Reviewerly/exHarmony huggingface dataset:
Extrinsic Camera Calibration with Line-Laser Projection

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.
Improving Automatic Essay Scoring for Indonesian Language using Simpler Model and Richer Feature

Automatic essay scoring is a machine learning task where we create a model that can automatically assess student essay answers. Automated essay scoring will be instrumental when the answer assessment process is on a large scale so that manual correction by humans can cause several problems. In 2019, the Ukara dataset was released for automatic essay scoring in the Indonesian language. The best model that has been published using the dataset produces an F1-score of 0.821 using pre-trained fastText sentence embedding and the stacking model between the neural network and XGBoost. In this study, we propose to use a simpler classifier model using a single hidden layer neural network but using a richer feature, namely BERT sentence embedding. Pre-trained model BERT sentence embedding extracts more information from sentences but has a smaller file size than fastText pre-trained model. The best model we propose manages to get a higher F1-score than the previous models on the Ukara dataset, which is 0.829.
Blockchain Framework in Digital Government for the Certification of Authenticity, Timestamping and Data Property

In an ever more digitized world where information and data are increasingly dematerialized, the question of how to certify intellectual property and define when a document has been created or modified without the presence of any third-party guarantor inevitably arises.This document proposes a decentralized method that, by exploiting blockchain technology and distributed peer-to-peer (P2P) networks, makes it possible to historicize information in such a way that it is not possible for a user to alter its dating, attribute ownership or modify it by impersonating the author.The data certification (document, image, film, data archive, etc.) takes place through the creation of an immutable relationship between the owner and the data.At the legal level, many countries are beginning to regulate blockchain technology to use in many areas, such as the production chain, the Internet of Things or Public Administration.In this paper, we present a solution to promote digital government and greater transparency, using a framework based on the Ethereum blockchain, a smart contract and a decentralized application.
Performance Measurement in Scaled Agile Organizations

Performance metrics are a key tool for organizations to direct and motivate their members toward desired outcomes.Despite their central role also in Agile development, little is known about how Agile organizations set performance objectives and metrics in practice and balance between business and software engineering goals.In particular, there is a knowledge gap regarding performance measurement and its challenges in scaled Agile organizations.This paper applies an exploratory case study method to examine performance objectives and metrics in two business units of a scaled Agile organization and suggests a framework for selecting and structuring performance objectives within them.We offer five performance measurement dimensions that can be applied to provide guidance for scaled Agile organizations in selecting and prioritizing performance objectives.
VideoGraph – Towards Using Knowledge Graphs for Interactive Video Retrieval

Video is a very expressive medium, able to capture a wide variety of information in different ways. While there have been many advances in the recent past, which enable the annotation of semantic concepts as well as individual objects within video, their larger context has so far not extensively been used for the purpose of retrieval. In this paper, we introduce the first iteration of VideoGraph, a knowledge graph-based video retrieval system. VideoGraph combines information extracted from multiple video modalities with external knowledge bases to produce a semantically enriched representation of the content in a video collection, which can then be retrieved using graph traversal. For the 2021 Video Browser Showdown, we show the first proof-of-concept of such a graph-based video retrieval approach.
A generic metamodel for data extraction and generic ontology population

As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.
Follower: A Novel Self-Deployable Action Recognition Framework

Deep learning technology has improved the performance of vision-based action recognition algorithms, but such methods require a large number of labeled training datasets, resulting in weak universality. To address this issue, this paper proposes a novel self-deployable ubiquitous action recognition framework that enables a self-motivated user to bootstrap and deploy action recognition services, called FOLLOWER. Our main idea is to build a “fingerprint” library of actions based on a small number of user-defined sample action data. Then, we use the matching method to complete action recognition. The key step is how to construct a suitable “fingerprint”. Thus, a pose action normalized feature extraction method based on a three-dimensional pose sequence is designed. FOLLOWER is mainly composed of the guide process and follow the process. Guide process extracts pose action normalized feature and selects the inner class central feature to build a “fingerprint” library of actions. Follow process extracts the pose action normalized feature in the target video and uses the motion detection, action filtering, and adaptive weight offset template to identify the action in the video sequence. Finally, we collect an action video dataset with human pose annotation to research self-deployable action recognition and action recognition based on pose estimation. After experimenting on this dataset, the results show that FOLLOWER can effectively recognize the actions in the video sequence with recognition accuracy reaching 96.74%.
RRT-A*-BT approach for optimal collision-free path planning for mobile robots

This paper deals with the problem of optimal collision-free path planning for mobile robots evolving inside indoor cluttered environments. Addressing this challenge, a hybrid approach is proposed combining Rapidly-exploring Random Trees (RRT), A-Star (A*) and Back-Tracking (BT) algorithms (RRT-A*-BT). Thus, a vision system is used for a nearly-exact modeling of the environment through image processing. Moreover, each iteration of the basic RRT approach is guided by A* algorithm while trying to take the shortest path linking the robot current position to target . In case of a blockage, BT algorithm is used to get out the robot from this situation. Finally, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) is used to smooth the generated optimal path. RRT-A*-BT approach is validated through different scenarios; obtained results are compared with previous works on same environments with same conditions. The results prove that RRT-A*-BT is better and faster than other algorithms of the literature, such as Genetic Algorithms and Conventional RRT, in terms of (i) computation time,(ii) path length and (iii) transfer time..
The Economic Impact of COVID-19 on the Omani Tourism Sector

The COVID-19 pandemic has continued to cripple the tourism sector globally. The Omani tourism sector is not an exception. However, there are no empirical studies focusing on how Oman’s tourism sector has been affected by the COVID-19 pandemic. This study aimed to analyze the economic effect of COVID-19 on Oman’s tourism sector. The study sought to achieve three specific objectives, namely, to find out the economic effect of COVID-19 on the number of tourists visiting Oman, to analyze the variations in the amount of tourism into Oman since the COVID-19 became a global health threat and to evaluate the effectiveness of the strategies that Oman is implementing to cushion the tourism sector from the economic effects of COVID-19. A secondary data methodology was adopted, which involved collection and analysis of data from already published sources. Inclusion/exclusion criteria of relevance, up-to-date and author credibility were used to select the sources of information to be used. Both qualitative and quantitative data was collected and analyzed. Findings from the study revealed that COVID-19 has orchestrated the decline in the performance of Oman’s tourism industry. There has been increased unemployment, reduced incomes and falling consumer purchase power. It is recommended that the Oman government develop and implement policies that reduce tax burden on tourist organizations and give tourist firms stimulus packages to cushion the sector from collapsing due to the effects of COVID-19.
Deep View Synthesis via Self-Consistent Generative Network

View synthesis aims to produce unseen views from a set of views captured by two or more cameras at different positions. This task is non-trivial since it is hard to conduct pixel-level matching among different views. To address this issue, most existing methods seek to exploit the geometric information to match pixels. However, when the distinct cameras have a large baseline (i. e., far away from each other), severe geometry distortion issues would occur and the geometric information may fail to provide useful guidance, resulting in very blurry synthesized images. To address the above issues, in this paper, we propose a novel deep generative model, called Self-Consistent Generative Network (SCGN), which synthesizes novel views from the given input views without explicitly exploiting the geometric information. The proposed SCGN model consists of two main components, i. e., a View Synthesis Network (VSN) and a View Decomposition Network (VDN), both employing an Encoder-Decoder structure. Here, the VDN seeks to reconstruct input views from the synthesized novel view to preserve the consistency of view synthesis. Thanks to VDN, SCGN is able to synthesize novel views without using any geometric rectification before encoding, making it easier for both training and applications. Finally, adversarial loss is introduced to improve the photo-realism of novel views. Both qualitative and quantitative comparisons against several state-of-the-art methods on two benchmark tasks demonstrated the superiority of our approach.
Large scale datasets for Image and Video Captioning in Italian

The application of Attention-based Deep Neural architectures to the automatic captioning of images and videos is enabling the development of increasingly performing systems.Unfortunately, while image processing is language independent, this does not hold for caption generation.Training such architectures requires the availability of (possibly large-scale) language specific resources, which are not available for many languages, such as Italian.In this paper, we present MSCOCO-it e MSR-VTT-it, two large-scale resources for image and video captioning.They have been derived by applying automatic machine translation to existing resources.Even though this approach is naive and exposed to the gathering of noisy information (depending on the quality of the automatic translator), we experimentally show that robust deep learning is enabled, rather tolerant with respect to such noise.In particular, we improve the state-of-the-art results with respect to image captioning in Italian.Moreover, in the paper we discuss the training of a system that, at the best of our knowledge, is the first video captioning system in Italian.
Does a presentation Media Influence the Evaluation of Consumer Products? A Comparative Study to Evaluate Virtual Reality, Virtual Reality with Passive Haptics and a Real Setting

Technologies based on image offer a high potential to present consumers with products by focusing on their visual characteristics, but lack the capacity to physically interact with an object, which can compromise how consumer products are evaluated.The present study aims to analyse the influence of different presentation media on how users perceive the product by comparing the evaluation of a piece of furniture made by a sample of 203 users, which was presented in three different settings: a real setting (R), a Virtual Reality setting (VR) and a Virtual Reality with Passive Haptics setting (VRPH).To evaluate the product in the different settings, a semantic differential scale was built that comprised 12 bipolar pairs of adjectives.To study the results, the descriptive statistics for the semantic differential scales were analysed, a study about the frequency of repetition was conducted of each evaluation, a Kruskal-Wallis test was conducted and Dunn's post hoc tests were performed.The results showed that the presentation media of a piece of furniture influenced the evaluation of how users perceived it.These results also revealed that the haptic interaction with a product influenced how users perceived it compared to an exclusively visual interaction.
New Crypto-Secured Lending System with a Two-Way Collateral Function

All existing secured loans, including crypto-secured loans, are provided under the condition that the collateral entrusted by the borrower is kept safe during the loan term. In other words, they use a one-way collateral function. Thus, a frequent drawback of these loans is that the collateral value increases if and only if the collateral price increases. To resolve this problem, this paper proposes a new crypto-secured lending system incorporating a new two-way collateral function. It would allow a borrower to invest proportions of their own collateral by predicting the market in both directions to make profits irrespective of whether the price of the collateral increases or decreases. This benefits the borrower since profit can be made even if the price of the collateral drops, by betting on the price decrease. This new lending system could include a new hedged portion, unlike traditional secured lending systems. As a result, larger loans can be made under this arrangement; further, this portion provides the advantage of reducing the underlying collateral price volatility risk.
Examining the Readability Level of Reading Texts in English Textbook for Indonesian Senior High School

Assuming that textbooks authors have conducted the readability measure, many of its users often take this key feature of a good textbook for granted. Nonetheless, considering the unique context of each classroom and the generic nature of textbooks written for the public, teachers should double-check the appropriateness of the book they use, including its readability. Although tech-assisted readability measurement is developing in many parts of the world, in Indonesia, this area is still under-researched. For this reason, the present study attempts to examine the readability level of an English textbook using Coh-Metrix. Content analysis is employed since the object being analyzed is a textbook. This study also expands previous studies on readability measures by building dialogue between the result of Coh-Metrix measurement and the students' perceived readability. To obtain the data, an automated Coh-Metrix readability measurement was conducted via cohmetrix.com. Also, to get more profound analysis, a questionnaire on students' perceived readability was distributed to 35 tenth graders. Then the collected data were analyzed by using interactive model of Miles and Huberman. The finding of this study revealed that regardless of the slight difference between the perceived readability level and the Coh-Metrix-generated readability level, most of the texts in the book are mostly below the students' level. Arguably, the textbook is relatively potential for language acquisition because it provides comprehensible input.
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy

We give a simple, computationally efficient, and node-differentially-private algorithm for estimating the parameter of an Erdos-Renyi graph---that is, estimating p in a G(n,p)---with near-optimal accuracy. Our algorithm nearly matches the information-theoretically optimal exponential-time algorithm for the same problem due to Borgs et al. (FOCS 2018). More generally, we give an optimal, computationally efficient, private algorithm for estimating the edge-density of any graph whose degree distribution is concentrated in a small interval.
Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition

In many fields, such as oil logging, it is expensive to obtain labeled data, and a large amount of inexpensive unlabeled data are not used. Therefore, it is necessary to use semisupervised learning to obtain accurate classification with limited labeled data and many unlabeled data. The semisupervised support vector machine (S3VM) is the most useful method in semisupervised learning. Nevertheless, S3VM model performance will degrade when the sample number of categories is not even or have lots of unlabeled samples. Thus, a new semisupervised SVM by hybrid whale optimization algorithm (HWOA-S3VM) is proposed in this paper. Firstly, a tradeoff control parameter is added in S3VM to deal with an uneven sample of category which can cause S3VM to degrade. Then, a hybrid whale optimization algorithm (HWOA) is used to optimize the model parameters of S3VM to increase the classification accuracy. For HWOA improvement, an opposition-based cubic mapping is used to initialize the WOA population to improve the convergence speed, and the catfish effect is used to help WOA jump out of the local optimum and obtain the global optimization ability. In the experiments, firstly, the HWOA is tested by 12 classic benchmark functions of CEC2005 and four functions of CEC2014 compared with the other five algorithms. Then, six UCI datasets are used to test the performance of HWOA-S3VM and compared with the other four algorithms. Finally, we applied HWOA-S3VM to perform oil layer recognition of three oil well datasets. These experimental results show that (1) HWOA has a higher convergence speed and better global searchability than other algorithms. (2) HWOA-S3VM model has higher classification accuracy on UCI datasets than other algorithms when combined, labeled, and unlabeled data are used as the training dataset. (3) The recognition accuracy and speed of the HWOA-S3VM model are superior to the other four algorithms when applied in oil layer recognition.
A fully integrated violence detection system using CNN and LSTM

Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.
Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

This study mainly focuses on the development of a second-order sliding mode-based meta-cognitive fuzzy neural network (MCFNN) control for power quality improvement, whereby an MCFNN is employed to learn modelling uncertainties. First, a second-order sliding mode control (SOSMC) is designed to track reference current for active power filter. Then, an MCFNN system with accurate approximation capability is further investigated to learn the unknown parts in SOSMC. Different from the existing predefined structure approaches, only necessary data can be extracted to adjust the structure and parameters of the networks in MCFNN. Subsequently, the Lyapunov stability analysis is presented to guarantee tracking performance and stability of the closed-loop system. Moreover, the excellent performance of the proposed MCFNN scheme is verified by simulation and experimental studies, and its remarkable characteristics are exhibited in comparison with other intelligent control schemes.
Performance evaluation of similarity measures for K-means clustering algorithm

Clustering is a useful technique that organizes a large quantity of unordered datasets into a small number of meaningful and coherent clusters. Every clustering method is based on the index of similarity or dissimilarity between data points. However, the true intrinsic structure of the data could be correctly described by the similarity formula defined and embedded in the clustering criterion function. This paper uses squared Euclidean distance and Manhattan distance to investigates the best method for measuring similarity between data objects in sparse and high-dimensional domain which is fast, capable of providing high quality clustering result and consistent. The performances of these two methods were reported with simulated high dimensional datasets.