelial side of the glomerular capillary loop, which is called spikelike projections. However, the spikes are very small and the diagnosis of spikes requires pathologist's experience, so it is easy to cause missed diagnosis. In this paper, we proposed a model, SPIKE-NET, for the classification of glomeruli, which combine the U-Net and the ResNet. U-Net is used as a data preprocessing step to segment the glomeruli, and ResNet is used to classify the spiked glomeruli and normal glomeruli. We established a dataset containing 1267 images of glomeruli stained with PASM. The F1 of SPIKE-NET reaches 0.9448. The higher Recall of the model means a lower missed diagnosis rate, which is of great significance in clinical diagnosis and provides a good foundation for the intelligent auxiliary diagnosis of membranous nephropathy.
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning

Adversary emulation is an offensive exercise that provides a comprehensive assessment of a system's resilience against cyber attacks. However, adversary emulation is typically a manual process, making it costly and hard to deploy in cyber-physical systems (CPS) with complex dynamics, vulnerabilities, and operational uncertainties. In this paper, we develop an automated, domain-aware approach to adversary emulation for CPS. We formulate a Markov Decision Process (MDP) model to determine an optimal attack sequence over a hybrid attack graph with cyber (discrete) and physical (continuous) components and related physical dynamics. We apply model-based and model-free reinforcement learning (RL) methods to solve the discrete-continuous MDP in a tractable fashion. As a baseline, we also develop a greedy attack algorithm and compare it with the RL procedures. We summarize our findings through a numerical study on sensor deception attacks in buildings to compare the performance and solution quality of the proposed algorithms.
Automatic Recommendation of a Distance Measure for Clustering Algorithms

With a large number of distance measures, the appropriate choice for clustering a given data set with a specified clustering algorithm becomes an important problem. In this article, an automatic distance measure recommendation method for clustering algorithms is proposed. The recommendation method consists of the following steps: (1) metadata extraction, including meta-feature collection and meta-target identification; (2) recommendation model construction using metadata; and (3) distance measure recommendation for a new data set by the recommendation model. Two different types of meta-targets and meta-learning techniques are utilized considering the possible different requirements of users. To validate the necessity and effectiveness of the distance measure recommendation method, an empirical study is conducted with 199 publicly available data sets, 9 distance measures, and 2 widely used clustering algorithms. The experimental results indicate that distance measure significantly influences the performance of the clustering algorithm for a given data set. Furthermore, performance analysis of the proposed recommendation method proves its effectiveness.
Linguistic Profiles in Biometric Security System for Online User Authentication

A typical biometric system aims to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB), being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users' writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on the writing profiles of OSN users is proposed. The developed system is cross-validated on a social interaction database of 241 Twitter users. The rank-1 identification rate from users' writing profiles is 91.70% and the rank-8 identification rate is 99%. Furthermore, the experimental results establish that the users' writing profiles have the highest impact over other social biometric features.
Distance learning during self-isolation: comparative analysis

Abstract The article provides a comparative analysis of the methods of teaching mathematics to university students of the traditional face-to-face form of education and in the form of distance learning using electronic teaching aids. To compare these forms the main elements that make up the teaching methodology were highlighted: teaching methods, levels of mastering the content of education, the type of educational situation, types of feedback. Each element of the methodology was presented in the form of a hierarchical subsystem performing teaching functions at different levels of assimilation. In the course of the conducted polls of the students and teachers it was found out that the traditional teaching methodology worsened its didactic properties with the transition to a distance form. Therefore, in order to increase the effectiveness of teaching it is necessary to transfer the methodology to new principles of cyberpedagogy and create a closed educational process with a directed information flow.
The Development of 3D Object Modeling Techniques for Use in the Unity Environmen

Today, computer graphics is one of the most rapidly developing areas of information technology. In the computer graphics industry, researchers assign a large place to the creation of characters as carriers of images that convey the necessary information to the viewer. At the moment, there is no single method for creating 3D objects from the moment of modeling the form to its final use. Only scattered information in the form of lessons, tips on forums and video tutorials exists. It is a big problem for novice specialists to collect this information and systematize it for successful use in their work. The goal of the research is to create a methodology for creating 3D objects for later use in the Unity environment, which could help young professionals speed up their understanding of modeling. The subject of the research is the modeling of 3D objects for use in the development of computer games. The object of the research is the method of creating a 3D object. Therefore, in this article, we will consider the development of a multimedia application using the example of an augmented reality mobile application using the Unity game engine. In the article, we will consider modeling based on the method of vertex extrusion.
Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

The signal-to-noise ratio of lidar signals decreases rapidly with an increase in distance, which seriously affects the application of lidar detection technology.Variational mode decomposition (VMD) has performed optimality in dealing with noise, but the number of modes, K, and the penalty parameter, α, must be preset.Therefore, a novel lidar signal denoising method that combines VMD with machine learning online optimization (MLOO) and the interval thresholding (IT) technique, named VMD-MLOO-IT, is proposed in this paper.The proposed method defines new fitness functions to evaluate the result of VMDbased denoising, and selects the optimal parameters by the model which development by MLOO.In addition, IT is used to deal with the recovered signal.The experimental results demonstrate the superiority of the presented method over the other empirical mode decomposition-based and VMD-based denoising methods.
Gaze, head and eye movements during somersaults with full twists

Somersaults with or without twists are the most important elements in sports such as gymnastics or trampolining. Moreover, to perform elements with the highest possible difficulty gymnasts should show good form and execution during the flight phase. In order to ensure perfect body control and a safe landing, gaze behavior has been proven to be crucial for athletes to orientate in the air. As eye movement and head movement are closely coordinated, both must be examined while investigating gaze behavior. The aim of the current study is to analyze athletes' head motion and gaze behavior during somersaults with full twists. 15 skilled trampoline gymnasts performed back straight somersaults with a full twist (back full) on the trampoline. Eye movement and head movement were recorded using a portable eye-tracking device and a motion capture suit. The results indicate that gymnasts use the trampoline bed as a fixation point for orientation and control the back full, whereas the fixation onsets for athletes of a better performance class occur significantly later. A strong coordination between gymnasts' eye movement and head movement could be determined: stabilizing the gaze during the fixation period, the eyes move in combination with the head against the twisted somersault direction to counteract the whole body rotation. Although no significant differences could be found between the performance classes with regard to the maximum axial head rotations and maximum head extensions, there seems to be a trend that less skilled gymnasts need orientation as early as possible resulting in greater head rotation angles but a poorer execution.
Efficient and Secure Implementation of BLS Multisignature Scheme on TPM

In many applications, software protection can not be sufficient to provide high security needed by some critical applications. A noteworthy example are the bitcoin wallets. Designed the most secure piece of software, their security can be compromised by a simple piece of malware infecting the device storing keys used for signing transactions. Secure hardware devices such as Trusted Platform Module (TPM) offers the ability to create a piece of code that can run unmolested by the rest of software applications hosted in the same machine. This has turned out to be a valuable approach for preventing several malware threats. Unfortunately, their restricted functionalities make them inconsistent with the use of multi and threshold signature mechanisms which are in the heart of real world cryptocurrency wallets implementation. This paper proposes an efficient multi-signature scheme that fits the requirement of the TPM. Based on discrete logarithm and pairings, our scheme does not require any interaction between signers and provide the same benefits as the well established BLS signature scheme. Furthermore, we proposed a formal model of our design and proved it security in a semi-honest model. Finally, we implemented a prototype of our design and studied its performance. From our experimental analysis, the proposed design is highly efficient and can serve as a groundwork for using TPM in future cryptocurrency wallets.
CPIN: Comprehensive present-interest network for CTR prediction

Personalized recommendation is a popular research direction in both industry and academia. Some research on recommender systems utilizes the users' interaction history on items to represent the users' interests, which has achieved remarkable success. Users' interests in the real world are dynamically changing and have a strong correlation with the interaction sequence. However, sometimes users' interests are less relevant to the order of the current interaction sequence, but are more relevant to certain items in the user interaction history. In this paper, a novel deep neural network model is proposed to deal with this situation. The developed model consists of two parts: the present interest relevant to the order of the interaction sequence and the comprehensive interest relevant to some items in the interaction sequence. An ancillary multi-layer perceptron (MLP) is constructed to improve the training of our model. Experiments on public and industrial datasets are conducted. The experimental results show that our proposed model outperforms the state-of-the-art models which demonstrates the effectiveness of the ancillary MLP.
Psychological Determinants of Consumer’s Usage, Satisfaction, and Word-of-Mouth Recommendations Toward Smart Voice Assistants

AI-based voice assistant (VA) technologies are facing an unprecedented growth. VA are available as a standalone device like Amazon Echo dot or Google home and also as an extension such as Google maps and OK Google. Extant research has mostly focused on the device specific characteristics to explain the adoption of VA. In this research, we take a different approach and examine the psychological determinants of VA adoption. We look at how factors such as playfulness, escapism, anthropomorphism, and visual appeal of VA influence the attitudes (hedonic and utilitarian) of consumers. Moreover, we also examine the effects of psychological characteristics of VA on usage intentions and satisfaction, which lead to a favorable word-of-mouth (WOM) behavior that is critical for adoption of a technology. Using a structural equation modeling approach, our results suggest that psychological factors have a significant positive influence on both attitudes. Hedonic attitude further influences satisfaction and utilitarian attitude positively impacts usage and satisfaction, which have a positive association with WOM. Our research offers useful insights to marketers to increase the VA adoption and makes contributions to the literature.
Developing voice-based information sharing services to bridge the information divide in marginalized communities: A study of farmers using IBM’s spoken web in rural India

Farming communities in developing countries like India are victims of the information divide. Voice-based information sharing services (VISS) can bridge this divide and help farmers address a range of challenges by exchanging knowledge with their peers. An analysis of in-depth phone interviews with farmers in remote, rural Gujarat, India, reveals four stages for developing a VISS for marginalized communities in developing countries. The four stages are: identify gatekeepers to seed the VISS, expand the VISS by building on existing communities of practice, share information, and incentivize members to sustain the VISS. The VISS in this study is based on IBM's Spoken Web platform, which enables even illiterate and semi-literate farmers to seek, share, use, and benefit from information using feature mobile phones. Research contributions and practical implications of the study are discussed at the end.
Click Through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting

Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be do... | Find, read and cite all the research you need on Tech Science Press
A three-dimensional measurement system calibration method based on red/blue orthogonal fringe projection

Structured light measurement system that consists of a camera and a projector has been widely used in three-dimensional (3-D) shape measurement. The premise of measurement is to calibrate the structured light system, and the calibration accuracy directly affects the accuracy of 3-D measurement system. The traditional calibration board-based methods suffer from either complex operations or uncompetitive accuracy. To overcome these limitations, this paper proposes a novel calibration method by introducing a liquid crystal display (LCD) with holographic projection film as a phase target to simplify the operations and improve the speed and accuracy of system calibration. The proposed calibration procedures are given as follows: Firstly, the LCD is turned on and kept still to display the red/blue orthogonal fringe pattern, and it is then turned off to reflect the projected fringe pattern with the holographic projection film, the brightness and frequency of the two patterns are adjusted and captured; Secondly, the accurate absolute phase maps are obtained using optimized phase calculation and overall error compensation method; Finally, the above two steps are repeated at several different positions and orientations in the measured field of view. The one-to-one correspondence between the camera and the projector pixel coordinate system and the phase target world coordinate system is established by using the phase equality, and the intrinsic and extrinsic parameters of the system are obtained to reconstruct 3-D shape of the objects. The experiment results demonstrate that the proposed method is feasible and the equipment is simple, which can effectively reduce the overall error, ensure the synchronous calibration of the camera and the projector, avoid the problem of time-consuming, and eventually improve the accuracy of the system calibration.
Synthesis of CNOT-Dihedral circuits with optimal number of two qubit gates

In this note we present explicit canonical forms for all the elements in the two-qubit CNOT-Dihedral group, with minimal numbers of controlled-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi></mml:math>(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi><mml:mi>S</mml:mi></mml:math>) and controlled-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi></mml:math>(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi><mml:mi>X</mml:mi></mml:math>) gates, using the generating set of quantum gates<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo stretchy="false">[</mml:mo><mml:mi>X</mml:mi><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>C</mml:mi><mml:mi>X</mml:mi><mml:mo>,</mml:mo><mml:mi>C</mml:mi><mml:mi>S</mml:mi><mml:mo stretchy="false">]</mml:mo></mml:math>. We provide an algorithm to successively construct the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qubit CNOT-Dihedral group, asserting an optimal number of controlled-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi></mml:math>(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi><mml:mi>X</mml:mi></mml:math>) gates. These results are needed to estimate gate errors via non-Clifford randomized benchmarking and may have further applications to circuit optimization over fault-tolerant gate sets.
WITHDRAWN: Secure data storage and accessing in cloud computing using enhanced group based cryptography mechanism

Cloud computing (CC) has become generally recognised as a computing model. CC 's key purpose is to reduce the need for consumer expenditure in new hardware or applications by providing scalable cloud resources , allowing consumers to take advantage of the pay per use strategy. CC ensures that certain protection and privacy concerns be addressed: all challenges and remedies (vulnerabilities, risks and attacks). The study addresses both these problems and solutions as protection concerns, safety issues or interlinked defence and safety issues. The report explores both the problems and solutions. The study primarily makes two contributions: firstly, the categorization of problems mentioned above; and secondly, literary analysis of the CC protection and privacy problems within the categorization context. The key lessons gained during the study include verifying the decisive position played by consumer protection and privacy technologies in the implementation of CCs; recognising the various flaws, risks and attacks; and recognising tests. Furthermore, the sheer number of confidence references (as regards both challenges and solutions) revealed that CC plays a major role in confidence. In this paper, we are proposed a secure cloud storage access mechanisms based on polling or getting user access based on game theory. Here for protecting data we use cryptographic mechanism.
Defect Prediction via LSTM Based on Sequence and Tree Structure

With the ever-expanding spread of contemporary software, software defect prediction (SDP) is attracting more and more attention. However, sequential networks used in previous studies, weaken syntactic information and fail to capture longdistance dependencies. To solve these problems, we develop a long short-term memory network based on bidirectional and tree structure (LSTM-BT). Specifically, LSTM-BT combines bidirectional long short-term memory networks (BI-LSTM) and tree long short-term memory networks (Tree-LSTM) to capture semantic and syntactic features from source codes. First, token vectors are captured from the abstract syntax tree (AST). Second, an embedding layer is used to extract semantic information hidden inside the AST nodes. Last, features are fed to the LSTM- BT, which is used to conduct predictions of defect-proneness. To validate our method, we carried out experiments on 8 pairs of Java open-source projects and the results show that LSTM- BT performs better compared to several state-of-the-art defect prediction models.
Using Eye-tracking Technologies in Vision Teachers’ Work – a Norwegian Perspective

To support specialists assessing children's vision is essential. However, it is difficult to determine a common support due to the missing standardized methods and tools they are using for their assessments. This study aims to investigate how affordable eye-tracking (ET) technologies can help vision teachers to determine children's oculomotor dysfunction (OMD) and to find out how the teachers can integrate this technology in their screening procedures The evaluation is based on an experimental study demonstrating the use of ET-based software measuring OMD and responses from eight vision teachers testing the technology. The results illustrate the benefits and current limitations of technologies and argue for the need for such technologies.