Reviewerly/exHarmony huggingface dataset:
Efficient Dependency Analysis for Rule-Based Ontologies

Several types of dependencies have been proposed for the static analysis of existential rule ontologies, promising insights about computational properties and possible practical uses of a given set of rules, e.g., in ontology-based query answering. Unfortunately, these dependencies are rarely implemented, so their potential is hardly realised in practice. We focus on two kinds of rule dependencies – positive reliances and restraints – and design and implement optimised algorithms for their efficient computation. Experiments on real-world ontologies of up to more than 100,000 rules show the scalability of our approach, which lets us realise several previously proposed applications as practical case studies. In particular, we can analyse to what extent rule-based bottom-up approaches of reasoning can be guaranteed to yield redundancy-free “lean” knowledge graphs (so-called cores) on practical ontologies.
Unrolling Multi-channel Weighted Nuclear Norm Minimization for Image Denoising

We propose an unrolled deep network that integrates the flexibility of model-based algorithms and the advantages of learning-based algorithms. Specifically, based on the multi-channel optimization model for real color image denoising under the weighted nuclear norm minimization formulation, we propose an algorithm for image denoising that can learn the weights for nuclear norm from training datasets through end-to-end training. Experimental results show that the proposed algorithm achieves better performance than traditional iterative algorithms.
Unstructured Data Analysis of Communication Network Based on Deep Learning

This goal has continued to drive academia and industry to investigate, design and develop different solutions to reduce the risk of data breaches. With the increasing demand for large labeled datasets, there is an urgent need for accurate and efficient evaluation of unstructured packet annotations. Second, some non-independent reliability indicators are used repeatedly, exaggerating the determinant role of this indicator on the target. Therefore, it is necessary to estimate the interactive traffic, and through the traffic estimation, relevant data such as the destination of the effective information flow, the traffic attributes, and the legality of the traffic can be obtained. The application of deep learning and natural language processing techniques to automatically identify user confusion in unstructured crowdsourced data labels is explored. The dataset contains images, computer-generated questions for each image, and responses from social media users. Existing data-driven techniques mainly employ traditional shallow learning models, such as Bayesian networks, neural networks, etc. However, it is usually suitable for abnormal data types with obvious characteristics and has poor universality. Mass data information interacts in the form of data traffic, and the information content and its security are directly related to the security of the entire communication system. Therefore, traditional desensitization of unstructured data mainly focuses on the network security rights control, access control and other means, which directly prohibits the leakage of unstructured data. Data extraction can be understood as the acquisition of non-defective semantic data in unstructured tables. Previous data extraction methods were mainly collected manually, which resulted in a large number of subjectivity, resulting in lower extraction accuracy and limited extraction range. The migration between different desensitization systems is poor, and the consequence of mode rigidity is that the desensitization results in a greater loss of data information. The in-depth learning model first reduces the dimension of the reliability index, then predicts the reliability based on the reliability characteristics after dimension reduction. In this paper, the in-depth learning model can be used to analyze unstructured data of communication network and complete reliability prediction in actual operation data.
Inferring Probabilistic Reward Machines from Non-Markovian Reward Signals for Reinforcement Learning

The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies. In recent years, the use of reward machines has relaxed this assumption by enabling a structured representation of non-Markovian rewards. In particular, such representations can be used to augment the state space of the underlying decision process, thereby facilitating non-Markovian reinforcement learning. However, these reward machines cannot capture the semantics of stochastic reward signals. In this paper, we make progress on this front by introducing probabilistic reward machines (PRMs) as a representation of non-Markovian stochastic rewards. We present an algorithm to learn PRMs from the underlying decision process and prove results around its correctness and convergence.
Detection of Mammogram Using Improved Watershed Segmentation Algorithm and Classifying with Feed Forward Neural Network (FNN)

Segmentation of breast tumors with more accuracy using computerized methods is essential for breast cancer monitoring and quantification. Both segmentation and classification of breast tumors using a fully automated or Computer-Aided Diagnosis system poses various problems in terms of imaging properties. In this work, a new hybrid algorithm is proposed for segmentation with a two-step process. Initially, a watershed transformation is applied to separate all basins based on pixel density variation from the mass present in tumors, since it has been quite booming in the presence of tumors in all circumstances. Though this is very perceptive to tiny fluctuations in the size of the image, large numbers of areas are produced unacceptably, and the boundaries after segmentations are also quite hard. The second level set is an effective method of segmenting all types of medical images because; it easily flows with, cavities, folds, splits, and merges. To make the recognition step easier and more accurate, the result of segmentation is considered the beginning position of the curve, and the same will be used at the next step of the level set. This produces a closed, smooth, and accurately placed contour or surface. As a result, the present research uses watershed segmentation to isolate tumor regions and performs classification using Feed Forward Neural Network (FNN) to extract features for classification. Experimental results are evaluated based on performance and quality analysis. In the classification process, the study obtained an accuracy rate of 91.2% in the learning model and 71.8% in a testing model.
Graph-Based Mechanism to Prevent Structural Attack Over Social Media

Social media sites contain the personal information of the users, which entice the attackers. The attacker performs different types of attacks on the social media site to get the user’s sensitive information. User privacy may be breached as the other kind of passive and active attacks are performed on social media sites; to prevent such a scenario, the network operator releases the data in an anonymized form. Social media operators fetch and store data from social media users to share among various third-party consumers. As the fetched information often contains sensitive data, the network operator releases the complete graph in anonymised and sanitised versions. But it does not provide a full guarantee of user privacy. This paper proposed a solution that provides a neighbourhood adjacency matrix based anonymisation process for the social network graph. This anonymisation process may be used to counter the neighbourhood attack over the social network graph. The proposed anonymisation process increases the number of isomorphic neighbourhood networks by adding dummy edges in the social network graph. Therefore, a user may not be re-identified in a social network graph based on its unique neighbourhood network.
Content Based Deep Factorization Framework for Scientific Article Recommender System

With the advancement in technology and the tremendous number of citations available in the digital libraries, it has become difficult for the research scholars to find a relevant set of reference papers. The accelerating rate of scientific publications results in the problem of information overload because of which the scholars spend their 70% of the time finding relevant papers. A citation recommendation system resolves the issue of spending a good amount of time and other resources for collecting a set of papers by providing the user with personalised recommendations of the articles. Existing state of art models do not take high-low order feature interactions into consideration, due to which the recommendations are not up to the desired level of performance. In this paper, we propose a content-based model which combines Deep Neural Network (DNN) and Factorization Machines (FM) where no pre-trainings are required for providing the citation recommendations.
Influence of the implementation of mobile devices in the paradigm shift of education in Latino-America

Society has adopted mobile technologies in a broad context. In this research, we seek to determine the influence mobile devices have had on the teaching-learning process in education in Latino-America through a systematic review of articles related to the subject in the last ten years. It used a qualitative analysis methodology to evaluate and contrast different researcher opinions. This review resumes the factors that involve implementing mobile devices in education and the influence on the teaching-learning processes grouped by the benefits, advantages, disadvantages, and limitations. At the end of this research, it is considered that sociodemographic factors are decisive in implementing mobile devices in education; this implementation must consist of excellent planning and structuring of pedagogical strategies so that mobile devices are functional tools and avoid possible risks.
General ChatBot for Medical Applications

Applications for chat bots (and voice bots) are being used more frequently in customer care or e-commerce as a direct line of communication between businesses and customers. A software programm known as a bot executes automated, repetitive, and pre-defined tasks. For people, it would be nearly impossible to respond to every single client question. The benefit of employing the notion of a bot is that it eliminates the laborious process of manually responding to customer requests. A bot handles everything automatically. Artificial intelligence is a concept that chatbots employ to communicate with people. It has security features like the ability to deactivate accounts that are sending unsolicited messages. We introduce the bot framework in this paper. Bot is a runtime engine that launches the chatbot programme automatically. and controls security. Bot is freely accessible and open source online.
The Large Annotated Corpus for the Arabic Language (LACAL)

Annotated corpora has an important role in the NLP field. They are used in almost all NLP applications: automatic dictionary construction, text analysis, information retrieval, machine translation, etc. Annotated corpora are the basis for training operation in NLP systems. Without these corpora, it is difficult to build an efficient system that takes into account all variations and linguistic phenomena. In this paper, we present the annotated corpus we developed. This corpus contains more than 12 million different words labeled by different types of labels: syntactic, morphological, and semantic. This large corpus adds value to the Arabic NLP field, and will certainly improve the quality of the training phase of Arabic NLP systems. Moreover it can be a suitable corpus to test and evaluate the quality of these systems.
Three Opportunities for Research on the Interdisciplinary Field of “Intellectual Property and Judiciary”

Judicial protection of intellectual property is no longer just the focus of a certain discipline, but the focus of international current affairs. Correspondingly, intellectual property law research is no longer limited to the translation and interpretation of legal texts, but must return to “law in action”. “Law in action” not only gives new life to intellectual property statutes through interpretation and renewal, but also promotes the reform of them. Intellectual property litigation can greatly benefit from the exploration of classic theoretical issues of civil procedure law or procedural law.
Message from the ISMAR 2022 Science and Technology Conference Program Chairs

We are delighted to welcome you to the 21st IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2022) conference program. Following the success of 2021, this year’s conference again featured a separate conference paper track, therefore continuing the clear distinction between the contributions of the journal and conference papers. The Science and Technology program of ISMAR 2022 collates both the journal and conference papers and presents them equally in terms of presentation time and sessions. This supports a clear collegial vision of the contributions within ISMAR 2022 showcasing cutting edge research in our field. We are confident this program will inspire further research while provoking discussions and creating a platform for co-creation of future work.
Implementation of Single Sign-on System for User Management at PT.XYZ

PT.XYZ is an educational consulting company. In carrying out its activities, PT. XYZ has many applications; this sometimes causes frequent human errors, such as employees forgetting passwords on an application because of the many applications and credentials that need to be remembered. This research aims to implement a Single Sign-On System (SSO), which can facilitate the management of web application users at PT.XYZ. With the implementation of SSO, it is expected that users can more easily access any web application and manage their credentials and make it easier for admins to manage user authorization for available web applications. The researcher collected data to strengthen the justification of the problems experienced by using observation and questionnaires distributed to twenty employees. The development of this system uses the waterfall method. For application security, penetration tests are carried out on SSO login authentication in the form of Dictionary and Rainbow table attacks
SHARING RHYMES I HAVE TEN LITTLE FINGERS

Nursery WorldVol. 2020, No. 13 Nursery RhymesSHARING RHYMES I HAVE TEN LITTLE FINGERSPenny TassoniPenny TassoniSearch for more papers by this authorPenny TassoniPublished Online:10 Oct 2020https://doi.org/10.12968/nuwa.2020.13.26AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 October 2020Volume 2020Issue 13ISSN (print): 0029-6422ISSN (online): 2059-9358 Metrics History Published online 10 October 2020 Published in print 2 October 2020 Information© MA Education LimitedPDF download
Multi-View Gated Graph Convolutional Network for Aspect-Level Sentiment Classification

Aspect-Level Sentiment Classification aims to identify the sentiment polarity of each aspect in a sentence. Syntax-based graph neural networks have been used to model dependencies between opinion words and aspects with good results. However, the analysis of these works is highly dependent on the quality of the dependency graph and may achieve suboptimal results for comments with ambiguous syntax. We explore a novel Multi-View Gated Graph Convolutional Network (MGGCN) to address the above problems. We utilize a Gated Graph Convolutional Network (GateGCN) for a more reasonable interaction of syntactic dependencies and semantic information, where we refine our syntactic dependency graph by adding sentiment knowledge and aspect-aware information to the dependency tree. We use the Inter-aspect Graph Convolutional Network (InterGCN) to capture information about the sentiment dependencies between multiple aspects that appear in a sentence. Finally, by adaptively learning multi-view sentiment information through Simple Residual Multilayer Perceptron(SResMLP). Experimental results on four public datasets show that our proposed model outperforms state-of-the-art models.
Abstractive Text Summarization Using Hybrid Methods

Text summarization involves the process of constructing a concise, condensed, short, coherent, and fluent summarized version of a comparatively longer document with more text and involves the process of collection and segregation of the text’s major note points. There are broadly two different kinds of approaches that can be used for the process of text summarization - a) Extractive Summarization(selects important sentences as a subset of original text) and b) Abstractive Summarization(introduces new words to create a meaning summary). Hybrid methods try to incorporate a combination of both the above models, extractive and abstractive to summarize the text where the final summary can either be abstractive or extractive based on the relative positioning of the two models.
Sequential Decision Making with “Sequential Information” in Deep Reinforcement Learning

By learning policy directly from high-dimensional visual inputs, e.g., video frames, Deep Reinforcement Learning (DRL) has achieved great successes for solving sequential decision-making problems, where 2D convolutional network is usually adopted for extracting the underlying spatial features. However, such spatial feature extraction methods do not consider the temporal information existed in the input frames. To address this issue, Transformer, 3D convolutional network and Long Short-Term Memory (LSTM) have been used in DRL, but often result in excessive increase of model parameters and computation cost. Furthermore, multiple down-sampling of images will lead to the loss of sequential information. In this paper, we propose a novel model for extracting sequential information, namely temporal aggregation network (TAN). Comparing with existing methods, TAN can extract the sequential information without the needs of multiple downsampling of consecutive images. Moreover, by decoupling the computation between spatial and channel dimensions, a lightweight model is built in TAN. Experiments in classic Atari 2600 games show that our method can improve the efficiency of decision-making of DRL algorithms compared with baselines.
The Implementation of Video Cipher Engine in FPGA

In the video system, a variety of different encryption engines are usually stacked in the FPGA to meet the system requirements for multiple encryption functions, which results in a large waste of FPGA resources. And because this structure is difficult to dynamically allocate the bandwidth of different algorithms according to the requirements of the system, it further aggravates the consumption of FPGA resources. According to the characteristics of video transport stream, this paper introduces a video encryption engine with pipeline structure. The encryption engine can support video data encryption standard DES, triple DES (3DES) and DVB common scrambling algorithm (DVB-CSA) at the same time, and can support electronic codebook (ECB), cipher block chaining (CBC) and reverse cipher block cipher (RCBC) encryption modes at the same time. It can also dynamically allocate bandwidth in different encryption algorithms and modes according to system requirements. The encryption engine can support simultaneous encryption of up to 32 transport stream packets, which can meet the performance requirements of the system line rate, while greatly reducing the requirements for FPGA resources.
Implementation of Innovation and Firm Performance through Market-Oriented Strategy of SME

기업 간의 기술력이 점점 평준화되고 있는 상황에서 기술력을 통한 경쟁우위의 획득은 점점 어려워지고 있다. 이에 따라, 기업들이 지속적으로 경쟁우위를 달성하기 위해 가장 필요한 것은 타깃 소비자의 Needs와 Wants를 파악할 수 있는 통찰력이다. 이러한 시장 지향성(Market Orientation)에 관한 연구는 대부분 대기업을 중심으로 이루어져 왔지만 본 연구에서는 국내 경제에서 중요한 역할을 하는 중소기업을 대상으로 시장 지향성에 대해 논의하고자 한다. 시장 지향성을 고객 지향성, 경쟁사 지향성, 상호 기능 간 조정으로 분류한 선행연구를 바탕으로 중소기업의 시장 지향성이 기업의 경영 성과와 제품 및 서비스 혁신에 어떠한 영향을 미치는지 연구하였다. 표본은 국내 유명 시장조사 기관에서 중소기업 CEO 및 임원과의 전화 인터뷰를 통해 제조업과 서비스업을 각각 150부씩 총 300부를 수집하였다. 그 결과, 시장 지향성은 경영 성과에 직접적으로 영향을 미치지 않고, 혁신성을 통해서 경영 성과에 유의미한 영향을 미치는 완전 매개로 분석되었다. 더불어 기업의 사회적 책임(CSR) 정도에 따라 혁신성에 미치는 영향도 상이한 것으로 나타났다. 따라서, 중소기업은 고객이 원하는 혁신적인 제품과 서비스를 지속해서 제공하는 시장 지향적인 관점을 통해 우수한 경영 성과를 달성할 수 있고, CSR을 통해 경쟁력을 더욱더 강화할 수 있다. In a market where technology is becoming increasingly more equalized, achieving a competitive advantage through technology is growing more challenging. Accordingly, the most important step for companies to take to continuously maintain a competitive advantage is to gain insights into the needs and wants of their target customers. Although most studies on market orientation have been focused on large enterprises, this study intends to discuss market orientation targeting small- and medium-sized enterprises (SMEs) that play an important role in the domestic economy. This study was conducted to determine whether the market orientation of SMEs affects firm performance through product and service innovation. A total of 300 phone interviews were conducted with CEOs and executives of SMEs at well-known research institutes in Korea. As a result, market orientation was analyzed as a complete mediator that does not directly affect business performance but significantly affects firm performance through innovation. In addition, the difference according to the degree of corporate social responsibility (CSR) was also found to have a significant effect on innovation. Therefore, the market orientation of SMEs can achieve excellent firm performance by continuously providing innovative products and services desired by customers, and CSR can further strengthen the competitiveness of SMEs.
Personalized Ranking Mechanism Using Yandex Dataset on Machine Learning Approaches

Web service technology is extensively utilized as data source by every client. As the quantity of Web information develops quickly, search engines are capable to retrieve data based on the customer preferences. Users now rely on the Internet to meet their information needs, but current search engines often return a long list of results despite using sophisticated document indexing algorithms, many of which were not constantly applicable to the needs of the customer. Because a customer has a precise aim in mind when looking for data, personalized exploration will deliver outcomes that precisely match the user’s plan and purpose. Query-based investigate is commonly used by businesses to assist customers in finding information and products on their Websites. We look at how to rank a collection of outcomes returned in reply for a query in the most efficient way possible. Based on a customer search and click record, we propose a personalized ranking mechanism. We present our Yandex personalized Web search challenge solution. The goal of this challenge was to personalize top-N document rankings for a group of test users using historical search logs.
Cognitive Architecture for Decision-Making Based on Brain Principles Programming

We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical applications as well as for the subject domain ontologies based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.