Reviewerly/exHarmony huggingface dataset:
Auto-expansion Approach for Searching in Reduced Set of Documents

Searching contents from a query performed using Natural Language is a hard task when there is a limited time to response and no other information but a reduced set of documents. This paper presents a novel approach to expand queries in Natural Language. The working hypothesis, a short introduction to some concepts of Morphosyntactic Linguistic Wavelets that are of interest, and a use case of the implemented model are within the scope of this paper. Preliminary results indicate the self-expansion proposal is fast, and produces a good number of correct re-phrasings that can be used to recall more documents with proper answers to the original query.
Detection of Duplicate Text Content Using Python Language Modules

One of the biggest problems of the World Wide Web and pub has probably, several times, been experiencing different, but almost identical, or nearly identical content results in a simple Google search. In the case of publishers, duplicate content represents plagiarism or copyright infringement, therefore large financial losses. Google is seriously dealing with content duplication and may penalize duplicate sites or pages as a result. There are many software packages and services that identify and/or solve this problem, but they are not free. The current paper describes how to remove duplicate, or nearly identical, content from a database, using utilities that implement specific algorithms. One of these utilities is Natural Language Toolkit (NLTK) written in Python. NLTK is a platform for creating programs in Python, to work with data in human language. The way to detect duplicate content is described for the applications running on open source, Linux and Windows operating systems.
Smart AI Bot for Healthcare Assistance

The new coronavirus has become a worldwide epidemic. In fact, this is a huge challenge for medical staff and managing the flow of large numbers of affected cases. Therefore, when an epidemic occurs directly, it becomes more difficult to consult a medical expert. Indeed, during this pandemic, progress has been made in the use of health chatbots to combat the spread of the virus. These applications provide a form of instant dialog between humans and artificial intelligence (AI) programs by using voice- and/or text-based commands in natural human language. This research work proposes an intelligent chatbot for the diagnostic of nCOV-19. The chatbot developed is composed of three interdependent modules: an information module (IM), a data module (DM) which collects user information, and an action module (AM) which yields answers to the chatbot.
Privacy-Preserving Data Integration for Digital Justice

The digital transformation of the Justice domain and the resulting availability of vast amounts of data describing people and their criminal behaviors offer significant promise to feed multiple research areas and enhance the criminal justice system. Achieving this vision requires the integration of different sources to create an accurate and unified representation that enables detailed and extensive data analysis. However, the collection and processing of sensitive legal-related data about individuals imposes consideration of privacy legislation and confidentiality implications. This paper presents the lesson learned from the design and develop of a Privacy-Preserving Data Integration (PPDI) architecture and process to address the challenges and opportunities of integrating personal data belonging to criminal and court sources within the Italian Justice Domain in compliance with GDPR.
Identification and Visualization of Legal Definitions and Legal Term Relations

Reading, analyzing, and implementing regulatory documents are cumbersome and still mostly manual tasks. The constantly increasing amount of regulatory documents causes an equally increasing need for supporting those tasks through information systems. One key aspect for accomplishing those tasks is to acquire knowledge of the different legal definitions, i.e., legal terms accompanied by their explanations, in order to build a legal vocabulary or an ontology. This paper proposes an approach taking European regulations as input and i) automatically extracting legal definitions, ii) determining semantic relations such as hyponyms, meronyms, and synonyms between legal terms, and iii) visualizing the results in form of a knowledge graph and statistics. The approach is evaluated on European regulations and made accessible particularly for non-technical users through an easy-to-use web service.
Gleason Grading System for Prostate Cancer Diagnosis

Artificial intelligence (AI) is the ability of a computer to think and behave like a human, without any human intervention. Through techniques like deep learning and neural networks where a machine is shown pictures of what it must classify, the model learns through examples like any other human would. The ability to mimic human intelligence is what makes these systems so accurate, and, when paired with a pathologist, these systems revolutionize cancer detection and patient prognosis. AI can allow pathologists to deal with complex relationships between different types of cancers and their features while enabling them to manage huge chunks of data in the form of high-resolution images. By making use of advanced algorithms, clinicians can remove subjectivity from decision-making which makes the process of diagnosis less biased. This research work is proposing an architecture to leverage the best out of state-of-the-art pretrained models and customize it to classify the type of prostate cancer without the need of a pathologist. This architecture is designed by using deep learning techniques such as CNN and EfficientNet; it is scalable on huge amounts of data, 300 GB tested in this work, and presented an effective way to combat the dataset size conundrum. With the use of such novel techniques, the aim of this work is to help pathologists all around the word to diagnose prostate cancer faster and better.
wGMU: A Novel Fusion Strategy to Identify the Important Parts of Sentence for Relation Classification

The progress in Natural Language Processing (NLP) has increased the research community’s interest in addressing different aspects of Information Extraction (IE). Relation Classification (RC) is one of the topics within IE that involves the identification and classification of semantic relationships present between two entities mentioned in a given text. Typical approaches require the use of complex deep-learning multi-task architectures. With the aim of addressing the same problem but without requiring huge amounts of data and sophisticated deep models, this work explores the possibility of splitting sentences by dividing them into five tokens: three context tokens (initial, intermediate, and final) and two entity tokens (head and tail). Additionally, this paper introduces a new information fusion strategy called weighted Gated Multimodal Unit (wGMU), which combines the token representations to obtain a sentence representation. This approach enables the analysis of the im- importance of each token that composes the sentence in the RC. According to F1-score, the wGMU strategy outperforms the early fusion strategy by up to 6% and the approach based on the original GMU by up to 14%. Moreover, the contribution analysis indicates that the final context token is not crucial in the RC task. Hence, excluding this representation, the model complexity can be reduced.
Using an Ontology for Defining Semantics of Fractal Enterprise Model

The paper discusses experience of using a formal ontology for defining semantics of a modeling language. This is done for an enterprise modeling language called Fractal Enterprise Model (FEM). FEM significantly differs from other enterprise modeling languages; it presents the operational activities of an enterprise using a combination of classes and sets. Although a model in FEM does not explicitly represent such concepts as individuals or time, these concepts should be introduced to formally define FEM's semantics. Thus, the ontology underlying FEM needs to include more concepts than FEM itself. The paper presents FEM in an informal fashion, and then suggest the first version of an ontology suitable for defining its formal semantics, which consist of the taxonomy of elements, formally defined relationships between them and a set of axioms.
Exploring Understandability in Socio-technical Models for Data Protection Analysis: Results from a Focus Group

The understandability of conceptual models depends not only on the model's inner complexity and representation but also on the personal factors of the model's audience. This is critical when conceptual models are used for achieving common ground during the early stages of requirements engineering for information systems and, moreover, for complex domains such as data protection. In this article, we present the results of an exploratory study consisting of eight focus groups with 21 experts on software development, business analysis and data protection, examining socio-technical models of an information system to identify privacy risks. We surveyed participants on their backgrounds to characterize the personal factors of understandability and performed an initial understandability assessment on a socio-technical model. We compared these values with the outcome of the focus group, i.e., the effectiveness of the participants in identifying privacy risks, annotating whether the risks are identified individually by a participant or collaboratively by two or more participants. The results suggest that most of the privacy risks were identified collaboratively, regardless of the previous understandability scores and personal factors such as experience and background.
euFAIR: A Digital Tool for Assessing the FAIR Principles

Over the last decade, the importance of FAIR principles as a reference for data reusability and openness has increased constantly. Various tools for FAIR data assessment exist, including manual approaches like the FAIR Data Self-Assessment Tool (SAT) and automated tools as the FAIR Evaluation Services and Generic Automatic Tool (GAT). However, subjectivity in manual assessment and limited guidance in automated tools represent significant limitations. In such a context, the Italian "Piano Triennale per l'Informatica nella Pubblica Amministrazione" has laid the foundations for open data practices in the Italian Public Administration (PA) since 2017. In this work, we propose a tool called euFAIR for the automatic assessment of dataset FAIRness in the Italian PA. The tool incorporates European Data Quality Guidelines for a more comprehensive dataset evaluation and implements a set of specific ad-hoc designed metrics. With this tool, we aim at improving data quality and sharing, as well as the adherence to community standards in the Italian PA. To validate our results, we compared euFAIR with SAT and GAT, highlighting significant differences.
Assessing the Rigidity of Separate Regions in a Multiple Object Moving Scene in Video

This paper presented a technique for classifying multiple object scenes in video as rigid and non-rigid objects. This paper presented the application of affine motion and clustering methods. Using this technique, we are going to convert the motion into different layers. We are using the “person pushing a cart” sequence. We are able to convert it into three layers. Then present the whole sequence with an image for every layer. Further, we call these images as reference image. This reference image when compared with subsequent image gives some deviation between the similarities of these images with respect to appearance of the image. We use the techniques of others to evaluate the periodicity of these images. Our assumption is that the change of appearances of rigid objects (vehicle) is slower than the non-rigid (human) objects. The presented framework is also applicable to other sequences with multiple motions.
Natural Language Contents Evaluation System for Multi-class News Categorization Using Machine Learning and Transformers

The exponential growth of digital documents has come with rapid progress in text classification techniques in recent years. This paper provides text classification models, which analyze various steps of news classification, where some algorithmic approaches for machine learning, such as Logistic Regression, Support Vector Machine, and Random Forest, are implemented. In turn, the uses of Transformers as classification models for the solution of the same problem, proposing BERT and DistilBERT as possible solutions to compare for the automatic classification of news containing articles belonging to four categories (World, Sports, Business, and Science/Technology). We obtained the highest accuracy on the machine learning side, with 88% using Support Vector Machine with Word2Vec. However, using Transformer DistilBERT, we got an efficient model in terms of performance and 91.7% accuracy for classifying news.
Similarity Search with Multiple-Object Queries

Within the topic of similarity search, all work we know assumes that search is based on a dissimilarity space, where a query comprises a single object in the space. Here, we examine the possibility of a multiple-object query. There are at least three circumstances where this is useful. First, a user may be seeking results that are more specific than can be captured by a single query object. For example a query image of a yellow hot-air balloon may return other round, yellow objects, and could be specialised by a query using several hot-air balloon images. Secondly, a user may be seeking results that are more general than can be captured by a single query. For example a query image of a Siamese cat may return only other Siamese cats, and could be generalised by a query using several cats of different types. Finally, a user may be seeking objects that are in more than a single class. For example, for a user seeking images containing both hot-air balloons and cats, a query could comprise a set of images each of which contains one or other of these items, in the hope that the results will contain both. We give an analysis of some different mathematical frameworks which capture the essence of these situations, along with some practical examples in each framework. We report some significant success, but also a number of interesting and unresolved issues. To exemplify the concepts, we restrict our treatment to image embeddings, as they are highly available and the outcomes are visually evident. However the underlying concepts transfer to general search, independent of this domain.
SDOclust: Clustering with Sparse Data Observers

Sparse Data Observers (SDO) is an unsupervised learning approach developed to cover the need for fast, highly interpretable and intuitively parameterizable anomaly detection. We present SDOclust, an extension that performs clustering while preserving the simplicity and applicability of the original approach. In a nutshell, SDOclust considers observers as graph nodes and applies local thresholding to divide the obtained graph into clusters; later on, observers’ labels are propagated to data points following the observation principle. We tested SDOclust with multiple datasets for clustering evaluation by using no input parameters (default or self-tuned) and nevertheless obtaining outstanding performances. SDOclust is a powerful option when statistical estimates are representative and feature spaces conform distance-based analysis. Its main characteristics are: lightweight, intuitive, self-adjusted, noise-resistant, able to extract non-convex clusters, and built on robust parameters and interpretable models. Feasibility and rapid integration into real-world applications are the core goals behind the design of SDOclust.
Accelerating k-Means Clustering with Cover Trees

The k-means clustering algorithm is a popular algorithm that partitions data into k clusters. There are many improvements to accelerate the standard algorithm. Most current research employs upper and lower bounds on point-to-cluster distances and the triangle inequality to reduce the number of distance computations, with only arrays as underlying data structures. These approaches cannot exploit that nearby points are likely assigned to the same cluster. We propose a new k-means algorithm based on the cover tree index, that has relatively low overhead and performs well, for a wider parameter range, than previous approaches based on the k-d tree. By combining this with upper and lower bounds, as in state-of-the-art approaches, we obtain a hybrid algorithm that combines the benefits of tree aggregation and bounds-based filtering.
The Advantages of Quantitative Management in Decision

There are two different kinds of uncertainties in the objective world: the uncertainty in occurrence of uncertain event; as well as the uncertainty in degree of the event. Though there are many uncertain events treated by human being have been used by means of probability theoretical modeling, nevertheless, it is just suitable for those events, which are occurred under uncertain in its occurrence.
Quantitative Risk Decision of Overseas Projects

Project risk is come from uncertainties of the project environment; if there is every thing is certain, there will be no risk at all. Unfortunately, there are more uncertainties in the overseas market; therefore, there will be more risky for the project under overseas market.
“Fuzz-AI Model” for Quantitative Management

The essential of management is: For definite purposes, using limited resource (time, manpower and asset) to make a series of decision under uncertain environment. In the past, management decision usually is made by project manager with his/her subjective judgment and subjective information to decide an action based on strategy instantaneously. However, what strategy is going to take? What action is later to follow up? Such process of decision making is characterized in its randomness, the decision quality is heavily depends upon the experiences and judgment of the decision maker, it is not a stable situation and the quality of decision cannot be guaranteed.
Reasoning About Games with Possibilistic Uncertainty

Game theory is the mathematical study of strategic interaction among rational agents and has found many applications in economics, politics, logic, AI, and computer science. Because of pervasive uncertainty in game-playing environments, how to deal with uncertainty is also a key issue in game theory. While probability calculus has been the standard theory for uncertainty management in games, information of exact probability assessment may be not available in many realistic situations. In such cases, possibility theory is an alternative tool for modeling uncertainty in games. In past decades, the cross-fertilization between logic and game theory has proved to be very successful. Therefore, the paper is aimed at the integration of possibilistic uncertainty into modal logics for reasoning about games including game logic and coalition logic. We will study syntax and semantics of the integrated logics as well as their reasoning problems.
Fast – Asymptotically Optimal – Methods for Determining the Optimal Number of Features

In machine learning – and in data processing in general – it is very important to select the proper number of features. If we select too few, we miss important information and do not get good results, but if we select too many, this will include many irrelevant ones that only bring noise and thus again worsen the results. The usual method of selecting the proper number of features is to add features one by one until the quality stops improving and starts deteriorating again. This method works, but it often takes too much time. In this paper, we propose faster – even asymptotically optimal – methods for solving the problem.
BASED INTERNET NETWORK DESIGN VSAT KU-BAND AT JANGGA DOLOK VILLAGE OFFICE

The number of islands and the size of Indonesia's territory is a problems in building telecommunications infrastructure. VSAT satellite technology is a solution and is useful when terrestrial relations experience issues. This is because not all regions are terrestrial and can reach all islands and mainland in Indonesia. In this study, the Ku-Band VSAT was used, and the Ku-Band VSAT installation location was in Jangga Dolok Village, Lumban Julu District, Toba Samosir Regency, North Sumatra Province. The research method involves needs analysis, design, implementation, and testing. The results showed that the quality of the network obtained showed a bandwidth of 024 Kbps, 637 ms delay, 0% packet loss, 1.84 Mbit/s download throughput, and 319.18 Kbit/s uploads. The measurement analysis results in the field show that the IP Config VSAT IP can be connected to the gateway, and the connection test shows the reply response. The transmission parameter output IDU downlink SQF is 168, and the uplink is 16 Apsk 1024 K 9/10, the upload, and download measurement results show the upload results of 1.8 Mbps and 0.3 Mbps upload. All the analysis results were carried out, and it showed promising results, and the VSAT-based internet network in Jangga Dolok village was feasible to use. Keywords: VSAT Ku-Band, Topology, satellite internet.&#x0D; &#x0D;
Blockchain Forensics in Policing and It’s Global Scenario

No centralization of financial authority and no involvement of banks are some of the accountability threats in bitcoin and online cryptocurrencies revealed by [1] and raised the question that why bitcoin matters to bankers.
Weighted Fuzzy Rules Based on Implicational Quantifiers

In this paper, we explore the use of General Unary Hypotheses Automaton (GUHA) quantifiers, explicitly implicational quantifiers, for analyzing specific relational dependencies. We discuss their suitability in fuzzy modeling and demonstrate their integration with appropriate fuzzy rules to create a new class of weighted fuzzy rules. This study contributes to the advancement of fuzzy modeling and offers a framework for further research and practical applications.
PENERAPAN MODEL PEMBELAJARAN QUANTUM TEACHING UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR SISWA KELAS VIII SMP KARTIKATAMA METRO TAHUN PELAJARAN 2017/2018

Abstract: Efforts to improve the activities and learning outcomes of junior highschool students Kartikatama Metro so far the school and teachers have done a lotof effort, but in fact science learning in junior Kartikatama Metro there are still manydifficulties. Therefore, a research entitled Implementation of Quantum TeachingLearning Model to Increase Student Activity and Learning Outcomes of Class VIIISMP Kartikatama Metro Lesson Year 2017/2018. The purpose of this study is toimprove the activity and learning outcomes of students with the application ofquantum teaching models in grade VIII SMP Kartikatama Metro Lesson year2017/2018. The research type is Classroom Action Research (PTK), with researchdesign using four cycles of action consisting of, planning, implementation,observation, and reflexology. The subject of this research is the students of classVIIIA. The research instrument uses an observation sheet to obtain data of studentlearning activities and test questions to obtain student learning result data. Basedon the result of research on the application of learning quantum teaching model,the achievement level of learning activity of cycle I is 60,50% and cycle II is72,84%, and the percentage of active students in cycle I is 31,67%, then increaseon cycle II by 95%. Furthermore, for the learning outcomes in the cycle I learnedstudent completeness reached 56.67% (high enough), increased in cycle II to86.67% (very high). Based on the result, it can be concluded that the application ofquantum teaching model can increase the activity and learning outcomes ofstudents in SMP Kartikatama metro Lesson Year 2017/2018.Keywords: Quantum teaching learning model, student learning activity, studentlearning outcomes