Reviewerly/exHarmony huggingface dataset:
Modeling Viewpoint of Forklift Operators Using Context-Based Clustering of Gaze Fixations

This study proposes a method to find optimal viewpoints for Human Machine Interface (HMI) of teleoperation system using human visual pattern. Forklift operation is used as a case study due to its operation complexity consisting of multiple work contexts such as driving and cargo handling. It is challenging to model human viewpoint because there is usually no prior knowledge of the behavior, complicated steps to process behavioral data, and difficulty to represent dynamic behavior throughout the operation. Therefore, a method is proposed to model human viewpoint using setups in a virtual environment that is reconstructed from a real laboratory environment mimicking the warehouse. Gaze points are measured during experiments in virtual environment, and the clustering methods are used to find natural spherical clusters resembling human foveal vision at different work contexts. Viewpoints of several category of forklift operators are derived from the proposed method, and their common viewpoints at each work context is represented using a piecewise function defining distribution of cluster centroids from the origin of local coordinate system. Spatial analysis of gaze pattern suggests distribution of gaze centroids are generally spatially independent between work contexts. More importantly, the common viewpoint is spatially correlated with the viewpoints of different category of operators. This suggests the proposed model is representative of the general viewpoint of forklift operation.
Monocular Vision Obstacle Avoidance UAV: A Deep Reinforcement Learning Method

In this paper, a method based on deep reinforcement learning (DRL) is proposed, which allows unmanned aerial vehicles (UAVs) to complete obstacle avoidance tasks only through vision in an environment full of common indoor obstacles. This technology is very important for indoor UAVs, due to the limited GPS signal and overcrowding of obstacles compared to the outdoor environment. We use Variational Autoencoder (VAE) to compress image information combined with the policy-based DRL model to implement the visual obstacle avoidance of VAVs. Simulation experiments have demonstrated that this method can make the UAV master obstacle avoidance in a continuous action space with a fixed direction. Compared with the traditional policy-based DRL visual obstacle avoidance algorithms, it can converge faster.
Intention-oriented Hierarchical Bundle Recommendation with Preference Transfer

Bundle recommendation offers promotions of bundled items instead of a single one, which is a common strategy for sales revenue increase and latent customer mining. Due to the scarcity of user-bundle interactions, it is compulsory to go beyond modeling user-bundle interactions and take user-item interactions into account. Existing studies consider user-item interactions by sharing model parameters or learning representation in a multi-task manner or modeling representation based on graph neural network. However, such methods ignore the mutual influence between user preferences for items and bundles. Moreover, they fail to analyse the intentions behind users' purchase behaviors, which can be utilized to make better bundle recommendation. To overcome the drawbacks of existing studies, we propose a novel model IHBR (Intention-oriented Hierarchical Bundle Recommendation with Preference Transfer). Specifically, we consider the co-purchase and co-occurrence information within items for modeling intention-oriented hierarchical representations. Furthermore, we provide a new perspective to exploit mutual influence between user preferences for items and bundles. The experimental results obtained on two real-world datasets demonstrate that our method outperforms the state-of-the-art baselines.
Modeling Movement Times and Success Rates for Acquisition of One-dimensional Targets with Uncertain Touchable Sizes

In touch interfaces, a target, such as an icon, has two widths: the visual width and the touchable width. The visual width is the target's appearance, and the touchable width is the area in which users can touch a target and execute an action. In this study, we conduct two experiments to investigate the effects of the visual and touchable widths on touch pointing performance (movement time and success rate). Based on the results, we build candidate models for predicting the movement time and compare them by the values of adjusted R^2 and AIC. In addition, we build a success rate model and test it through cross-validation. Existing models can be applied only to situations where the visual and touchable widths are equal, and we show that our refined model achieves better model fitness, even when such widths are different. We also discuss the design implications of the touch interfaces based on our models.
Analysis of Requirements for Academic Staff at Higher Education Institutions

The purpose of this study is to analyse the modern requirements for the teaching staff. The article examines the regulatory requirements for various positions of the teaching staff, analyses 338 vacancies of Russian universities on one of the largest job search sites and the most relevant domestic and foreign research on this topic. The methodological basis of the study is the results of monitoring and analysis of regulatory requirements for the teaching staff and current research on the designated topic, as well as the results of content analysis of current vacancies for 35 qualification and competency requirements, depending on the position on the search site work “hh.ru”. As a result of the study, the author was able to identify the most relevant requirements of various stakeholders to the teaching staff. The results of the study can be used by university leaders to improve activities related to assessing the quality of employees, by candidates for faculty positions in order to identify individual points of growth, and can also serve as a basis for further research in this area.
Vision ray metrology for freeform optics

Vision ray techniques are known in the optical community to provide low-uncertainty image formation models. In this work, we extend this approach and propose a vision ray metrology system that estimates the geometric wavefront of a measurement sample using the sample-induced deflection in the vision rays. We show the feasibility of this approach using simulations and measurements of spherical and freeform optics. In contrast to the competitive technique deflectometry, this approach relies on differential measurements and, hence, requires no elaborated calibration procedure that uses sophisticated optimization algorithms to estimate geometric constraints. Applications of this work are the metrology and alignment of freeform optics.
Bangla↔English Machine Translation Using Attention-based Multi-Headed Transformer Model

Machine Translation (MT) refers to translate texts or documents from the source language into the target language without human intervention.Any MT model is language-dependent and its development requires grammar, phrase rules, vocabulary, or relevant data for the particular language pair.Hitherto, little research on MT for Bangla-English is reported in the literature, although Bangla is a major language.This study presents a deep learning-based MT system concerning both-way translation for the Bangla-English language pair.The attention-based multi-headed transformer model has been considered in this study due to its significant features of parallelism in input processing.A transformer model consisting of encoders and decoders is adapted by tuning different parameters (especially, number of heads) to identify the best performing model for Bangla to English and vice versa.The proposed model is tested on SUPara benchmark Bangla-English corpus and evaluated the Bilingual Evaluation Understudy (BLEU) score, which is currently the most popular evaluation metric in the MT field.The proposed method is revealed as a promising Bangla-English MT system achieving BLEU scores of 21.42 and 25.44 for Bangla to English and English to Bangla MT cases, respectively.
Extended Reality for Knowledge Work in Everyday Environments

Virtual and augmented reality (VR and AR) have the potential to change information work. The ability to modify the workers senses can transform everyday environments into a productive office, using portable head-mounted displays (HMDs) combined with conventional interaction devices, such as keyboards and tablets. While a stream of better, cheaper, and lighter HMDs has been introduced for consumers in recent years, there are still many challenges to be addressed to allow this vision to become reality. This chapter gives an overview of the state of the art in the field of extended reality for knowledge work in everyday environments, identifies challenges and proposes steps to address the open challenges.
A Constraint Satisfaction Service Composition Method Supporting One to Many Task Pattern

The current service composition problem is usually aimed at the constrained service composition problem of Web services. However, this kind of scheme is not suitable for the situation that one service corresponds to multiple tasks. In order to solve the problem of service composition meeting business constraints and users’ needs in a specific field, this paper uses Markov decision process to model the problem, proposes a method based on deep Q-learning to solve the problem, and uses random sampling method for the inference. This method calculates the candidate services that can be used for composition, and takes the maximum cumulative rewards represented by the degree of constraint satisfaction as the optimization objective, and globally optimizes the results to meet the needs of users to the greatest extent. The experimental results show that: in this problem, compared with the existing methods, this method has higher combination efficiency.
Analysis of Causes and Effects of Longer Lead Time in Software Process Using FMEA

Longer lead time in small and medium enterprises results in direct impact on delivering the project at the stipulated time period, hence resulting in more costs involved. The reason for the delay in the lead time is because of the various ‘waste’ factors which can be analyzed by using Failure Mode Effective Analysis (FMEA). The paper discusses on the steps used in FMEA right from listing of potential failure modes to taking necessary actions in eliminating or reducing the high-risk failure modes.
FPNet: A Deep Light-Weight Interpretable Neural Network Using Forward Prediction Filtering for Efficient Single Image Super Resolution

The requirements of light-weight and low-power of portable devices in applications involving super resolution make it necessary to design the underlying algorithms with small number of parameters. In this brief, based on the idea of forward prediction of adaptive signal processing, a novel super block is developed for the task of image super resolution by a light-weight neural network. The design of the super block is based on using a sequence of dense residual blocks and recalibrating their outputs by a squeeze-and-excitation unit, in order to implement the idea of forward prediction. It is shown that a network that employs our super block provides a performance superior to that of the other light-weight deep networks for the task of image super resolution.
Simulation Annealing Based Approach to Enhanced Load Balancing in Cloud Computing

In today's environment as demand of users to access services or data and manage that data in easily manner is challenging issue so that to overcome this problem cloud computing is widely used. CC provides various services and space to store data to the users. Due to heavy load on cloud as number of similar requests received a cloud in seconds is very challenging task. To handle dynamic load is very challenging for cloud servers. To overcome this problem in this paper we propose a Simulation Annealing SA based approach to dynamically distribute loads to the cloud so that throughput may increases.
A Modified Cuckoo Search for the n-Queens Problem

A modified cuckoo search algorithm is proposed in this paper to solve the n-queens problem. For solving the problem, the host nests are treated as permutations of a cluster of distinctive values. The main idea is that the cuckoos change their eggs (permutations) randomly using Lévy flight approach without affecting the distinctive characteristics of the solution. During evolution of the optimal solution, a cuckoo search parameter, namely fraction of worst nests Fw, is also fine-tuned. The results obtained show that the proposed cuckoo search method is favorable in solving the similar constraint satisfaction problem.
Bi-RRT* Based Trajectory Optimization and Obstacle Avoidance for a Serial Manipulator

Nowadays, due to the rapid development and application of robotics and artificial intelligence technology, the collision-free operation of robotic manipulators has attracted a great deal of interest in the industrial manufacturing field. In complex manufacturing situations catering to one specific task, one critical issue is to develop an obstacle-free trajectory for a manipulator. In this research, we adopt one novel trajectory planning method based on the Bi-RRT algorithm to achieve a smooth and fast planning process for a serial manipulator. We first build a distance estimation and collision detection model utilizing the point cloud data. Different from the previous research of utilizing the mesh information of dynamic objects, only the position information will be used, of which is simple to develop. Moreover, GPU and parallel programming will also be applied to accelerate the procession of distance calculation. Second, a collision-free global path will be generated by using a random sampling method, namely RRT-connect algorithm, which will largely improve the convergence speed of finding a feasible path from start to finish while also avoiding obstacles. Third, a smooth trajectory will be generated via quintic polynomial interpolation. This method guarantees that the trajectory's acceleration will remain constant during the whole planning process. Finally, we conduct a numerical test on a UR5 manipulator, with an efficient solution being provided to the problem of collision-free trajectory generating for a manipulator.
Optimization of concentrically braced steel frame structures based on SNI 1726:2019, SNI 1727:2020, SNI 1729:2020, and AISC 341-16

Abstract Damages resulted from earthquakes are a loss in the economic sector. The structure of multi-story buildings needs an earthquake-proof design with higher performance to reduce such losses. By utilizing the metaheuristic algorithm, this study aims to identify the most compatible brace configuration and profile used in a concentrically braced steel frame structures with minimal total weight and that will meet the safety requirements. This algorithm is suitable owing to the fact that it is able to find solutions to any known optimization problem either through Particle Swarm Optimization (PSO), Symbiotic Organisms Search (SOS), or Differential Evolution (DE). The performance of these algorithms will demonstrated in a form of comparison through a case study of optimizing a 5-span, 6-story steel frame structure. These systems will determine the lightest frame weight, which also correlates to a lower construction cost, without compromising the constraints of SNI 1726:2019, SNI 1727:2020, SNI 1729:2020, and AISC 341-16. Based on the results of data processing, SOS is shown to achieve the highest algorithm performance compared to PSO and DE.
Automated Attendance Marking System Using Computer Vision and Deep Learning

The advancement in the history of computer vision utilizing deep learning approaches especially convolutional neural networks have accomplished to solve difficult problems in face recognition field. Face recognition-based approach is one amongst the important identification methods which can be used as a possible substitution for conventional system of marking attendance manually, especially if a huge classroom of students is addressed for an hour session. In this proposed paper, an automated attendance system is implemented utilizing algorithms such as Histogram of Oriented Gradients (HOG) and Face landmark Estimation for face detection and finally, the deep convolutional neural network is trained for face recognition. In this system, a database containing the student’s images is created. A camera installed in the classroom captures the image of all the student in the classroom. This image is processed using machine learning algorithms to detect faces and to mark the attendance automatically in an excel sheet.
Tendencies in Augmented Reality

The expansion of the internet and new information and communication technologies have changed people’s lives in many ways. Augmented Reality (AR) is a promising alternative in the midst of the great technological revolution. These technologies are projected in the field of decision-making as a trend that could transform current practices. The objective of this research is to create a theoretical framework for the subject that allows researchers to analyze trends in the methods for using augmented reality, the use strategies used in research and the main areas of application. As conclusions, is identified the need to improve methods to use augmented reality, as well as the possibility of using augmented reality in decision-making and in project management.
Structure-aware Loss Function for Ultrasound Image Segmentation

In deep learning, loss function plays a crucial role in training an effective neural network model. In the task of ultrasound image segmentation, the pixel-wise loss functions such as cross-entropy loss and dice loss are usually used to train a deep neural network model. These loss functions only count the distribution differences between the predicted results of the model and the ground-truth labels at pixel level, but do not pay attention to the consistence of the spatial relations between different tissues in the predicted results and the real images. In order to improve the semantic expression of the loss function in measuring the difference between the predicted results and the ground-truth labels, we use the concept of relative fuzzy connectedness to add the relative position relationship between tissues in ultrasound images to the loss function as a prior knowledge.
Efficient Resnet Model for Atmospheric Visibility Classification

Atmospheric visibility is an important element of meteorological observation. With existing methods, defining image features that reflect visibility accurately and comprehensively is difficult. This paper proposes a visibility detection method based on a deep learning model termed Resnet that addresses issues caused by a lack of sufficient visibility labelled datasets. The proposed model is used for calculating the visibility of external geo-tagged images directly without relying on weather images or data that require costly sensing or customized capturing. A large collection of internet photographs is used for the current data-driven approach to learning rich scene and visibility variations. Finally, the performance of Resnet is compared over the different variant of Resnet and other deep learning models in terms of diverse performance metrics and shows the ability of proposed Resnet model in atmospheric visibility detection.
Design of a Secure Blockchain Based Privacy Preserving Electronic Voting System

Blockchain is an emerging technology, which offering numerous opportunities to develop decentralized and distributed digital services by ensuring privacy and transparency. It has mainly concentrating on the legal and technical issues rather developing advanced digitized services. In this article, we make use of the smart contracts with Blockchain to design the secure electronic voting system. The aspect of privacy, authenticity, transparency and security is a threat and challenging in the traditional voting systems. In general, mostly elections is based on the centralized infrastructure consists of central entity that maintains over all the voting process. The major pitfalls in the existing E-voting infrastructure is with an entity that has full influence over the system, it is feasible to modify with databases of considerable opportunities. In addition, the paper based voting systems are assisted by Electronic Voting Machines (EVMs) have multiple vulnerabilities, which can be caused to election rigging, fraudulent intent of the third party entities and government. The decentralized public Blockchain technology might offers a scalable solution to current voting systems by providing trust based and fraud proof digital voting.
Research on Decision–Making Method of Air Combat Embedded Training Based on Extended Influence Diagram

In recent years, it is difficult for pilots of domestic air force to complete combat mission training efficiently. Thus, embedded training system of combat aircraft in the modern war rises at this moment. Research content of this paper mainly focuses on structures of the embedded training system. For system analysis, method that based on the influence diagram shall be selected. Through fully considering and making full use of environment state information of air combat training, situation domain method which can better reflect the pilot against thinking should be selected and adopted in situation assessment module construction. Fully consider limitations of existing independent decision-making algorithm, to analyze realize function of independent decision-making module and construct the influence diagram. In view of construction defects of traditional influence diagram, to study the expanded method ad put forward reasonable calculating method. Finally, according to above mentioned structure design, implement the simulation analogue of the embedding training system, and conduct test analysis in view of various adverse situation. It can solve practical problems such as the large calculation amount and poor adaptability of the existing decision-making algorithms, thereby improving the authenticity and practicality of the embedded training system.
A Knowledge Representation Method of Joint Entity and Neighbor Information

The classical knowledge representation model is usually based on a single triplet, however, the knowledge graph is a complex network that could not accurately represent the knowledge of knowledge graphs by just a single triplet. This paper presented a knowledge representation method of joint entity and neighbor information, which extracts the hidden neighbor information around the entity from the knowledge graphs and represents it jointly with the vector of the entity itself. Finally, we use the classical translating model to the further representation training. This model uses a concise and effective method to aggregate the neighbor node information, and the performance in the experiment is more efficient than the traditional translation model.
Prototype of Force Feedback Tool for Mixed Reality Applications

This prototype demonstrates the viability of manipulating both physical and virtual objects with the same tool in order to maintain object permanence across both modes of interaction. Using oppositional force feedback, provided by a servo, and an augmented visual interface, provided by the user’s smartphone, this tool simulates the look and feel of a physical object within an augmented environment. Additionally, the tool is also able to manipulate physical objects that are not part of the augmented reality, such as a physical nut. By integrating both modes of interaction into the same tool, users can fluidly move between these different modes of interaction, manipulating both physical and virtual objects as the need arises. By overlaying this kind of visual and haptic augmentation onto a common tool such as a pair of pliers, we hope to further explore scenarios for collaborative telepresence in future work.