Reviewerly/exHarmony huggingface dataset:
A semantically annotated corpus of tombstone inscriptions

Abstract The digital preservation of funerary material is of interest to many different scientific disciplines. Textual information found on tombstones often goes far beyond the expected (name of the deceased, dates of birth and death), and may include information about commemorators, family roles, occupations, references to biblical or other texts, places of birth and death, cause of death, epitaphs and poems. Gravestones are multi-modal media, and besides text are often decorated with artistic symbols. To capture this information in a systematic way and make it available on a large scale for research purposes, a meaning representation based on linking entities by relations has been designed that will extend search capabilities beyond simple string matches. Concepts are represented as WordNet synsets, and a vocabulary of 32 relations make connections between concepts. This formalisation has been developed and evaluated based on a dataset of more than 1,000 Dutch tombstones.
DevOps in Industry 4.0: A Systematic Mapping

DevOps is the acronym for the integration of development and operations, which allows the improvement of communication and collaboration. Likewise, its objective is to help organizations develop software products and services quickly without sacrificing quality or cost. Additionally, its focus is on development, integration, delivery, release, testing, and continuous monitoring through the automation of tasks. Industry 4.0 solutions seek to digitize the industries’ processes and services to make them faster, safer, and more efficient through new technologies. These developments are not unrelated to the practices proposed by DevOps. This article presents a systematic mapping of the literature to identify the adoption prospects of DevOps practices for Industry 4.0 solutions reported in the literature. This work gathers proposals that show the importance and relevance of adopting DevOps to develop solutions for Industry 4.0. Organizations must prepare to address Industry 4.0 solutions; this will allow them to survive and prosper in the era of the Fourth Industrial Revolution. Therefore, it is necessary to conceptualize and adopt a solution development model that enables them to address these challenges and DevOps with its principles and standards, which would reduce operating costs and improve the service levels, efficiency, and administration of the different areas.
Development of Open-Source Bot: An Action Research Approach

With the rapid development of online courses in recent years and because of the covid-19 all learners have changed the way of learning, from physical courses to online courses. This study used action research method. We interview what problems or issues that students have to take an online class. After interviewed 11 students and learned some of their experiences and ideas about online courses. We can find that there are many inconveniences in online courses, such as: ask the teacher questions or online class questions. we hope to make a PA through an open-source platform and use this PA as a scaffolding to help students learn.
Improving of Action Localization in Videos Using the Novel Feature Extraction

Nowadays, automatic video analysis using state-of-the-art machine learning techniques is highly relevant and finds lots of industrial applications. This approach gives the possibility to solve a set of the problems such as finding the main part of the video, tracking in time by the duration of an action that is labor-intensive and almost impossible to be solved manually or by old-fashioned methods. One of the most promising approaches is the Weakly-supervised method. The Weakly-supervised technique can be used for temporal action localization, which is a challenging task because it uses only video-level labels during the training. The main benefit of this method is that it is not required to create large-scale datasets with temporal annotations, which are quite time-consuming. In this work, we propose to improve the current state-of-the-art in temporal action localization by introducing the new feature extraction procedure. We achieved better results on the RGB stream by making the new feature extraction using more powerful neural network models on the benchmark THUMOS”14 dataset [1]. We also provide a deep analysis of the importance of the feature extraction procedure in the whole workflow for temporal action detection. The results of our investigations can be applied directly for solving a wide range of industrial problems in a robust and accurate manner.
Straight Skeleton Based Automatic Generation of Hierarchical Topological Map in Indoor Environment

Real-time autonomous navigation and scheduling are regarded as the prerequisite capabilities for indoor mobile robots, while the application of prior information about the indoor map can contribute to reducing the level of computational complexity significantly. The indoor maps can be generated through manual marking or using automatic generation methods. The latter considerably reduces human intervention, which makes it more suitable for large indoor environments. However, most of the existing approaches to the automatic generation of indoor maps suffer such problems as imprecision and the lack of smoothness. These issues impede the generated map from meeting the requirement for safety and traceability. In order to address these issues, this paper proposes a novel approach to the automatic generation of the indoor hierarchical topological map (HTM) based on the straight skeleton. This method involves three steps. Firstly, the boundaries of walls and obstacles are extracted from the grid map or the CAD drawing. Secondly, the extracted boundaries are used to automatically generate the primary tracks in narrow corridors, the regions in large open areas, and the secondary tracks within these regions. Thirdly, for the robots that are incapable of taking a spin turn, the clothoid-based smoothing method is applied to ensure the curvature continuity of generated tracks. According to the experimental results, the proposed method is accurate in describing the topological structure of complex and changeable indoor environments and effective in providing smooth and easy-to-track trajectories.
Twitter Sentiment Analysis on COVID-19

The COVID-19 pandemic has been a trending topic on social media since it first started in December 2019. This pandemic originated in the city of Wuhan in China. India was vastly affected by this pandemic due to its large population. However, due to its vast population, India has a large number of social media users, which can provide crucial insight into people's perspectives on topics related to the pandemic. This paper uses natural language processing and sentiment analysis on the posts created by users on the social media platform of Twitter. The study uses APIs and keywords to get the data to analyze and understand the emotions of the tweets linked to topics like oxygen, vaccine, beds, and lockdown in the times of COVID-19. The results and observations are presented using various graphs, charts, and word clouds. This paper aims to help the government, researchers, and frontline workers to get an insight into the sentiment on social media about various topics concerning the covid -19 pandemic.
Discrete hashing with triple supervision learning

In recent years, discrete supervised hashing methods have attracted increasing attention because of their high retrieval efficiency and precision. However, in these methods, some effective semantic information is typically neglected, which means that all the information is not sufficiently utilized. Moreover, these methods often only decompose the first-order features of the original data, ignoring the more fine-grained higher-order features. To address these problems, we propose a supervised hashing learning method called discrete hashing with triple supervision learning (DHTSL). Specifically, we integrate three aspects of semantic information into this method: (1) the bidirectional mapping of semantic labels; (2) pairwise similarity relations; (3) second-order features from the original data. We also design a discrete optimization method to solve the proposed objective function. Moreover, an out-of-sample extension strategy that can better maintain the independence and balance of hash codes is employed to improve retrieval performance. Extensive experiments on three widely used datasets demonstrate its superior performance.
Viktoria: A New Architecture for Hash Functions

Hash functions are increasingly playing an important role in contemporary cryptography. These functions are used to generate and verify digital signatures, to generate cryptographic keys, to generate pseudo-random bits, among other applications. The SHA-3 competition, sponsored by NIST (National Institute of Standards and Technology), has sparked new interest in research into new types of hash functions. New paradigms must withstand the increasingly technical and fast attacks of cryptoanalysis. In this paper we present a new paradigm for building a robust hash function, based on a permutation routine that builds its own randomness as it progresses through reading the message and transforms it into a compact 512-bit code (it can also be a larger hash). The new paradigm features two major innovations: fast preprocessing that initiates an internal state of 256!2 permutations, and post-processing that guarantees a minimum number of executed rounds of the order of 213. In the preprocessing it is possible to differentiate nearly identical messages very quickly and the post-processing brings an extra security to the final hash, coming from a large but indeterminate number of rounds. This paper presents the main characteristics of this new paradigm for building hash functions.
Spatio-Temporal Graph Complementary Scattering Networks

Spatio-temporal graph signal analysis has a significant impact on a wide range of applications, including hand/body pose action recognition. To achieve effective analysis, spatio-temporal graph convolutional networks (ST-GCN) leverage the powerful learning ability to achieve great empirical successes; however, those methods need a huge amount of high-quality training data and lack theoretical interpretation. To address this issue, the spatio-temporal graph scattering transform (ST-GST) was proposed to put forth a theoretically interpretable framework; however, the empirical performance of this approach is constrained by the fully mathematical design. To benefit from both sides, this work proposes a novel complementary mechanism to organically combine the spatio-temporal graph scattering transform and neural networks, resulting in the proposed spatio-temporal graph complementary scattering networks (ST-GCSN). The essence is to leverage the mathematically designed graph wavelets with pruning techniques to cover major information and use trainable networks to capture complementary information. The empirical experiments on hand pose action recognition show that the proposed ST-GCSN outperforms both ST-GCN and ST-GST.
Energy Constrained Online Coverage Path Planning with a Lower Bound For the Optimal Performance

Coverage path planning is a problem that is faced on a daily base by different robots varying from indoor cleaning robots to agricultural drones. These platforms are sometimes expected to cover previously unknown areas, which may be too large to cover with a single battery charge, requiring multiple visits to a charging or a tanking station. This paper proposes a new method for this energy constrained coverage path planning problem. The proposed approach derives from contour following, and it provides superior performance compared to the existing work in the literature. Additionally, environment topology independent bounds are derived for the minimum number of charges and energy consumed for the energy constrained coverage path planning problem.
A Scheme and Implementation of Automatic Deployment of Multilingual Industrial Mechanism Model Based on OpenStack

In the current construction process of the industrial Internet platforms, a large amount of precipitation of industrial mechanism models is a vital part of it. However, its multiple languages and various operation modes cause many difficulties and repetitive work for researchers in terms of deployment and management. To solve this problem, this paper proposes a multi-language industrial mechanism model automatic deployment scheme based on OpenStack. Build a custom deployment environment by according to the demand and the automated deployment process interaction, to realize the mechanism of the development of language model to the variety of the unified deployment and management, easy to extend, at the same time for developers to save the workload at the same time, The rapid precipitation of a large number of industrial mechanism models also provides good support for the construction of industrial ecosystem and the realization of industrial intelligence.
Towards Optimal Motion Planning of Surface Vehicles in Confined Waters

This paper presents a method for motion planning of fully actuated autonomous surface vehicles in confined waters. The goal is to generate a dynamically feasible trajectory from a given initial state to a given final state without collisions of the vehicle with obstacles. Both the own vehicle and static obstacles are represented as convex polygons. A sampling-based approach is used, where multiple samples in a state space are connected by third-order polynomials. A method is proposed to compute optimal polynomials that both minimize a cost function and account for input variable constraints. Within the cost function, time is weighted together with the control effort. The obtained objective function is a scalar nonlinear function that is solved using numerical minimization techniques. Embedded in a sampling-based motion planning algorithm, the major motion planning problem is solved by many small optimizations. The result is an optimal trajectory from an initial state to a final state, taking into account dynamics and input variable constraints, and avoiding collisions of the vehicle with static obstacles. A simulation is used to demonstrate that the proposed method is basically applicable.
Vertical Scaling of Virtual Machines In Cloud Environment

Virtual Machine (VM) scaling in cloud is a common technique followed in cloud infrastructures to efficiently make use of the VM instances to get more throughput. There are two major scaling approaches which are being followed these days namely Horizontal Scaling and Vertical Scaling. The horizontal scaling is widely used as compared to the vertical scaling due to its ease of implementation and profit measure whereas the implementation of vertical scaling is more complex. The Vertical Scaling is accomplished using the Hypervisor tool - Virtual Box. We make use of the CLI provided by the Virtual Box to vertically scale the VMs. Python script is written to take care of all the edge cases that could be encountered in the vertical VM scaling. In addition to this, user activity statistics are logged to the database accompanying with the changes to the allotted VMs. A web interface is provided to the user/administrator to keep track of the changes in the VM environment in different user sessions. Scaling of different VM parameters like RAM & VRAM size, Usage of number of CPUs and Availability of different audio controller types are discussed in this paper. This paper proposes a cost effective method to implement vertical scaling of VM in cloud architecture.
Hybrid Density-based Adaptive Clustering using Gaussian Kernel and Grid Search

Density-based spatial clustering of data with noise (DBSCAN) is a popular clustering algorithm that groups data points which are close together using two parameters eps - which is the radius of each cluster, and Minpts, which is the minimum number of points in each cluster. However, the performance of DBSCAN reduces for the datasets with varying density clusters. This paper proposes the implementation of a distributed and adaptive DBSCAN algorithm on the HPCC Systems platform. The proposed approach uses techniques such as grid search and Gaussian kernel to search optimized values for the threshold density of clusters, thus eliminating the requirement for users to specify the parameters. Further, the experimental investigation suggests that proposed ADBSAN performs better compared to existing ADBSCAN implementations using k-dist and Gaussian kernels.
An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks

It is well-known that training of generative adversarial networks (GANs) requires huge iterations before the generator’s providing good-quality samples. Although there are several studies to tackle this problem, there is still no universal solution. In this paper, we investigated the effect of sample mixing methods, that is, Mixup, CutMix, and newly proposed Smoothed Regional Mix (SRMix), to alleviate this problem. The sample-mixing methods are known to enhance the accuracy and robustness in the wide range of classification problems, and can naturally be applicable to GANs because the role of the discriminator can be interpreted as the classification between real and fake samples. We also proposed a new formalism applying the sample-mixing methods to GANs with the saturated losses which do not have a clear "label" of real and fake. We performed a vast amount of numerical experiments using LSUN and CelebA datasets. The results showed that Mixup and SRMix improved the quality of the generated images in terms of FID in most cases, in particular, SRMix showed the best improvement in most cases. Our analysis indicates that the mixed-samples can provide different properties from the vanilla fake samples, and the mixing pattern strongly affects the decision of the discriminators. The generated images of Mixup have good high-level feature but low-level feature is not so impressible. On the other hand, CutMix showed the opposite tendency. Our SRMix showed the middle tendency, that is, showed good high and low level features. We believe that our finding provides a new perspective to accelerate the GANs convergence and improve the quality of generated samples.
Watch and Buy

"Watch and Buy: Multimodal Product Identification(WAB)" challenge is a new task in the field of cross-modal retrieval, which aims to retrieve the relevant products when users watching live streamers selling fashion products. In practice, it is very hard to get the product items accurately and quickly because of large deformations, occlusions and motion blur of product items in a real-world live streaming environment. In this paper, our solution for WAB challenge is presented, which includes the model and training methods of fashion product localization and identification, as well as the detailed strategy for optimization, model assembly, and post-process rank. Experiments show that our strategies for data enhancement, model fusion and result ranking can lead to a better result. Finally, our model is small and efficient with competitive results and attains 0.4915 on test B in the final season, ranking 5th. And our model attains 0.5604 on test A, ranking 1st in the late submission.
Ways to Solve the Problems of Employer-Sponsored Education

The national innovation system is based on the creative economy, the value of which is expressed in new ideas. Creative economy changes the approach to the labor market, production, trade, and provision of services. There is traditionally a shortage of qualified specialists, especially in engineering areas for industrial enterprises in the Russian labor market. One of the reasons for the lack of specialists is the small number of university graduates who are employed according to their specialty. Employer-sponsored education can serve as a tool to increase the mutual responsibility of the educational organization, the future employer and the applicant for the choice of the future specialty and further employment. To implement employer-sponsored education, the company acts as a future employer for the graduate, the state provides a state-funded place for the student’s training, the university carries out the educational process by order of an enterprise, and a student receives an education and works at the enterprise for at least three years. The aim of the article is to analyze the experience of the UrFU in implementing employer-sponsored education within the allocated quota at the expense of the federal budget. The paper considers the indicators that characterize the dynamics of employer-sponsored education at the university for the period from 2015 to 2020. The authors identify the main problems in the organization of the admission of citizens to employer-sponsored programs that reduce its attractiveness, and suggest ways to increase the number of students studying under the conditions of employer-sponsored admission.
Classification of the Suicide-Related Text Data Using Passive Aggressive Classifier

As technological advancements have been increasing in day-to-day life, most people are relying on Internet usage where they are using social media platforms to convey their status through their respective profiles. As social media made a big boom on the planet, most people use these applications to share their views in many ways like text, images or videos, etc., with which a huge amount of data gets generated. From the data related to their posts, it is been observed that most people are not able to get rid of their mental stress. As the report of WHO (World Health Organization) shows that suicides are the second-largest global pandemic, our objective is to analyze the text within the suicide notes posted on Twitter (one among the social media platforms). Text classification is been done on the data, and we have many machine learning algorithms, neural networks, regression techniques, etc., Like the large stream of data is been generated to get better results, we use passive aggressive classifier (PAC) algorithm. Along with PAC, we use SVM, Naive Bayes, random forest, and decision tree and have a comparative study. Since we considered the labeled textual dataset, we apply the above models on the dataset and classify and show the result as “Suicidal” or “Non-Suicidal.”
EmoHinD: Fine-grained multilabel emotion recognition from Hindi texts with Deep learning

Emotion recognition is a critical sub-field within Affective Computing and a step towards comprehensive natural language understanding. This might pave the way for a wide range of applications in natural language processing for social good, such as suicide prevention and employee mood detection as well as helping businesses provide personalized services to their users. Due to a scarcity of resources and a lack of standard labeled textual corpora for emotions in Hindi, constructing an emotion analysis system is a tough task in such a low resource language. This paper presents a novel multilabel Hindi text dataset of 58000 text samples with 28 emotion labels and a fine-tuned Multilingual BERT-based transformer model on the fine-grained dataset. The model achieves state of the art performance with an overall ROC-AUC score of 0.92 upon evaluation.
Malware Searching Methods at FML-Architecture

A matrix-logical method for diagnosing malicious components is being developed, which has high performance due to parallel operations. A vector-matrix method for searching malware in CPS is presented, which uses functionality and malware responses to test datasets. All models and algorithms have been verified in the program code and are ready for implementation in the cloud federated machine learning system for malware detection. The algorithms proposed in the study have linear or quadratic computational complexity. Due to the parallel execution of register logical operations, procedures for analyzing matrices or tables outperform their analogs by two to 10 times in performance.