Reviewerly/exHarmony huggingface dataset:
Controlled Terminology for Monuments, Museum and Gallery Objects: Preliminary Research on Vocabularies Reconciliation

The purpose of controlled vocabularies is to organize information and to provide terminology to catalogue and retrieve information in information systems. Vocabularies provide definitions and scope notes, which is especially important in the field of cultural heritage, capturing the richness of meanings and relationships between terms. Therefore, they are important tools for data integrity control and providing reliable search and retrieval, and also essential educational resources. Currently, there are only two controlled vocabularies in the Croatian language related to the field of cultural heritage, primarily museum collections and the protection of cultural monuments: 1) the Classification of museum and gallery objects, 2) the Thesaurus of monument types. They overlap to some extent, but also differ according to the levels of specificity, exhaustivity and structure. As an initial method of vocabulary reconciliation, mapping based on Simple Knowledge Organization System (SKOS) mapping properties is used. This paper aims to examine the possibilities and challenges of vocabulary reconciliation and to provide guidelines for the future use of these valuable terminological resources, to improve the users' access to cultural heritage. Additional mapping of both terminologies to the Art & Architecture Thesaurus will ensure interoperability in the global linked open data environment and provide a multilingual context.
Libel Inc: An Analysis of the Libel Site Ecosystem

Libel sites publish anonymously submitted un-proven libelous claims about individuals that often include personal information about the subject. The stated goal of the sites is to “warn” the public about an individual but the impact is harassment and ruining the subject's reputation. These individual libelous posts are surfaced when searching for a person's name using an online search engine and can cause a range of harms from emotional to economic. For example, the libelous posts might surface if a potential employer performs a Google search as part of a “background check.” There have been prior news reports of this troubling phenomena but no systematic analysis of the ecosystem. In this paper, we conduct a rigorous analysis of these libel sites, supporting services, and intervention by Google. We discovered and analyzed 9 libel sites, 7 websites for reputation management services, and 12 related websites. We found that all of the libel websites included at least one method of generating revenue. The most common revenue generation method was including advertisements for “reputation management services” which require payment for the removal of a post. We found that all of these removal services were dubious in nature and that the removal policies were akin to extortion. Our analysis of Google's intervention to reduce the visibility of these websites indicated that it appeared to only reduce the visibility of the specific libel post URL but that other URLs containing links to the post or the headline text of the post were still highly ranked. Based on our findings, we make recommendations to many of the stakeholders about potential approaches for mitigating this abusive ecosystem.
A Russian Hate Speech Corpus for Cybersecurity Applications

The spread of hate speech may induce hate crimes, cyber violence, and cause great harm to the security of both cyberspace and physical society. Therefore, it has become a crucial issue for both governments and academic researchers. To help supervise online hate speech, this paper creates a Russian corpus of 20,476 comments, which are annotated for hate speech. The created corpus could develop to be a reference dataset for online media security monitoring system that automatically detects the hate speech. Besides, how the multi-dimensional annotation scheme was designed and applied to the corpus is also discussed in this paper. Specifically, a multi-dimensional labeling method is proposed to better model the different aspects of the hate speech, thereby reducing the inconsistency of the results that may be introduced by different annotators. We also compare the annotations that made by expert annotators and native students, the results prove the effectiveness of our proposed annotation scheme.
Adjoining to (K,s,t)-Wythoff’s game its P-generators as moves

(K,s,t)-Wythoff's game is a generalization of the classical (s,t)-Wythoff's game, where a player is allowed to remove a multiple of K tokens in each move (K is a fixed positive integer). In this paper, we adjoin to (K,s,t)-Wythoff's game four subsets of its P-generators (a particular class of P-positions) as moves, resulting in four different classes of games. The P-positions of some classes are characterized, and the equivalences among these new games are proved. The results show that under certain conditions only a few of the added P-generators play a key role in the formation of our new games, which can change the set of P-positions of (K,s,t)-Wythoff's game.
Algorithm for Finding the Optimal Obstacle Avoidance Maneuver for Wheeled Robot Moving Along Trajectory

Considered a problem of stabilization of the wheel robot along a pre planned trajectory in a statistical and dynamical obstacles environment. Developed an algorithm for searching path for maneuver with returning to a target trajectory. The results of numerical and real experiments with wheeled robot prototype are presented.
A comparative study of abstraction and informational density in higher and lower Indonesian graduate students' L2 English academic texts

Abstract The language features of the second language (L2) English academic texts written by Indonesian graduate students enrolled in Hungarian higher education are employed in the present study. The study focuses on the level of abstraction and informational density in student assignments in particular. Seven high-stakes essays were collected from seven Indonesian graduate students registered in the faculty of Social Sciences at three different Hungarian universities. Coh-Metrix, a corpus-based computational tool, was used in this study to examine indices of content words and abstraction. As for the comparison between higher and lower proficiency level students, parametric statistics were used to conduct a quantitative analysis of the selected linguistic features, including nouns, verbs, adjectives, adverbs, pronouns, and concreteness and abstraction. The results show that C1 English proficiency level students outnumber B2 students in terms of informational density. Their texts are more abstract than those of B2 students.
Applying Neural Networks for the Identification of Control Object Mathematical Models for the Control Problems

In order to obtain optimal control of a real object, it is necessary to know the precise mathematical model of this control object. In the present study an artificial neural network is used for building a mathematical model of the control object. First, some forms of control are defined, and with the help of these controls, the control object is modeled. The obtained values of the controls and the space state vector are stored to create a training sample. The artificial neural network is then trained on this training set. For a trained neural network, a set of optimal control problems is solved. The optimal control obtained by the trained artificial neural network is applied to a real control object. The accuracy of the approximation of the mathematical model by an artificial neural network can be estimated based on the proximity of the functional values of the control object and the trained neural network.
Quaternion Discrete Racah Moments Convolutional Neural Network for Color Face Recognition

In this paper, we propose a novel architecture called quaternion discrete Racah moments convolutional neural network (QRMCNN) for color face recognition to improve the recognition accuracy and to reduce the computational complexity. Quaternion Racah moments (QRM) can extract effective features from color images. The use of QRM as first layer aims to reduce the input matrix size of the proposed architecture in the earliest orders and hence enormously decrease the number of parameters and the computational time. Experiments are carried out on faces96 and FEI face datasets to demonstrate the efficiency of the proposed architecture in the training process. The obtained results indicate clearly that the QRMCNN outperforms previous methods in terms of recognition rate and GPU elapsed time.
Drone path optimization in complex environment based on Q-learning algorithm

Path planning of intelligent agents in an emergency context is one of the most popular issues within nowadays context. This work proposes an environment acquisition and a path optimization solution based on reinforcement learning. The proposed solution implements Q-Learning algorithm and enables the agent to choose the path that maximizes the reward and minimizes the penalty. When tested in an experiment grid and compared to other solutions the proposed solution proved to be more stable and more efficient.
Sobel Potential Field: Addressing Responsive Demands for UAV Path Planning Techniques

Dealing with the trade-off challenge between computation speed and path quality has been a high-priority research area in the robotic path planning field during the last few years. Obtaining a shorter optimized path requires additional processing since iterative algorithms are adopted to keep enhancing the final optimized path. Therefore, it is a challenging problem to obtain an optimized path in a real-time manner. However, this trade-off problem becomes more challenging when planning a path for an Unmanned Aerial Vehicle (UAV) system since they operate in 3D environments. A 3D map will naturally have more data to be processed compared to a 2D map and thus, processing becomes more expensive and time-consuming. This paper proposes a new 3D path planning technique named the Sobel Potential Field (SPF) technique to deal effectively with the swiftness-quality trade-off. The rationale of the proposed SPF technique is to minimize the processing of potential field methods. Instead of applying the potential field analysis on the whole 3D map which could be a very expensive operation, the proposed SPF technique will tend to focus on obstacle areas. This is done by adopting the Sobel edge detection technique to detect the 3D edges of obstacles. These edges will be the sources of the repulsive forces while the goal point will be emitting an attractive force. Next, a proposed objective function models the strength of the attractive and repulsive forces differently to have various influences on each point on the map. This objective function is then optimized using Particle Swarm Optimization (PSO) to find an obstacle-free path to the destination. Finally, the PSO-based path is optimized further by finding linear shortcuts in the path. Testbed experimental results have proven the effectiveness of the proposed SPF technique and showed superior performance over other meta-heuristic optimization techniques, as well as popular path planning techniques such as A* and PRM.
Bitcoin Oracle Contracts: Discreet Log Contracts in Practice

Contracts established on a blockchain remove the need for intermediary third parties, but usually require external data to decide on an outcome, provided by a so-called oracle. Discreet Log Contracts were proposed as a way to establish such contracts on the Bitcoin blockchain without requiring interaction with an oracle, increasing the privacy of the contracting parties. To enable cross-compatible implementations to be developed, a specification effort was carried out, that led to various improvements over and additions to the original proposal. In particular, we present in this paper a simpler protocol making use of adaptor signatures, detail the handling of contracts with numerical outcomes, and how to create them using multiple oracles. We also provide some algorithmic optimizations to speed up contract creation and experimental results on performance highlighting their impact and demonstrating the useability of the overall system.
An improved compression algorithm based on IDN model of image super-resolution reconstruction

At present, the convolutional neural network is deepening in level and has a huge amount of computation, so it is difficult to realize application in scenarios with low computing capacity. Therefore, this paper proposes a method based on channel pruning and weight quantization to reduce the amount of computation and compress the image super-resolution to reconstruct the network model IDN. Experimental results show that the proposed method effectively compresses the model structure, greatly shortens the calculation time of the model and makes the model more lightweight under the premise that the performance indexes are basically unchanged.
A Survey on Skills of DevRel professionals

Context: Developer Relations (DevRel) is an organizational area responsible for maintaining synergy among developer communities and organizations. This team can be seen in companies like Amazon, Apple, Google, and Microsoft, with a business model based on software ecosystems (SECO). A DevRel team has roles that perform specific activities (e.g., hackathons, developer programs) to achieve the organization’s goals and a better developer experience. Problem: The lack of clarity regarding the nature and set of skills needed by a DevRel professional can negatively impact the performance of related activities and consequently the purpose of SECO. Solution: We investigated a set of DevRel skills for education/training purposes. IS theory: This paper was generated from the perspective of the Theory of Organizational Creativity. This theory provides the key to understanding organizational effectiveness and survival. Method: We performed an opinion survey with 24 participants from ten countries. Results: The nature of DevRel’s professional skills is soft skills in which communication, willingness to learn, teamwork, interpersonal, and listening skills stand out. Hard skills have their share of contribution to DevRel activities and marketing and software programming/development skills stand out. Contributions and Impact in the IS area: The main contribution of the paper is to make a product viable for any organization or individual to be able to develop the skills of a DevRel professional. Topics: Digital and business ecosystems
Instantaneous Rotational Speed Measurement of Wind Turbine Blades using a Marker-Tracking Method

Rotational speed of wind turbine blades is an important parameter reflecting the operating and structural health conditions of wind turbines. Traditionally, the rotor speed is measured using inertial measuring units (IMUs). The biggest issue with IMUs is that the measurements drift over time and require constant calibration. This paper presents a direct way of measuring the instantaneous rotational speed of wind turbine blades using a camera and a marker-tracking method. The proposed measurement method is assessed by conducting a series of simulation experiments under different conditions – fixed speed, stepped varying speed, and linear varying speed. Results demonstrate that when using a camera with a frame rate of 30 fps (frames per second), the marker-tracking method yields a relative error within ±0.5% at the speed between 5 to 30 rpm (revolutions per minute). Compared to the image-correlation method, the marker-tracking method provides better results in terms of accuracy and reaction time, especially under varying speed conditions.
GRSPOI: A Point-of-Interest Recommender Systems for Groups Using Diversification

Context: With the massive availability and usage of the Internet, the search for Points of Interest is becoming an arduous task. Thus, Points of Interest Recommender Systems arise to help users in the search. These systems traditionally recommend points of interest to individual users, however, there are scenarios in which individuals gather, therefore creating the need to recommend items to groups. Problem: The problem is that users’ location is not always considered, only their preferences. Hence, there are studies indicating the greater is users commuting, the less POIs relevance appears to them. Furthermore, the recommendations belong to the same category, without diversity. Solution: Develop a Points of Interest Recommendation System for a group using a diversity algorithm, based on members’ preferences and their locations. IS Theory: This work was conceived in the light of the General Theory of Systems, in particular open systems as they undergo interactions with the environment where they can be inserted. Recommender systems depend on a continuous exchange of information with the external environment. Method: The research is based on the literature, and its evaluation was carried out through an online experiment with real users. The analysis of the results was carried out with a qualitative approach. Summary of Results: Precision metrics were used in the evaluation, and it was observed that the level at which the results are analyzed is relevant. For the top-3, recommendations without diversity performed better, but at the top-5 and top-10 levels, diversification had a positive impact on the results. Contributions and Impact in the IS area: A recommendation system for groups that considers the geographic location of users, their preferences and the diversity of recommendations. In addition, we provide the community with a dataset with user ratings of points of interest and geolocation information.
Subspace-based outlier detection using linear programming and heuristic techniques

A useful strategy to perform outlier detection (OD) in highdimensional data, especially in the presence of multiple classes of outliers, is to decompose the outlier detection problem into a set of relevant subspace selection (RSS) problems. In this approach, one relevant subspace is selected locally for each data point, and then the outlierness of that data point within its relevant subspaces is evaluated. The k-nearest neighbor algorithm has been used in most local-based subspace selection methods for OD application in high dimensional data. However, such techniques suffer from the curse of dimensionality, exhibit high computational complexity, and also, it may not be suitable for arbitrary distributions of the data. Additionally, the subspace nearest neighbor search (SNNS) problem in high dimensional data is rarely discussed in these methods. In this paper, we formulate the local selection of relevant subspace method for OD problem that employs a linear programming (LP) model with the objective function of local sparsity maximization. Our formulation for local selection of relevant subspace as an LP also considers the subspace nearest neighbor search problem to simultaneously solve these two subproblems (local selection of relevant subspace and subspace nearest neighbor search) that are tightly coupled. In addition, a novel heuristic algorithm, with linear complexity with respect to the number of dimensions, is developed to solve the LP model through an iterative procedure that alternates between two algorithms of subspace nearest neighbor search and RSS. Experimental results on both synthetic and real datasets show the feasibility of our formulation, the effectiveness of the heuristic algorithm, and also show the superiority of our proposed subspace outlier detection algorithm based on linear programming and heuristic techniques (SODLPH) to other published studies in terms of detection accuracy. Assessing by the average of the AUC on seven real-world UCI datasets, the performance of our proposed method has improved at least 8.72% in comparison with other related published works.
On Bifurcations Along the Spiral Organization of the Periodicity in a Hopfield Neural Network

In this work we report numerical results involving a certain Hopfield-type three-neurons network, with the hyperbolic tangent as the activation function. Specifically, we investigate a place of a two-dimensional parameter-space of this system where typical periodic structures, the so-called shrimps, are embedded in a chaotic region. We show that these structures are organized themselves as a spiral that coil up toward a focal point, while undergo period-adding bifurcations. We also indicate the locations along this spiral in the parameter-space, where such bifurcations happen.
DarSpeech: An Automatic Speech Recognition System for the Moroccan Dialect

Due to the continuous increase of information and data, it has been proven that Automatic Speech Recognition (ASR) systems are more efficient and less expensive when it comes to a variety of important tasks, such as customer relationship management. However, the most complex and accurate speech recognition models are developed and implemented for languages in which data is highly available, such as English and French. This document proposes an automatic speech recognition system for the Moroccan dialect, a very low-resource language, that is spoken by almost every Moroccan citizen and adopted in many organizations that are both public and private. The proposed solution is based on a state-of-the-art architecture, named Deep Speech 2 by Baidu. We tested the model on 24 hours of speech and obtained 22.7% word error rate and 6.03% character error rate.
PNCF: neural collaborative filtering based on pre-trained embedding

In this paper, we propose the recommendation algorithm PNCF for neural networks. We designed a pre-training task for a distributed representation of embeddings based on many-to-many information. We used the word2vec technique in natural language processing to implement the embedding of users and items. We also constructed a brand-new video website tagauthor pre-training dataset. The code in this paper was implemented in PyTorch and is publicly available on GitHub (github.com/jannchie/ PNCF).
On The Formalization of The TOGAF Content MetaModel Using Ontologies

Ontology, when well designed, provides a semantic model that facilitates human interpretation of models and reduces their ambiguity and complexity. A formal-based standards ontology can be processed automatically by computer programs, providing several benefits such as automatic knowledge discovery and inference, inconsistency detection, and interoperability. Several frameworks have been developed to control the complexity of the enterprise architecture. This paper focuses on the TOGAF framework and precisely on its content metamodel. It analyses and discusses the reported approaches to its formalization using ontologies. Additionally, it proposes some possible future directions to improve this work and enhance its adoption by the industrial world and academic communities.
Abstract Interpretation, Hoare Logic, and Incorrectness Logic for Quantum Programs

Abstract interpretation, Hoare logic, and incorrectness (or reverse Hoare) logic are powerful techniques for static analysis of computer programs. All of them have been successfully extended to the quantum setting, but largely developed in parallel. In this paper, we examine the relationship between these techniques in the context of verifying quantum while-programs, where the abstract domain and the set of assertions for quantum states are well-structured. In particular, we show that any complete quantum abstract interpretation induces a quantum Hoare logic and a quantum incorrectness logic, both of which are sound and relatively complete. Unlike the logics proposed in the literature, the induced logic systems are in a forward manner, making them more useful in certain applications. Conversely, any sound and relatively complete quantum Hoare logic or quantum incorrectness logic induces a complete quantum abstract interpretation. As an application, we are able to show the non-existence of any sound and relatively complete quantum Hoare logic or incorrectness logic if tuples of local subspaces are taken as assertions.