Reviewerly/exHarmony huggingface dataset:
Probabilistic learning on manifolds

This paper presents novel mathematical results in support of the probabilistic learning on manifolds (PLoM) recently introduced by the authors. An initial dataset, constituted of a small number of points given in an Euclidean space, is given. The points are independent realizations of a vector-valued random variable for which its non-Gaussian probability measure is unknown but is, a priori, concentrated in an unknown subset of the Euclidean space. A learned dataset, constituted of additional realizations, is constructed. A transport of the probability measure estimated with the initial dataset is done through a linear transformation constructed using a reduced-order diffusion-maps basis. It is proven that this transported measure is a marginal distribution of the invariant measure of a reduced-order Itô stochastic differential equation. The concentration of the probability measure is preserved. This property is shown by analyzing a distance between the random matrix constructed with the PLoM and the matrix representing the initial dataset, as a function of the dimension of the basis. It is further proven that this distance has a minimum for a dimension of the reduced-order diffusion-maps basis that is strictly smaller than the number of points in the initial dataset.
Penerapan Metode SCRUM dalam Pengembangan Sistem Informasi Layanan Kawasan

The development of technology is very influential in the business processes of an organization to be able to carry out its duties and functions. As a government agency engaged in research, the Indonesian Institute of Sciences (LIPI) needs to make organizational changes to support its vision as a world-class research institution. One of the first steps taken is reorganizing and redistributing employees that have a high impact on the business process of service to employees because the supporting resources are placed corporately and no longer in the work units. To deal with this problem, we developed a Regional Service Information System using the Scrum methodology. The output is a web-based software that facilitates service requests needed by employees, ranging from service submission, processing by the Area Manager and Central Manager, to being received again by the service requester. The Regional Service Information System is expected to be a solution to the problems that arise as a result of the redistribution of employees at LIPI and to improve the effectiveness of employees as the research supporting resources.
An ExpTime Upper Bound for ALC with Integers

Concrete domains, especially those that allow to compare features with numeric values, have long been recognized as a very desirable extension of description logics (DLs), and significant efforts have been invested into adding them to usual DLs while keeping the complexity of reasoning in check. For expressive DLs and in the presence of general TBoxes, for standard reasoning tasks like consistency, the most general decidability results are for the so-called ω-admissible domains, which are required to be dense. Supporting non-dense domains for features that range over integers or natural numbers remained largely open, despite often being singled out as a highly desirable extension. The decidability of some extensions of ALC with non-dense domains has been shown, but existing results rely on powerful machinery that does not allow to infer any elementary bounds on the complexity of the problem. In this paper, we study an extension of ALC with a rich integer domain that allows for comparisons (between features, and between features and constants coded in unary), and prove that consistency can be solved using automata-theoretic techniques in single exponential time, and thus has no higher worst-case complexity than standard ALC. Our upper bounds apply to some extensions of DLs with concrete domains known from the literature, support general TBoxes, and allow for comparing values along paths of ordinary (not necessarily functional) roles.
Reversible data hiding in encrypted binary image with shared pixel prediction and halving compression

Abstract In this paper, a new reversible data hiding scheme in encrypted binary image is proposed by shared pixel prediction and halving compression. We first divide binary image into non-overlapping pure color blocks and non-pure color blocks, and then partition each non-pure color block as shared pixels and embeddable pixels by a cross-segmentation mechanism. Then, a location sequence which marked all blocks is further compressed by halving compression and is considered as auxiliary data. Subsequently, after performing image encryption, data hider embeds additional data into the pixels of pure color blocks and embeddable pixels of non-pure color blocks. Finally, separable operations of data extraction, direct image decryption, and image recovery are conducted by the receiver. With shared pixel prediction mechanism, perfect image recovery can be achieved. Extensive experiments demonstrate that compared with existing methods, our scheme can obtain a higher visual quality of decrypted images, while maintaining a larger embedding capacity.
Artificial intelligence applications An Introduction to Education Development in the light of Corona Virus Pandemic (COVID-19) Challenges

The present study aimed to identify artificial intelligence and its applications that can be used to develop the educational process in light of the challenges of the Corona Virus Pandemic (COVID-19), and the study adopted the descriptive approach, by extrapolating and analyzing studies, researches, books and periodicals that relate to its subject.For the purposes of the study, an open questionnaire was designed to find out the most important problems and challenges facing the educational process and the role of artificial intelligence applications in facing these challenges.It was presented to some of the officials responsible for the educational process in university and preuniversity education Who numbered (31).The study found that there are several challenges and problems related to the following aspects: (the educational process -the educational administration -the teacher -the learner the parents -the evaluation of the learners) in light of the Corona crisis, including: Limited availability of teachers and digital infrastructure in the educational environment, Weak interest in training teachers and learners to use modern technologies, And rely completely in the educational process on paper books.It also concluded that through the use of some applications of artificial intelligence in the educational process, such as smart education systems, Virtual reality technology (VR) and augmented reality (AR), Layer applications, Aurasma, and Augmented 4 applications, among others in the face of some of those challenges and problems.The study presented some recommendations according to its findings, among the most important: The need to adopt some applications of artificial intelligence in educational
Representation Learning of Knowledge Graphs with Embedding Subspaces

Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.
Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification

In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.
Argumentation Semantics under a Claim-centric View: Properties, Expressiveness and Relation to SETAFs

Claim-augmented argumentation frameworks (CAFs) constitute a generic formalism for conflict resolution of conclusion-oriented problems in argumentation. CAFs extend Dung argumentation frameworks (AFs) by assigning a claim to each argument. So far, semantics for CAFs are defined with respect to the underlying AF by interpreting the extensions of the respective AF semantics in terms of the claims of the accepted arguments; we refer to them as inherited semantics of CAFs.&lt;br&gt;A central concept of many argumentation semantics is maximization, which can be done with respect to arguments as in preferred semantics, or with respect to the range as in semi-stable semantics. However, common instantiations of argumentation frameworks require maximality on the claim-level and inherited semantics often fail to provide maximal claim-sets even if the underlying AF semantics yields maximal argument sets. To address this issue, we investigate a different approach and introduce claim-level semantics (cl-semantics) for CAFs where maximization is performed on the claim-level. We compare these two approaches for five prominent semantics (preferred, naive, stable, semi-stable, and stage) and relate in total eleven CAF semantics to each other. Moreover, we show that for a certain subclass of CAFs, namely well-formed CAFs, the different versions of preferred and stable semantics coincide, which is not the case for the remaining semantics. We furthermore investigate a recently established translation between well-formed CAFs and SETAFs and show that, in contrast to the inherited naive, semi-stable and stage semantics, the cl-semantics correspond to the respective SETAF semantics. Finally, we investigate the expressiveness of the considered semantics in terms of their signatures.
Duality for Instantial Neighbourhood Logic via Coalgebra

Instantial Neighbourhood Logic (INL) has been introduced recently as a language for neighbourhood frames where existential information can be given about what kind of worlds occur in a neighbourhood of a current world. Apart from its semantics, its proof theory and bisimulation games have also been studied. However, conspicuously absent from the treatment of INL is the notion of descriptive frames. This is the gap that we are closing in this paper. We introduce descriptive frames for INL and we prove that these are dual to boolean algebras with instantial operators (BAIOs), which give the algebraic semantics of INL. Our methods for establishing this duality make essential use of coalgebra: we observe that BAIOs are algebras for a functor on the category of boolean algebras and show that this functor is dual to the double Vietoris functor (i.e. the composition of the Vietoris functor with itself), thus obtaining a dual equivalence between double Vietoris coalgebras and BAIOs. The proof of our main result is then completed by showing that double Vietoris coalgebras correspond precisely to descriptive frames. As a corollary we obtain that every extension of INL is sound and complete with respect to descriptive frames, that descriptive frames enjoy the Hennessy-Milner property, and as a result, that finite neighbourhood frames enjoy the Hennessy-Milner property.
Modelling the Polysemy of Spatial Prepositions in Referring Expressions

In previous work exploring how to automatically generate typicality measures for spatial prepositions in grounded settings, we considered a semantic model based on Prototype Theory and introduced a method for learning its parameters from data. However, though there is much to suggest that spatial prepositions exhibit polysemy, each term was treated as exhibiting a single sense. The ability for terms to represent distinct but related meanings is unexplored in the work on grounded semantics and referring expressions, where even homonymy is rarely considered. In this paper we address this problem by analysing the issue of reference using spatial language and examining how the polysemy exhibited by spatial prepositions can be incorporated into semantic models for situated dialogue. We support our approach on theoretical developments of Prototype Theory, which suggest that polysemy may be analysed in terms of radial categories, characterised by having several prototypicality centres. After providing a brief overview of polysemy in spatial language and a review of the related work, we define the Baseline Model and discuss how polysemy may be incorporated to improve it. We introduce a method of identifying polysemes based on `ideal meanings' and a modification of the `principled polysemy' framework. In order to compare polysemes and aid typicality judgements we then introduce a notion of `polyseme hierarchy'. Subsequently, we test the performance of the extended Polysemy Model by comparing it to the Baseline Model as well as a data-driven model of polysemy which we derive with a clustering algorithm. We conclude that our method for incorporating polysemy into the Baseline Model provides significant improvement. Finally, we analyse the properties and behaviour of the generated Polysemy Model, providing some insight into the improvement in performance, as well as justification for the given methods.
WOLED: A tool for Online Learning Weighted Answer Set Rules for Temporal Reasoning Under Uncertainty

Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present WOLED, a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a crisp version of the algorithm that learns unweighted rules, on CER datasets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel implementation, both in terms of efficiency and predictive performance.
Exploring dynamic multilayer graphs for digital humanities

Abstract The paper presents Intergraph, a graph-based visual analytics technical demonstrator for the exploration and study of content in historical document collections. The designed prototype is motivated by a practical use case on a corpus of circa 15.000 digitized resources about European integration since 1945. The corpus allowed generating a dynamic multilayer network which represents different kinds of named entities appearing and co-appearing in the collections. To our knowledge, Intergraph is one of the first interactive tools to visualize dynamic multilayer graphs for collections of digitized historical sources. Graph visualization and interaction methods have been designed based on user requirements for content exploration by non-technical users without a strong background in network science, and to compensate for common flaws with the annotation of named entities. Users work with self-selected subsets of the overall data by interacting with a scene of small graphs which can be added, altered and compared. This allows an interest-driven navigation in the corpus and the discovery of the interconnections of its entities across time.
Virtual and Augmented Reality as a Digital Support to HR Systems in Production Management

Digital transformation has disrupted every domain of production management, as well as the human resource management as one of the production manager's most important responsibilities. Besides of product and process design and issues involving planning and control of quality and capacity, production management also includes responsibility for organization and supervision of the workforce. In new industry operations, it is important to anticipate human resource requirements, as well as to translate them into recruiting and training programs in order to develop a base of highly skilled and qualified workers capacitated for installed machinery and equipment. According to the relevant literature review, integration of virtual and augmented reality technology in any business process has a proven potential for employee engagement improvement and memory retention capacity. The aim of this research is to provide a comprehensive summary of using VR and AR technologies as a digital support for managing human elements in operating systems. This study found that VR and AR can be successfully used in improving workers' productivity, enhancing employee training, reducing costs, making worksites safer and closing the skills gap more effectively and efficiently. This study could encourage both academics and practitioners to seek innovative use of VR and AR technologies in a particular industry as digital tools that saves time and costs in human resource management.
BEHAVIOUR CONTROL WITH AUGMENTED REALITY SYSTEMS FOR SHARED SPACES

Abstract. Augmented Reality (AR) in a traffic context has mainly been used in navigation with path augmentation, focused around safely guiding the user with prior knowledge of the route and the destination. Other works are reported to warn drivers by visualizing other traffic participants or dangers, which are yet currently out of sight. However they do not cover aspects of mediating control by recommending users with actions, even when such efforts are expected to foster collaboration in a multiagent environment. To the best of our knowledge, AR has not yet been applied to visualize virtual control information, e.g. virtual lanes or signposts, notably in the context of shared spaces. Such an environment should support spatial understanding of proximate participants with adaptive augmented controls to recommend actions to each user. However when such systems work in context where a conflict of interest would arise, a rule based control logic centered on priority should be accounted for. Traditionally, these rules are defined by traffic management. This paper presents a Behaviour Control with AR (BCAR) Systems based framework for control of user behaviour in a shared space via augmentation and proposes how a control logic can be part of it. The framework which incorporates navigation focuses on mapping users from real to the virtual world .This framework also enables simulations and visualization of multiagent interactions and proposing controls for user actions leveraging the environment complexity reduction achieved through the real to virtual transfer. A prototype implementation of the proposed framework with ARCore and unity3D has been evaluated for pedestrian behaviour control to understand its feasibility.
Implementation Of Long Short-Term Memory (LSTM) For User Authentication Based On Keystroke Dynamics

The paper reviews the usage, development and implementation of keystroke dynamics as a viable authentication system. AI methods are advancing, but we are still lacking biometric authentication systems in modern software which is being used daily. This paper shows the usage of long short-term memory layers for solving problems like keystroke dynamics and efficiently shows that with modern hardware, training and maintaining a small model is not taxing on the resources, as it may have been.
PREPROCESSING ARABIC DIALECT FOR SENTIMENT MINING: STATE OF ART

Abstract. Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies, especially for sentiments written in non-structured or semi-structured languages. In this paper, we present a literature review on the pre-processing task on the field of sentiment analysis and an analytical and comparative study of different researches conducted in Arabic social networks. This study allowed as concluding that several works have dealt with the generation of stop words dictionary. In this context, two approaches are adopted: first, the manual one, which gives rise to a limited list, and second, the automatic, where the list of stop words is extracted from social networks based on defined rules. For stemming two, algorithms have been proposed to isolate prefixes and suffixes from words in dialects. However, few works have been interested in dialects directly without translation. The Moroccan dialect in particular is considered as the 5th dialect studied among Arabic dialects after Jordanian, Egyptian, Tunisian and Algerian dialects. Despite the significant lack in studies carried out on Arabic dialects, we were able to extract several conclusions about the difficulties and challenges encountered through this comparative study, as well as the possible ways and tracks to study in any dialects sentiment analysis pre-processing solution.