Reviewerly/exHarmony huggingface dataset:
The technology of augmented reality based on 3D modeling to improve special skills for vocational students in the era of industrial revolution 4.0

Abstract Currently, the development of virtual learning technology continues to increase. Virtual technology such as augmented reality is becoming a sophisticated learning innovation. On the other hand, the special abilities of vocational education graduates have decreased relatively. This study aims to: 1) map the components of the need to improve special skills in vocational education graduate students; 2) develop 3D-modeling based augmented reality learning technology; and 3) testing the attractiveness of the 3D-modeling based augmented reality learning technology. The method used is the research and development (R&amp;D) method. Requirement component map data was taken using qualitative methods through questionnaires and observations. Informants at this stage were students in the field of vocational education at State Universities in East Java. Furthermore, expert tests were conducted by material experts in the field of vocational education and online learning media experts. The results of this study include 1) map of the components of the need to improve special skills in vocational education student graduates consisting of idea development (78%), utilization of infrastructure (82%), deepening knowledge (90%), professional organization (88%), and mental stimulation (90 %); 2) the developed 3D-modeling based augmented reality learning technology has an excellent level of attractiveness and feasibility based on online learning media experts and material experts; and 3) learning technology products based on 3D-modeling augmented reality which is developed can increase the special skills of graduate vocational education students.
<scp>Dare‐to‐Share</scp>: Collaborative <scp>privacy‐preserving</scp> recommendations with (almost) no crypto

Abstract Collaborative recommending systems aim to predict a potential user‐item rating on the basis of remaining ones. Since, in several contexts, sharing of other users' ratings may be prevented by confidentiality concerns, several works have effectively addressed the design of privacy preserving recommenders. Still, most of the proposed solutions rely on advanced cryptographic methodologies, whose may conflict with the simplicity and viability requirements of real world deployments. In contrast, we propose an approach which does not require any complex cryptography. We show that whenever we can tolerate recommendations based on average values, we can transform the recommender into a privacy‐preserving one, by using two noncolluding replicas of the same system, and by distributing randomly “blinded” data to these replicas. To protecting each user's rating, a key asset of our approach is the ability to conceal which specific items are rated by which users. Our proposal is secure under the honest‐but‐curious attacker's assumption, and we show how it can be extended to guarantee robustness also against malicious adversaries. Finally, as a proof‐of‐concept, we present an implementation of the proposed approach for our motivating use case—collaborative assessment of computer/network vulnerabilities without revealing which of them affect one own infrastructure.
Crowd density estimation via a multichannel dense grouping network

Deep learning has made substantial progress in crowd density estimation, but in practical application, due to the interference factors such as uneven distribution of crowd and changes in illumination, the existing methods still have large errors in counting. To solve the above problems, a crowd density estimation method based on multichannel dense grouping network is proposed. To solve the problem that it is difficult to extract the multiscale information of the crowd due to the uneven distribution of the crowd, multichannel dense grouping module (McDGM) is designed. In the module, Improved grouping convolution block (IGCB) is dense connected with other layers to obtain different levels of crowd characteristics, so as to avoid the loss of multi-scale information. In addition, the parameters of IGCB are reduced by group convolution. Then, to overcome the change of illumination, a crowd image enhancement algorithm is designed, which makes the image clear by the average pixel value and adjusting the contrast. Finally, to enhance the sensitivity of the network to crowd counting, a new loss function is proposed, which adds counting loss to the previous pixel space loss to improve the accuracy of crowd counting. The algorithm in this paper has been tested on ShanghaiTech and UCF_CC_50 datasets. The test results show that the algorithm in this paper has better statistical accuracy.
A Step Towards the Use of Chatbots to Support the Enterprise Decision-Making Processes

Chatbots are informatics applications that use Artificial Intelligence and natural language interfaces to simulate interactions with the end-user, providing a pleasant user experience. Generally, chatbots are related to customer service, trying either to answer the frequently asked questions that do not need direct human interaction or to generate leads to the after-client follow-up and contact. Chatbots represent a potential change in the way that people interact with information and online services. This article addresses the creation of a chatbot capable of generating insights for a manager on the decision-making process and the advantages that it represents. In order to create the referred chatbot, it is crucial to deal with multiple API's interconnected, external services, and numerous databases from the organizations, always focused on the improvement of the business process like the time optimization and the quality of the information retrieved, and the user experience related to them. Further system analysis shows a time saving of approximately 60% compared to the traditional way to access the information.
A Semi-supervised Framework with Efficient Feature Extraction and Network Alignment for User Identity Linkage

Nowadays, people tend to join multiple social networks to enjoy different kinds of services. User identity linkage across social networks is of great importance to cross-domain recommendation, network fusion, criminal behaviour detection, etc. Because of the high cost of manually labeled identity linkages, the semi-supervised methods attract more attention from researchers. Different from previous methods linking identities at the pair-wise sample level, some semi-supervised methods view all identities in a social network as a whole, and align different networks at the distribution level. Sufficient experiments show that these distribution-level methods significantly outperform the sample-level methods. However, they still face challenges in extracting features and processing sample-level information. This paper proposes a novel semi-supervised framework with efficient feature extraction and network alignment for user identity linkage. The feature extraction model learns node embeddings from the topology space and feature space simultaneously with the help of dynamic hypergraph neural network. Then, these node embeddings are fed to the network alignment model, a Wasserstein generative adversarial network with a new sampling strategy, to produce candidate identity pairs. The proposed framework is evaluated on real social network data, and the results demonstrate its superiority over the state-of-the-art methods.
"Lend Me a Hand" – Extending the Reach of Seated VR Players in Unmodified Games Through Remote Co-Piloting

Playing virtual reality (VR) games while seated can be challenging because many games are designed for a mobile, standing player. Sitting down not only limits mobility but also the reach of the player. This is particularly problematic if the player does not have a choice in whether they play seated or standing because it then becomes an accessibility issue. We have taken the concept of co-piloting (controls shared across multiple input devices and players) and applied it to VR with the aim of extending the reach of the VR player. To demonstrate the feasibility of the concept, a software prototype was developed that allowed a second, remote, player to take over the VR player's controllers and help them reach out. Because the prototype manipulates the SteamVR middleware and not the game, it can be used with any application that runs on SteamVR. A small formative study was conducted to inform further research and proof the concept.
NovemE - Color Space Net for Image Classification

Image classification is one of the basic applications of computer vision where several deep convolutional neural networks (DCNN) have been able to achieve state of the art results. In this paper, we explore the efficacious effect of color space transformations on image classification. After empirically establishing that color space transforms indeed affects the accuracy of the classification in DCNN, we propose NovemE, an ensemble-based model made up of nine (novem) base learners. We use transfer learning with significantly reduced training time and improved accuracy of classification. This model integrates different color spaces and DCNNs in order to achieve a higher accuracy in classifying the given data. We experimented with CINIC10 and Street View House Number (SVHN) datasets and were successful in achieving significant improvement on the current state of the art results on these datasets.
User Experience Design for Disaster Management Mobile Application using Design Thinking Approach

One of the main challenges during a disaster is the dissemination of risk information. The problem of lack of knowledge and awareness in disaster preparedness is also evident as some people are unaware of the damages that disasters can inflict on them The use of the mobile application is one of the considerations that may resolve these issues. This study aims to determine the user experience de- sign of a mobile application intended for household, barangay, and municipal users to improve the disaster management system in the province of Marinduque. The Design Thinking methodology includes five phases namely, Empathy, Define, Ideate, Prototype, and Test, and was used to determine the current situation or experiences, problems and issues, and possible solutions to their concerns. The Design Thinking workshop which was facilitated to thirty participants determined the functionalities that users required based on their needs and these are Announcements, Safety Guidelines, Evacuation Information, Request for Aid, Incident Reporting, Rescue Monitoring, Household Information, and Risk Index and Facilities Maps. Prototype design for these functionalities was created and presented to the users to validate their requirements and needs. The results show the relevant importance of these features to the users before, during, and after calamities.
Text sentiment analysis method based on attention word vector

In order to improve the accuracy of text sentiment classification, this paper proposes a sentiment analysis method based on attention word vector. Aiming at the problems of irregular, sparse and unclear user comments, an AT-C-GRU model combining attention mechanism, convolutional network and gated recurrent unit is proposed. The model converts the crawled user comments into word vectors and introduces the attention mechanism as input. After convolution and pooling, the sentiment classification of the comments is carried out through the GRU network, so that the key information in the user comments can be extracted more easily, and the accuracy of sentiment classification is improved. Experimental analysis shows that the accuracy of the sentiment classification method of this article on 3 data sets reaches 94.5%, 93.0% and 90.3%. This proves that the method in this paper has a good effect on sentiment classification of online review text.
Asset tagging for library system - does QR relevant?

Abstract This research paper is to review the techniques available in asset tagging or labelling such as barcode, RFID, and QR Code. It is known that QR codes are mostly used for marketing and promotion, information dissemination, and localization. By taking advantage of the increasing use of smartphones and their ability to read QR codes, the researchers attempted to implement QR codes on the existing system for improvement where it could facilitate users in borrowing or reserving library materials. The current web-based system allows users to borrow books however book reservation is only possible for physical materials available in the library repository. With the implementation of the QR code, this flaw can be solved.
StreamTable: An Area Proportional Visualization for Tables with Flowing Streams

Let M be a two-dimensional table with each cell weighted by a nonzero positive number. A StreamTable visualization of M represents the columns as non-overlapping vertical streams and the rows as horizontal stripes such that the intersection between a stream and a stripe is a rectangle with area equal to the weight of the corresponding cell. To avoid large wiggle of the streams, it is desirable to keep the consecutive cells in a stream to be adjacent. Let B be the smallest axis-aligned bounding box containing the StreamTable. Then the difference between the area of B and the sum of the weights is referred to as the excess area. We attempt to optimize various StreamTable aesthetics (e.g., minimizing excess area, or maximizing cell adjacencies in streams).
Kolmogorov-Smirnov Goodness-of-Fit test for identifying distribution of the number of earthquakes and the losses due to earthquakes in Indonesia

Abstract Earthquakes of high magnitude often result in large casualties and huge economic losses in Indonesia. An earthquake risk assessment must be carried out. The risk assessment process requires a structured approach. This paper is containing of the first step of risk assessment using actuarial-statistical method. The data used as research variables in this paper are the number of earthquakes and the losses due to earthquakes. The earthquakes data used are earthquakes with magnitude &gt; 5 Richter Scale. Data is collected within the last 40 years, from 1980 to 2019. Data is collected from Indonesian Disaster Data and Information, and NOAA National Centers for Environmental Information (NCEI). Descriptive statistics analysis obtains that the most frequently earthquakes with magnitude &gt; 5 Richter Scale occurred in Indonesia in 2004, where as it is known that at the end of 2004 an earthquake with a magnitude of 9.1 Richter Scale occurred in Aceh which caused the highest loss, which was around 147485.500 billion rupiah. Normality test of the variables results that the number of earthquakes and the losses due to earthquakes are not normally distributed. Kolmogorov-Smirnov Goodness-of-Fit test results that the number of earthquakes follows Gamma Distribution with shape parameter α = 1.800 and scale parameter β = 2.236; while the losses due to earthquakes follows Power Function Distribution with shape parameter α = 0.072, and boundary parameter a = 0, b = 2.810 x 10 5 .
Aggregate Particle Size Calculation Based on Optimized Equivalent Ellipse

Abstract The precise calculation of the aggregate size is crucial for detecting aggregate gradation. Therefore, the aim of this study was to propose a method for calculating aggregate particle size. First, a series of image-processing methods, such as filtering, enhancement, segmentation, and morphology, were implemented to obtain a binary image. Consequently, a method for determining the particle shape and correcting the particle size was devised. The aggregate particles are divided into four categories, i.e., rectangle, ellipse, diamond, and triangle, according to three parameters: compact factor, projected contour, and distance distribution from edge point to centre of mass. The corresponding correction coefficient of each category based on the equivalent elliptical minor axis was then calculated, and the grading accuracy of the corrected particle size was analyzed. The accuracy of the particle size classification obtained by the method of aggregate particle shape determination and particle size correction proposed in this paper is 80.7 %.
Rearranging Tasks in Daily-life Environments Using a Monte Carlo Tree Search and a Feasibility Database

In this paper, we address the task of rearranging items with a robot. A rearranging task is challenging because one should solve the following issues: to determine how to pick the items and plan how and where to place the items. In this study, we focus on how to obtain a sequence of actions that the robot could execute reducing the failures when the motion planner creates the trajectory to move the robot, such as not finding a solution. To confirm the sequence of instructions before executing them with the robot, we combine a motion planner with a symbolic planner. For that purpose, we propose a Motion Feasibility Checker (MFC), which quickly decides if a given set of pick-and-place poses can be executed with respect to the robot's kinematics. The MFC uses a database of possible pick and place poses of the target robot; given the initial and target pose of the item, the MFC finds a set of pick-and-place poses to execute that action with the robot. We use the Monte Carlo Tree Search (MCTS) to achieve a high performance of the symbolic planning. In the proposed method, the MCTS searches for the goal while it collaborates with the MFC. We tested the proposed method in a simulation environment doing a sandwich rearranging task in a convenience store setup.
Nonlinear Model Method of Microwave Power Device Based on Extreme Learning Machine

In large-signal simulations, building accurate models of power devices is essential for microwave circuit design. The traditional non-linear vector network measurement system is very complicated, and has the disadvantages of long test cycles and many measurement data. However, the powerful learning ability and accurate prediction ability of the extreme learning machine (ELM) neural network algorithm can be used to make up for the shortage of traditional measurement systems and improve the design efficiency. In this paper, an X-parameter model based on ELM neural network is established, the comparison of the model simulation with the actual circuit simulation shows that the fundamental error is within ±0.1 dBm and the second harmonic error is within ±0.3 dBm, which further verifies the correctness of the prediction model based on X-parameters of the ELM neural network.
Probabilistic Collision Constraint for Motion Planning in Dynamic Environments

Online generation of collision free trajectories is of prime importance for autonomous navigation. Dynamic environments, robot motion and sensing uncertainties adds further challenges to collision avoidance systems. This paper presents an approach for collision avoidance in dynamic environments, incorporating robot and obstacle state uncertainties. We derive a tight upper bound for collision probability between robot and obstacle and formulate it as a motion planning constraint which is solvable in real time. The proposed approach is tested in simulation considering mobile robots as well as quadrotors to demonstrate that successful collision avoidance is achieved in real time application. We also provide a comparison of our approach with several state-of-the-art methods.
Fast Single-Image Dehazing Algorithm Based on Piecewise Transformation in Optical Model

Single-image dehazing techniques are extensively used in outdoor optical image acquisition equipment. Most existing methods pay attention to use various priors to estimate scene transmission. In this paper, a fast single-image dehazing algorithm is proposed based on a piecewise transformation model between the minimum channels of the hazy image and the haze-free image in optical model. The minimum channel of the haze-free image is obtained by the piecewise transformation, which is a quadratic function model that we establish for the dark region, and a linear transformation model is established for the bright region. Using the minimum channels of the hazy image and the haze-free image, a transmission estimation model is established based on the haze optical model with adjustment variables. To obtain an accurate estimation of atmospheric light, we estimate the atmospheric light twice. Finally, the haze-free image is restored. Experimental results show that the proposed algorithm has minimal halo artifacts and color distortion in various depths of field, flat areas and sky areas. From the subjective evaluation, objective evaluation and running time analysis, it can be seen that the algorithm in this paper is superior to most existing technologies.
Generating Personalized Titles Incorporating Advertisement Profile

Advertisement (Ad) title plays a significant role in the effectiveness of online commercial advertising. However, it's difficult for most advertisers to think of attractive titles for their products. By mining keywords from current ad material, traditional retrieval methods and neural text generation models have been applied to solve this problem. However, few of them focus on personalized ad titles generation. Ad titles from different advertisers can be very diversified, and there is massive previous advertising data available, which can tell the style, content, and vocabulary of specific advertisers. Based on massive previous advertising data and current ad material, we propose an Ad-Profile-based Title Generation Network (APTGN) to automatically generate personalized titles for ads. The model utilizes massive advertising data and current ad material to construct a profile for each ad, which is further integrated into the generation model to help recognize the preferences of specific ads. Automatic evaluation metrics and online A/B testing both show that our model significantly outperforms all the baselines, increasing the adoption rate of recommendation titles by 27.22%. Through our deployed model, once an advertiser needs to customize an ad title for their products, satisfactory titles can be recommended automatically without bothering to write any words.