Reviewerly/exHarmony huggingface dataset:
Nested OSTRICH

Low-code frameworks strive to simplify and speed-up application development. Native support for the reuse and composition of parameterised coarse-grain components (templates) is essential to achieve these goals. OSTRICH --- a rich template language for the OutSystems platform --- was designed to simplify the use and creation of such templates. However, without a built-in composition mechanism, OSTRICH templates are hard to create and maintain.
Developing a Rule-Based Machine-Translation System, Ewondo–French–Ewondo

Machine translation (MT) significantly contributes to democratizing access to textual information across multiple languages and is established as a dynamic language service in the global multilingual society. Not surprisingly, the attractiveness of the MT market has stirred up spectacular innovations, driven by artificial intelligence in the digital technology industry. The commercial stakes of the industry has led to massive investments in the development of automatic translation systems for languages of wider communication and an increased marginalization of minority languages in this avenue. This article reports on the on-going development of a low-tech, rule-based MT system for Ewondo, a Bantu low-resourced language spoken in Cameroon. The project aims to fill the gap in access to MT services in the target minority language community and to generate parallel corpora from and into the Ewondo language.
Body-based user interfaces

The relation between the body and computer interfaces has undergone several shifts since the advent of computing. In early models of interaction, the body was treated as a periphery to the mind, much like a keyboard is peripheral to a computer. The goal of the interface designers was to optimize the information flow between the brain and the computer, using these imperfect peripheral devices. Toward the end of the previous century the social body, as well as the material body and its physical manipulation skills started receiving increased consideration from interaction designers. The goal of the interface designer shifted, requiring the designer to understand the role of the body in a given context, and adapting the interface to respect the social context and to make use of the tacit knowledge that the body has of how the physical world functions. Currently, we are witnessing another shift in the role of the body. It is no longer merely something that requires consideration for interface design. Instead, advances in technology and our understanding of interaction allows the body to become part of the interface. We call these body-based user interfaces. We identify four ways in which the body becomes part of the interface: (1) The interaction might occur on or in the human body, for example using implanted tactile stimulation or touch interfaces on the body. Here the material of the body becomes part of the interface. (2) The interaction changes the morphology of the body and corresponding control structures, for example by providing users with additional skills, such as drawing or playing instruments or additional limbs that help complete complex tasks. Here the shape of the body, or the corresponding ability to act, is affected by the interface. (3) The interaction engages with or modifies how we perceive the world, for example by manipulating the sense of direction in VR or allowing users to experience non-existent stimuli, such as mid-air friction. Here, the idiosyncrasies of multi-modal perception and perceptive acts become part of the user interface. Finally, (4) the interaction might engage with the experience of having a body, for example by manipulating the sense of body ownership, location, or agency. Here the introspective access to one’s own body is used in the design of the interface. In this chapter, we present a brief history of the body’s role in human–computer interaction, leading up to a definition of body-based user interfaces. We follow this by presenting examples of interfaces that reflect the different ways in which interfaces can be body-based. We conclude by presenting outlooks on benefits, drawbacks, and possible futures of body-based user interfaces.
Where is the description?

Smart Home devices (e.g. Alexa Echo dot, Smart TVs) are unique technologies to improve everyday lives, especially for people with disabilities. However, despite the advances provided by these technologies, people who are blind reported accessibility issues in the use of mobile applications that mediate interaction with smart devices. Our study aims to understand these applications' current state of accessibility by analysing their Portuguese versions. First, we surveyed 20 Brazilians who are blind to map the uses and main accessibility challenges of Smart Home devices. Next, we evaluated the accessibility of five mobile applications used in this context: LG ThinQ, Amazon Alexa, openHAB, Google Home, and RSmart. We found a total of 1148 accessibility issues in their Portuguese versions. The most frequent issues were missing or incorrect labels and incorrect context (e.g., content descriptions in English). The tasks that presented the most accessibility problems were the devices' search and configuration. These issues could limit the initial use of intelligent devices by users with disabilities and compel them to rely on someone else to perform essential tasks in these apps, even those created by tech giants.
Resolving Lexical Level Ambiguity: Word Sense Disambiguation for Telugu Language by Exploiting IndicBERT Embeddings

Generally, acquiring proper interpretation of a word or a sentence in a particular context is very important in many natural language applications like machine translation, information retrieval, sentiment analysis, and many others. Performing semantic analysis aims to resolve the misinterpretation of a word or a sentence. Word sense disambiguation (WSD) helps to improve the performance of semantic analysis. In this paper, we have proposed a novel algorithm to solve WSD for the TELUGU language that works based on IndicBERT word embeddings. Our algorithm calculates Cosine similarity between sense and context vectors to assign correct sense. The performance of our algorithm also depends on context window size. In the case of resolving lexical level ambiguity our algorithm has reported an average accuracy of 64.7 and 71.7% for window sizes of 3 and 4, respectively.
Artificial Intelligence Impact on Pattern Classification in Association with IoT for Advanced Applications

Pattern recognition (PR) is the investigation of how machines can look at the earth, determine how to recognize examples of enthusiasm from their experience, and settle on solid and doable choices with respect to classes of the examples. Be that as it may, even after very nearly 80 years of exploration, the plan of an application dependent on design recognizer stays a questionable objective. Additionally, presently, there are immense volumes of information that must be managed, which incorporate picture, video, text and web reports; DNA; microarray quality information; and so on. Among the different structures wherein design acknowledgment has been generally planned, the measurable and AI approaches have been most extensively considered and utilized by and by. As of late, profound learning procedures and techniques have been accepting expanding consideration. The fundamental goal of this audit is to sum up PR applications, withdrawing from the significant calculations utilized for their plan. Hence this chapter enumerates in detail the latest models associated with PR and time series classification accompanied with either a machine or deep learning algorithm for advanced application with case studies of video surveillance systems.
Refraction calibration of housing parameters for a flat-port underwater camera

In underwater computer vision systems, the camera is usually housed in a watertight case with a glass interface between water and air, which causes the light rays to refract, affecting image formation geometrically. In this paper, we propose an approach to calibrate the housing parameters of a flat-port underwater camera for a reasonable underwater refractive camera model. Our approach is specifically designed to deal with the refraction effect caused by the light traveling in different mediums. In this camera model, the calibrated housing parameters include the distance between the camera and glass interface and the normal of the glass interface in the camera coordinate system. We employ an underwater binocular vision system to calibrate the housing parameters in two steps. First, the distance to the glass interface is estimated by capturing a checkerboard image and computing the distance from the camera center to a known flat plane. Then, the normal of the glass interface is calculated by a multiobjective optimization method using geometric constraints in the underwater binocular stereo system. Extensive experiments have been conducted on our underwater dataset and the results verify the effectiveness of the proposed scheme for underwater camera calibration and three-dimensional measurements.
Results of Digital Competence Development for Philology Students within Blended Learning

The relevance of the problem is due to dynamic changes in digital society. The study aims to determine and compare the levels of digital competence development for future philologists-teachers before and after studying the module “Digital Training Tools”. &#x0D; The research methodology provided consideration of the target, content and procedural aspects of digital competence development for future philologists within limits of competence, activity and technological approaches. The methods of comparative analysis of scientific publications and documents on the topic, practical survey methods using Google forms, analysis of received empirical data, generalization of scientific and pedagogical workers’ experience at Ukrainian Language and Literature Faculty named after H. F. Kvitka-Osnovianenko, who ensure the implementation of educational programs in the specialty «Philology».&#x0D; The comparison is conducted with the help of pedagogical methods and appropriate extracurricular activities. It has been found out that the majority of philology students (90 students) of Bachelor Degree qualification in the program subject area «Philology» at the Ukrainian Language and Literature Faculty named after H. F. Kvitka-Osnovianenko and the Faculty of Foreign Philology are at the average level of digital competence development before studying the module “Digital Training Tools”. &#x0D; The results of the study have shown that the number of students at the average level of communication and interaction in the digital society has decreased, it is completely absent at the basic level, and it has increased at the high level. The obtained and compared results after studying the module and conducting extracurricular activities are provided: the number of students at the high level of computer literacy has increased by 17% and the number of those ones at the average level has decreased by 15%. The statistics on the levels of information literacy and ability to work with digital data are given. The results indicate the effectiveness of the offered module and activities. &#x0D; Conclusions. The basics for the digital competence development are defined. They are motivation and awareness of the digital competence necessity by all students in different subjects; availability of technical tools and free digital content with the native-language interface. The prospects for further research are outlined.
Cyber economic espionage: a framework for future research

The study of economic espionage has been dominated by scholarship focused on its legal and legislative aspects. However, economic espionage has important political economy dynamics that have largely gone unexplored. This is unfortunate because recent technological advances and changing dynamics of interstate economic competition mean there is much scope for a progressive research agenda focused on the political economy of cyber economic espionage. In this chapter, I outline key under-researched areas where the most progress is possible. I advocate for studies focused on examinations of the motives of cyber economic espionage, its conduct, how governments respond to it and how it is influenced by economic interdependence.
Blockchain and the law - legality, law-like characteristics, and legal applications

Because of the range of potential applications of blockchain, the technology has a complex relationship with the law. As a novel and economically significant form of organization, blockchain's intersection with the law tends to involve one of three things. First, when describing the most salient application of the technology - to develop and secure cryptocurrency networks - the question tends to deal with the legal status of a given cryptocurrency. Second, blockchains themselves have important law-like characteristics in terms of their effects in constraining and incentivizing network participants to act in the interest of network users. Third, blockchains have several potentially transformative legal applications in terms of facilitating smart contracts and more complex associational forms. In this chapter I detail each of these distinct relationships between blockchain and the law in order to clarify the legal space for blockchain scholars and protocol designers.
Analyzing the publication activity effectiveness of a scientific organization based on the Citation Index (the CEMI RAS case)

Subject. The article addresses the assessment of publication activities effectiveness of an organization engaged in scientific research in the humanities, carried out on the basis of citation indexes of publications of employees. Objectives. The purpose is to evaluate the effectiveness of publication activities of a scientific organization and its employees, based on indicators of citation level, the number of publications, the Hirsch index. Methods. The study rests on general scientific research methods. Results. We performed a comparative analysis of publication activities of three institutes engaged in research in the field of economics, presented an overview of Russian databases, i.e. the Russian Science Citation Index (RSCI), the eLibrary platform, analyzed the effectiveness of publication activities of scientific researchers of the Central Research Institute of the Russian Academy of Sciences by field of science and by academic position, using the indicators of citation index (Hirsch index), the number of publications included in the RSCI, and the number of references from the RSCI. Our analysis demonstrated high efficiency of the CEMI RAS, which to a greater extent depends on the publication activities of its staff in the position of chief and leading researchers. Conclusions. The existing system for evaluating the publication activities of scientific organizations, which rests on quantitative indicators, is effective, though not sufficiently informative. It is required to expand the list of indicators and conduct a peer review of researchers’ publications.
A Model-Free Kullback–Leibler Divergence Filter for Anomaly Detection in Noisy Data Series

Abstract We propose a Kullback–Leibler divergence (KLD) filter to extract anomalies within data series generated by a broad class of proximity sensors, along with the anomaly locations and their relative sizes. The technique applies to devices commonly used in engineering practice, such as those mounted on mobile robots for nondestructive inspection of hazardous or other environments that may not be directly accessible to humans. The raw data generated by this class of sensors can be challenging to analyze due to the prevalence of noise over the signal content. The proposed filter is built to detect the difference of information content between data series collected by the sensor and baseline data series. It is applicable in a model-based or model-free context. The performance of the KLD filter is validated in an industrial-norm setup and benchmarked against a peer industrially adopted algorithm.
CATs++: Boosting Cost Aggregation With Convolutions and Transformers

Cost aggregation is a process in image matching tasks that aims to disambiguate the noisy matching scores. Existing methods generally tackle this by hand-crafted or CNN-based methods, which either lack robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive fields and inadaptability. In this paper, we introduce Cost Aggregation with Transformers (CATs) to tackle this by exploring global consensus among initial correlation map with the help of some architectural designs that allow us to benefit from global receptive fields of self-attention mechanism. To this end, we include appearance affinity modeling, which helps to disambiguate the noisy initial correlation maps. Furthermore, we introduce some techniques, including multi-level aggregation to exploit rich semantics prevalent at different feature levels and swapping self-attention to obtain reciprocal matching scores to act as a regularization. Although CATs can attain competitive performance, it may face some limitations, i.e., high computational costs, which may restrict its applicability only at limited resolution and hurt performance. To overcome this, we propose CATs++, an extension of CATs. Concretely, we introduce early convolutions prior to cost aggregation with a transformer to control the number of tokens and inject some convolutional inductive bias, then propose a novel transformer architecture for both efficient and effective cost aggregation, which results in apparent performance boost and cost reduction. With the reduced costs, we are able to compose our network with a hierarchical structure to process higher-resolution inputs. We show that the proposed method with these integrated outperforms the previous state-of-the-art methods by large margins. Codes and pretrained weights are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ku-cvlab.github.io/CATs-PlusPlus-Project-Page/</uri> .
A Hyperparameter Optimization for Galaxy Classification

In this study, the morphological galaxy classification process was carried out with a hybrid approach.Since the Galaxy classification process may contain detailed information about the universe's formation, it remains the current research topic.Researchers divided more than 100 billion galaxies into ten different classes.It is not always possible to understand which class the galaxy types belong.However, Artificial Intelligence (AI) can be used for successful classification.There are studies on the automatic classification of galaxies into a small number of classes.As the number of classes increases, the success of the used methods decreases.Based on the literature, the classification using Convolutional Neural Network (CNN) is better.Three metaheuristic algorithms are used to obtain the optimum architecture of CNN.These are Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms.A CNN architecture with nine hidden layers and two full connected layers was used.The number of neurons in the hidden layers and the fully connected layers, the learning coefficient and the batch size values were optimized.The classification accuracy of my model was 85%.The best results were obtained using GWO.Manual optimization of CNN is difficult.It was carried out with the help of the GWO meta-heuristic algorithm.
Analyzing and Addressing the Difference in Toxicity Prediction Between Different Comments with Same Semantic Meaning in Google’s Perspective API

Social media has become an essential facet of modern society and is quickly becoming indispensable. The ubiquity of social media, while having its benefits, also opens up people to a world of toxicity that is often left unregulated. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. To tackle these hate comments, Google and Jigsaw together have developed the Perspective API, which detects toxicity and can be used for moderation. Through this study, we highlight that Perspective API poses a discrepancy in determining the toxicity of sentences that are semantically the same but with different arrangements of words. We prove that changing a sentence from active voice to passive voice and sentence reordering while maintaining the semantic meaning leads to a substantial difference in the toxicity of that sentence. Furthermore, we discuss possible methods to overcome this bias and help toxicity prediction models become capable of predicting more consistent results across different sentences with the same semantics terms.
An Empirical Study of Multi-class Imbalance Learning Algorithms

Real-life data are often imbalanced which represent the major hurdle in classification applications. Despite incremental improvements, gaining from this skew datasets remain a core interest of intense research. Deeper insight into the nature of imbalanced learning has been gained with the expansion of machine learning and data mining. This turned out to be more complicated with the arrival of the big data era and new emerging challenges of multi-class classification. Strategies for dealing with imbalanced dataset issues are continually being improved. Recent researches focus on analyzing not just the disparity between classes, yet in addition, different troubles implanted in the nature of data. These motivate to focus on computationally efficient, versatile, and real-time techniques for the imbalanced multi-class classification.
Ontological Engineering of Interrelated Processes in Complex Cyber-Physical Systems

The processes in complex cyber-physical system are always interrelated, which means different processes share the same information. Such sharing requires the robust approach to create, update and maintain the ontologies of processes and cyber-physical system as a whole in order to create well-organized data structure and to avoid duplication of information. The resulting complete ontology of cyber-physical system should be a convenient and flexible basis for modeling processes and ensuring the functioning of information-analytical and system processes. Metagraphs are considered as the basis for building an applied system ontology and extracting the ontologies of processes. A modification of the metagraph is proposed to include events and system methods in the ontology. This approach to the construction of a metagraph allows to include the process component in the ontological model of the system as an integral part of it, which allows to flexibly and with lower time expenses design, update and maintain process models based on segments of the general ontological model. As a basis for modeling, it is proposed to use a temporal neuro-fuzzy variety of Petri nets. An example of the implementation of a software-instrumental environment for ontological engineering and further construction of models of processes of a complex cyber-physical system is presented.