Knowledge Discovery in Databases

Se describe el desarrollo y de una aplicación para el análisis semántico de una colección documental. La herramienta permite interactuar con otros sistemas de gestión documental que aporten una organización del conocimiento específica, a partir de los términos de indexación de sus documentos y definiendo de forma automatizada un espacio vectorial en función de los pesos semánticos de cada término evaluado a partir del factor TF-IDF. Como resultado a la consulta del usuario se construye un gráfico hipertextual, el que representa el nivel de presencia de cada concepto en una o varias colecciones, y facilitando la navegación de las bases de datos vinculadas y el acceso a sus objetos almacenados. La aplicación puede interactuar con cualquier sistema que permita la interoperabilidad, y pueda organizar los datos en correlación con los criterios establecidos en el complemento presentado. En este caso particular, la experiencia se trabajó bajo el sistema para repositorios DSpace.

In recent years, digital marketing has transformed the way in which companies communicate with their customers around the world. The increase in the use of social networks and how users communicate with companies on the Internet has given rise to new business models based on the bidirectionality of communication between companies and Internet users. Digital marketing, new business models, online advertising campaigns, and other digital strategies have gathered user opinions and comments through this new online channel. In this way, companies have started to see the digital ecosystem as not only their present, but also as their future. From this long-term perspective, companies are concerned about sustainability and the growth of their business models. There are new business models on the Internet that support social causes, new platforms aimed at supporting social and sustainable projects, and digital advertising campaigns promoting sustainability. The overarching aim of this Specia...

Executive Summary: The Vision
Imagine every human thought, sentence, image, video, code snippet, or creative work having a unique "DNA fingerprint" that can be traced across languages, domains, and time. Like Bitcoin created decentralized money, the Universal Attribution Framework (UAF) creates decentralized knowledge attribution - a system where ideas have permanent, verifiable lineage regardless of format, language, or domain.
This isn't just about academic papers or scientific theories. This is about everything: social media posts, conversations, art, music, code, patents, memes, videos, podcasts - all human knowledge creation getting proper attribution through a universal system that transcends current limitations.

This paper focuses on the development of a quality assured software, it shows the importance of software reviews in the software development process and also how indispensable security is to software development; any software with no security is vulnerable. Therefore, this paper recommends that software should be developed in a very well defined way; using strict sequence of methodological steps, in a formal and systematic way. It should be noted that quality is the responsibility of a product and security is a part of the totality of quality of a system.

Clinical trials for drug repositioning aim at evaluating the effectiveness and safety of existing drugs as new treatments. This involves managing and semantically correlating many interdependent parameters and details in order to clearly identify the research question of the clinical trial. This work, which is carried out within the PONTE (Efficient Patient Recruitment for Innovative Clinical Trials of Existing Drugs) project, aims to improve the trial design process, by not only offering access to a variety of relevant data sourcesincluding, but not limited to, drug profiles, diseases and their mechanisms, genes and past trial results -but also providing the ability to navigate through these sources, perform queries on them and intelligently fuse the available information through semantic reasoning. This article describes our intention to consume and aggregate information from Linked Data sources in order to produce answers for the clinical researcher's questions.

SQL supports a wide range of both technical and business-oriented roles within
various organizations, reflecting its versatility and importance in the modern
data-driven landscape. For business users, it is crucial to grasp the nuances
of data to effectively extract pertinent and actionable information that can drive
decisions and strategies. On the other hand, for technical users, the creation,
management, and optimization of databases represent essential tasks that
require a deep understanding of SQL’s capabilities. In November 2022, there
was a notable selection of standout SQL interview questions that focused on
various important topics such as JOINs, Subqueries, Aggregates, GROUP BY,
and Multiple-Table queries. These questions were carefully collected from
recent SQL interviews conducted at some of the world’s leading and most
innovative companies, highlighting the growing demand for SQL proficiency in
the job market.

This study estimates the factors affecting socially vulnerable groups’ demand for and accessibility levels to green public spaces in Dhaka City, Bangladesh. Dhaka is a high-density city with one of the lowest levels of green space per capita in the world. Dhaka has just 8.5% of tree-covered lands, while an ideal city requires at least 20% of green space. Urban public green space provides a healthy environment to city dwellers as well as ecological soundness. This study aims to examine the effects of population density and size of a community area (Thana) on the social demand for and accessibility to green parks. To determine the socially vulnerable group demand index, this study used demographic data from the National Population and Housing Census 2011 conducted by the Bangladesh Bureau of Statistics. This study used geographical data extracted from Google Earth Pro to measure accessibility levels, and additionally analyzed geographical data with ArcGIS 10.0 and Google Earth Pro. We...

A distributed system is one in which components located at networked computers communicate and coordinate their actions only by passing messages. Now days Distributed system face different type of problems. One of those problems is load balancing in distributed system. A lot of improvement in hardware is being done. But in this paper we are representing a software technique in which a heuristic is designed to optimize the query processing with the help of cost optimization. Cost of query processing is depending on different parameter. With the help of all parameter we have design a heuristic (A mathematical equation) which helps to have best query sequencing and designing to have optimum load balancing in distributed environment. For the representation of query operation sequencing we use the concept of operator tree.

The paper presents a new interpretation regarding a funerary monument, namely a profiled funerary stela, discovered at Porolissum, Roman Dacia. Helped by the Highlight Reflectance Transformation Imaging (H-RTI) method, improvements on previous editions of this text are advanced. Thus, it is demonstrated that we are dealing with a Latin poetic text dedicated to a deceased person, a young female named Aelia, most likely by her parents. These new edition and interpretation have a certain importance as they bring to light one of the few funerary verse inscriptions discovered so far in Dacia, oπering us, as well, glimpses on the forma mentis and funerary poetic landscapes of the Romans.

Terrorist attacks are the biggest challenging problem for the mankind across the world, which need the wholly attention of the researchers, practitioners to cope up deliberately. To predict the terrorist group which is responsible of attacks and activities using historical data is a complicated task due to the lake of detailed terrorist data. This research based on predicting terrorist groups responsible of attacks in Egypt from year 1970 up to 2013 by using data mining classification technique to compare five base classifiers namely; Naïve Bayes (NB), K-Nearest Neighbour (KNN), Tree Induction (C4.5), Iterative Dichotomiser (ID3), and Support Vector Machine (SVM) depend on real data represented by Global terrorism Database (GTD) from National Consortium for the study of terrorism and Responses of Terrorism (START). The goal of this research is to present two different approaches to handle the missing data as well as provide a detailed comparative study of the used classification algorithms and evaluate the obtained results via two different test options. Experiments are performed on real-life data with the help of WEKA and the final evaluation and conclusion based on four performance measures which showed that SVM, is more accurate than NB and KNN in mode imputation approach, ID3 has the lowest classification accuracy although it performs well in other measures, and in Litwise deletion approach; KNN outperformed the other classifiers in its accuracy, but the overall performance of SVM is acceptable than other classifiers.

This study proposes a new method of identifying possible kidney disease biomarkers using Kidney Diseases Biomarker Ontology (KDBO). This study classically merges clinical imaging, biopsy data, proteomics, and genomic data derived from various sources through the Kidney Development Subontology (KDSO) of Gene Ontology (GO). Machine learning algorithms, network analysis, and statistical approaches are used to identify novel biomarkers that have implications for renal impairment at different stages of the disease have been employed here. A comparison between the new upstart markers found and the available right markers was made in patient cohorts to confirm their accuracy. The study showed that employing kidney ontology improves diagnosis, prognosis, and treatment by facilitating earlier identification of dysfunction, precise evaluation of disease severity levels, tailored choice therapy, addressing issues related to renal ontologies, and presenting suggestions for future studies.

The functional stability of ecosystems depends greatly on interspecific differences in responses to environmental perturbation. However, responses to perturbation are not necessarily invariant among populations of the same species, so intraspecific variation in responses might also contribute. Such inter-population response diversity has recently been shown to occur spatially across species ranges, but we lack estimates of the extent to which individual populations across an entire community might have perturbation responses that vary through time. We assess this using 524 taxa that have been repeatedly surveyed for the effects of tropical forest logging at a focal landscape in Sabah, Malaysia. Just 39 % of taxa -all with non-significant responses to forest degradation -had invariant responses. All other taxa (61 %) showed significantly different responses to the same forest degradation gradient across surveys, with 6 % of taxa responding to forest degradation in opposite directions across multiple surveys. Individual surveys had low power (< 80 %) to determine the correct direction of response to forest degradation for one-fifth of all taxa. Recurrent rounds of logging disturbance increased the prevalence of intra-population response diversity, while uncontrollable environmental variation and/or turnover of intraspecific phenotypes generated variable responses in at least 44 % of taxa. Our results show that the responses of individual species to local environmental perturbations are remarkably flexible, likely providing an unrealised boost to the stability of disturbed habitats such as logged tropical forests.

In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant reduction in required expert involvement for uncertain image region classification.

Certified AI Cybersecurity Analysts are not just professionals — they are the frontline architects of digital trust. Their hybrid expertise allows organizations to combat threats with unmatched intelligence and foresight. Learn more in Certified AI Cybersecurity Analyst (CAICA) Certification by Tonex.

The availability of Arabic text documents on the Internet entails the use of convenient Arabic ‎text ‎classification (TC) techniques. Arabic TC requires extensive work in analyzing the content of valuable ‎Arabic documents. Its rich vocabulary, semantic ambiguity, and words ‎with ‎semantic relations characterize the Arabic language. ‎Therefore, using a bag-of-words (BoWs) text representation model may yield unsatisfactory results. This study is concerned with utilizing synsets and semantic relations from the original words to enhance Arabic TC accuracy. These relations are extracted using the Arabic WordNet (AWN) thesaurus as a lexical and semantic provenance. AWN provides various semantic relations for the original word. Some relations are more beneficial than ‎others with respect to dataset content. Consequently, we suggest either assigning ‎a weight for each relation, at which, the effect of weak relations can be minimized and the strong relations can be boosted, or selection of ...

En este artículo se muestra por medio de consultas específicas el comportamiento del modelo relacional y objetorelacional, y se presenta así un estudio en el que se mide y a la vez se compara la eficiencia (tiempo, uso de recursos del sistema) de operaciones que involucran cláusulas GROUP BY, subconsultas (con las cláusulas IN y EXISTS), y reuniones (joins).

Hukum Gauss merupakan salah satu pilar utama dalam elektromagnetisme dan mekanika klasik yang memberikan wawasan mendalam mengenai bagaimana fluks medan listrik atau gravitasi tersebar dalam ruang. Hukum ini dinyatakan dalam bentuk integral yang menggambarkan hubungan antara jumlah total muatan atau massa dalam suatu volume tertutup dengan medan yang dihasilkan di sekelilingnya. Melalui formulasi matematisnya, hukum ini menunjukkan keterkaitan antara sumber muatan atau massa dengan fluks yang melingkupinya, menegaskan prinsip keterhubungan fundamental dalam fisika.

Hukum Gauss merupakan salah satu pilar utama dalam elektromagnetisme dan mekanika klasik yang memberikan wawasan mendalam mengenai bagaimana fluks medan listrik atau gravitasi tersebar dalam ruang. Hukum ini dinyatakan dalam bentuk integral yang menggambarkan hubungan antara jumlah total muatan atau massa dalam suatu volume tertutup dengan medan yang dihasilkan di sekelilingnya. Melalui formulasi matematisnya, hukum ini menunjukkan keterkaitan antara sumber muatan atau massa dengan fluks yang melingkupinya, menegaskan prinsip keterhubungan fundamental dalam fisika.

Hukum Gauss merupakan salah satu pilar utama dalam elektromagnetisme dan mekanika klasik yang memberikan wawasan mendalam mengenai bagaimana fluks medan listrik atau gravitasi tersebar dalam ruang. Hukum ini dinyatakan dalam bentuk integral yang menggambarkan hubungan antara jumlah total muatan atau massa dalam suatu volume tertutup dengan medan yang dihasilkan di sekelilingnya. Melalui formulasi matematisnya, hukum ini menunjukkan keterkaitan antara sumber muatan atau massa dengan fluks yang melingkupinya, menegaskan prinsip keterhubungan fundamental dalam fisika.

The Meta-Unsolvability Function: Recursive Limits of Mathematical Knowledge and the Paradox of Proving Unprovability
We introduce the Meta-Unsolvability Function (MUF), proving that some mathematical problems cannot even be proven unsolvable, revealing recursive limitations in mathematical knowledge itself. Our Meta-Paradox Theorem demonstrates that there exist problems P* where M(P*) is undefinable, creating mathematical statements that resist all attempts at meta-level analysis. This work establishes fundamental barriers to complete mathematical automation, proving that AI reasoning systems will inevitably encounter problems where they cannot determine whether unsolvability is provable.
Despite theoretical impossibility, we develop practical algorithms for meta-analysis within recursive constraints, achieving 95% success on META-P problems and enabling 40% better performance in AI reasoning systems through meta-awareness protocols. Our framework provides the first rigorous analysis of recursive undecidability with immediate applications in automated theorem proving, AI safety protocols, and mathematical discovery algorithms.
The results extend classical undecidability theory to meta-levels, revealing that the search for mathematical limits is itself fundamentally limited. This has profound implications for artificial intelligence development, automated reasoning systems, and the philosophical foundations of mathematical knowledge, establishing meta-mathematical analysis as essential for understanding the ultimate boundaries of rational knowledge and computational reasoning.

This paper presents an AI-powered personal fitness coaching system utilizing deep learning and real-time computer vision to assist users in exercise recognition and personalized workout planning. Leveraging YOLOv11 convolutional neural networks, the model is designed to classify 36 exercise types. However, due to dataset limitations, the current implementation is evaluated on 30 well-represented exercises. The system provides dynamic feedback on movement correctness, helping prevent injuries and enhance training outcomes. A modular web-based interface allows users to interact, visualize performance graphs, and receive customized plans. The AI-powered fitness assistant demonstrates a significant advancement in computer vision applications for health and wellness, making fitness training more accessible and effective.

Hukum Gauss merupakan salah satu pilar utama dalam elektromagnetisme dan mekanika klasik yang memberikan wawasan mendalam mengenai bagaimana fluks medan listrik atau gravitasi tersebar dalam ruang. Hukum ini dinyatakan dalam bentuk integral yang menggambarkan hubungan antara jumlah total muatan atau massa dalam suatu volume tertutup dengan medan yang dihasilkan di sekelilingnya. Melalui formulasi matematisnya, hukum ini menunjukkan keterkaitan antara sumber muatan atau massa dengan fluks yang melingkupinya, menegaskan prinsip keterhubungan fundamental dalam fisika.

Hukum Gauss merupakan salah satu pilar utama dalam elektromagnetisme dan mekanika klasik yang memberikan wawasan mendalam mengenai bagaimana fluks medan listrik atau gravitasi tersebar dalam ruang. Hukum ini dinyatakan dalam bentuk integral yang menggambarkan hubungan antara jumlah total muatan atau massa dalam suatu volume tertutup dengan medan yang dihasilkan di sekelilingnya. Melalui formulasi matematisnya, hukum ini menunjukkan keterkaitan antara sumber muatan atau massa dengan fluks yang melingkupinya, menegaskan prinsip keterhubungan fundamental dalam fisika.

Disusun Oleh: Kelompok 1
S1 Teknik Elektro
UPN Veteran Jakarta

Raffi Akbar (2310314036)
Sion Lukas Elkana (2310314068)
Dandi Ardian (2310314079)
Wanda Ramadhan (2310314087)
Gracia Aurora (2310314090)


Nikola Tesla adalah tokoh penting dalam perkembangan teknologi listrik, terutama melalui penemuannya terhadap sistem arus bolak-balik (AC) dan motor induksi. Ia juga menggagas konsep transmisi energi tanpa kabel, meskipun belum berhasil diwujudkan sepenuhnya. Penemuannya menjadi dasar sistem kelistrikan modern dan mencerminkan pandangan filosofis tentang energi dan alam semesta. Warisannya tetap berpengaruh besar dalam dunia sains dan teknologi hingga kini.

Hukum Gauss merupakan salah satu prinsip fundamental dalam elektromagnetisme dan gravitasi yang menyatakan bahwa fluks medan listrik atau gravitasi melalui suatu permukaan tertutup sebanding dengan jumlah muatan atau massa di dalamnya. Lebih dari sekadar hukum fisika, Hukum Gauss mencerminkan keteraturan, simetri, dan keterhubungan mendasar dalam alam semesta. Artikel ini mengeksplorasi Hukum Gauss dari perspektif ilmiah dan filsafat, serta menggali konteks historis di balik rumusannya oleh Carl Friedrich Gauss. Melalui lensa epistemologi, hukum ini dipahami tidak hanya sebagai hasil eksperimen, melainkan juga sebagai pengetahuan sintetis a priori sebagaimana dikemukakan oleh Kant, yang menekankan struktur kognitif manusia dalam memahami realitas. Dalam pandangan Platonik, hukum ini merupakan jendela terhadap dunia ide yang lebih tinggi. Pembahasan ini juga mengaitkan hukum Gauss dengan konsep simetri dalam fisika modern, teori informasi, dan entropi, serta relevansinya dalam kosmologi dan teori medan kuantum. Artikel ini menegaskan bahwa penemuan Gauss tidak hanya lahir dari kejeniusannya, tetapi juga dari jejaring pengetahuan dan pengaruh personal dalam kehidupannya. Dengan demikian, Hukum Gauss bukan hanya kerangka ilmiah dalam fisika, tetapi juga refleksi mendalam tentang bagaimana manusia mengonstruksi pengetahuan dan memahami tatanan semesta.

Kata kunci: Hukum Gauss, fluks, simetri, epistemologi, Carl Friedrich Gauss, filsafat sains

People often value the sensual, celebratory, and health aspects of food, but behind this experience exists many other value-laden agricultural production, distribution, manufacturing, and physiological processes that support or undermine a healthy population and a sustainable future. The complexity of such processes is evident in both everyday food preparation of recipes and in industrial food manufacturing, packaging and storage, each of which depends critically on human or machine agents, chemical or organismal ingredient references, and the explicit instructions and implicit procedures held in formulations or recipes. An integrated ontology landscape does not yet exist to cover all the entities at work in this farm to fork journey. It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains and their relationship, material, and more abstract organization and role entities. The challenge is to make this merger be, by analogy, one language, rather than nouns and verbs from a dozen or more dialects which cannot be used directly in statements about some aspect of the farm to fork journey without expensive translation or substantial dialect education in order to understand a particular text or domain of knowledge. This work focuses on the ontology components-object and data properties and annotations-needed to model food processes or more general process modelling within the context of the Open Biological and Biomedical Ontology Foundry and congruent ontologies. Ideally these components can be brought together in a general process ontology that can be specialized not only for the food domain but for carrying out other protocols as well. Many operations involved in food identification, preparation, transportation and storage-shaking, boiling, mixing, freezing, labeling, shipping-are actually common to activities from manufacturing and laboratory work to local or home food preparation.

Рецензенти: докт. екон. наук, докт. техн. наук, професор, завідувач кафедри економічної кібернетики Східноукраїнського національного університету ім. В. Даля, заслужений діяч науки і техніки України Рамазанов С. К.; докт. екон. наук, доцент, заступник директора Навчально-наукового інституту "Економічна кібернетика" Донецького національного університету Левицький С. І. Рекомендовано до видання рішенням вченої ради Харківського національного економічного університету імені Семена Кузнеця.

The VoH image database reflects the spatial and temporal breadth envisioned in the project by collecting thousands of visual forms and formats of astral knowledge from Eurasian history of almost 6000 years. The collection includes calendars and other media, instruments, tables, objects of art or architecture with star constellations, planets, and other heavenly inhabitants or phenomena in anthropomorphic, zoomorphic, diagrammatic, or semiotic formats, as well as theoretical models.

A customized metadata scheme including identifiers of the objects, detailed descriptions of the themes they visualize, the places and time periods of the objects’ origin as well as the current holding institutions was designed to facilitate comparative research on processes of knowledge formation, exchange, and transformation. It allows our international team of researchers to collaborate in reviewing and editing the data and resources, and thus ensures a highly curated collection. The virtual bringing together of objects, the focus on their life-cycles as well as a fine-grained visualization and statistical analysis of the data, aims at challenging the histories that we tell about the places and times of humans making sense of the heavens above.

We enable research on the materiality of the heavens in different cultures, and on the alleged contexts from which these historical artifacts were created—their “Sitz im Leben.”

Questions of interest are:

1. What pictorial types were developed in different areas of Eurasia in antiquity?
2. Which components of these pictorial types continued into new cultural and/or geographical contexts?
3. What were the itineraries and conditions of travel?
4. Who served as mediators, distributors, and translators?
5. What is known about their educational, religious, political, linguistic, or artistic contexts?
6. How did the pictorial types change with movements across space, time, and cultures?
7. How can we interpret these changes, and what do they tell us about cross-cultural exchanges of knowledge?
8. What is the relationship between text and visual and material representation?

Using a web-based survey methodology, the accessibility and completeness of library information on the Web sites of 20 major theological seminaries representing a various Christian denominations were appraised. Ten seminaries were chosen by the researcher based on factors such as the clarity and availability of the library services and digital resources which satisfied the study purpose. Core components including theological library collections, digital access and catalogue, and general website usability were examined as a part of the assessment. The websites were analysed directly to collect the data, paying special attention to the influence of each seminary's denomination and edge, and how their libraries' available range reflected this. The study investigates how the online libraries information can facilitate theological education through increasing availability of resources, encouraging lifelong learning as well as involving both faculties and students. It also points out gaps and challenges with the goal of identifying deficiencies, outdated content and usability problems with an eye towards improving the quality of online library services for theological education. The results finally aim to offer perspectives on how useful digital library resources are in aiding theological education in Kerala.

The problem of finding connected components in undirected graphs has been well studied. It is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications, such as social network analysis, web graph mining and image processing. Recently, it has been a major area of interest within the field of large graph processing. However, much of the research has focused on solving the problem using High Performance Computers (HPC). In large distributed systems, the MapReduce framework dominates the processing of big data, and has been used for finding connected components in big graphs although iterative processing is not directly supported in MapReduce. Current big data processing systems have developed into supporting iterative processing and providing additional features other than MapReduce. Moreover, current connected component algorithms in large distributed processing system only use the traditional approach to choosing the component identifier based on the lexical ordering of the node ID value. This study investigates how to enhance the performance of finding connected components algorithm for large graph in distributed processing system. It uses the approach to considering the graph degree property in choosing the component identifier, reviewing how this can affect the efficiency of the algorithm. In the design of our proposed algorithm features provided by current new processing systems such as moving the computation more toward the data partition in Spark are integrated. This study thus review how this has affected the performance. The degree approach to choosing the component identifier is experimentally tested using different algorithms. The study then applies the proposed approach on the fastest existing algorithm, and experimentally compare the performance of the connected component algorithm using both the original and our modified algorithm. The results show that using the degree approach has played a vital role in the evolution of the graph size during the process, leading to a faster convergence and significant performance improvement when case vertex pruning is applied in the algorithms. Furthermore, they demonstrate that in many cases optimising the design of the algorithm with local pre-processing of the data has resulted in performance enhancement.

The problem of finding connected components in undirected graphs has been well studied. It is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications, such as social network analysis, web graph mining and image processing. Recently, it has been a major area of interest within the field of large graph processing. However, much of the research has focused on solving the problem using High Performance Computers (HPC). In large distributed systems, the MapReduce framework dominates the processing of big data, and has been used for finding connected components in big graphs although iterative processing is not directly supported in MapReduce. Current big data processing systems have developed into supporting iterative processing and providing additional features other than MapReduce. Moreover, current connected component algorithms in large distributed processing system only use the traditional approach to choosing the component...

This study aims to propose a Technology network architecture and explains a new algorithm that boosts the effectiveness of the present smart parking cloud-based system. Finding open parking places is difficult for users to perform at busy times of the day, but the "SMART PARKING SYSTEM" project offers an algorithm-based technique to do so. The finest solution for the parking problem in big cities is provided by this initiative. The method proposed in the study helps users find a free place automatically by considering the size and overall availability of spaces in each parking IoT. We successfully implemented the proposed strategy as well. Additionally, customers can find vacant parking sites and reserve a spot that uses a smartphone application. Smart Parking might create more jobs by lowering gasoline carbon emissions and pollution in big cities.

One of the "holy grails" of computational linguistics has been to have a machine carry out a conversation, and to have some idea of what it is talking about. Loglan's (Brown, & 1975) ) machine grammar was a first attempt to carry out such a project using a grammar which was unambiguous, yet able to encompass the whole of human discourse. Writing a logical, speakable language, with a SLR-1 (simple left-to-right parsing, with one look-ahead) grammar, and then reducing that to a functional form results in a language which is hard to use for spoken logic, and is hard to translate into. A more useful way to go is to use the symbols of predicate, first-order logic, second-order logic, and higher-order logic, to use the word-classes of Loglan, to build a functional form from those in combination, and then to work backward from such a functional form to a speakable language, as much like English and Loglan, in priority order, as possible. Such a language is feasible, speakable, understandable, and useful . The result was the JCB-English language. The thesis presented herein is that JBC-English can be improved by a number of means, making the language easier to learn and speak, more concise, and faster to process. The research and development projects detailed herein are to produce an improved version of the language, and the language processing system, which can be effectively used for human and machine discourse, and a demonstration system, which converses in this language, in such a way as to be useful in business and academia.

Esta tesis de master esta enfocada en el analisis de trayectorias de procesos discretos , implementando un diagrama que permita representar de forma visual el comportamiento de un trayecto generado por la clasificacion basada en reglas de estados.

In this paper we propose a novel parallel algorithm for frequent itemset mining. The algorithm is based on the filter-stream programming model, in which the frequent itemset mining process is represented as a data flow controlled by a series of producer and consumer components (called filters), and the data flow (communication) between such filters is made via streams. When production rate matches consumption rate, and communication overhead between producer and consumer filters is minimized, a high degree of asynchrony is achieved. Following this strategy, our algorithm employs an asynchronous candidate generation, and minimizes communication between filters by transferring only the necessary aggregated information. Another nice feature of our algorithm is a look forward approach which accelerates frequent itemset determination. Extensive evaluation shows the parallel performance and scalability of our algorithm.

auto-évolutifs, c'est-à-dire capables de s'adapter au style d'écriture et aux habitudes de chacun, sans toutefois nécessiter de période d'apprentissage fastidieuse. Nous utilisons une approche d'apprentissage incrémental de classifieurs basés sur les systèmes d'inférence floue de type Takagi-Sugeno. Cette approche comprend d'une part, une adaptation des paramètres linéaires associés aux conclusions des règles en utilisant la méthode des moindres carrés récursifs, et d'autre part, un apprentissage incrémental des prémisses de ces règles afin de modifier les fonctions d'appartenance suivant l'évolution de la densité des données dans l'espace de classification. ABSTRACT. We present in this paper a new method for the design of customizable self-evolving handwriting recognition systems, which are able to adapt to writing style and needs of each writer, without require time-consuming re-learning process. The presented approach is based on first-order Takagi-Sugeno fuzzy inference system. This approach involves first an incremental clustering and adaptation of the premise part of the system, and secondly, an incremental learning of the linear consequents parameters of the system using a modified version of the Recursive Least Square method.

Abstract: Bayesian graphical models are commonly used to build student models from data. A number of standard algorithms are available to train Bayesian models from student skills assessment data. These models can assess student knowledge and skills from a few ...

Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/ process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.

This report provides insights into the challenges, emerging topics, and opportunities related to human–data interaction and visual analytics in the AI era.
The BigVis 2024 organizing committee conducted a survey among experts in the field. They invite the Program Committee members and the authors of accepted papers to share their views. Thirty-two scientists from diverse research communities, including Databases, Information Visualization, and Human–Computer Interaction, participated in the study. These scientists, representing both industry and academia, provided valuable insights into the current and future landscape of the field.

In this report, we analyze the survey responses and compare them to the findings of a similar study conducted four years ago. The results reveal some interesting insights. First, many of the critical challenges identified in the previous survey remain highly relevant today, despite being unrelated to AI. Meanwhile, the field’s landscape has significantly evolved, with most of today’s vital challenges not even being mentioned in the earlier survey, underscoring the profound impact of AI-related advancements.

Durant la ultima decada hem viscut l&#39;explosio de la fotografia digital. Les possibilitats de captar imatges s&#39;ha multiplicat de manera gairebe infinita sense implicar costos afegits pel fotograf. Aixo ens obliga a replantejar-nos la necessitat d&#39;establir uns criteris ben definits per avaluar, triar i eliminar les fotografies que ingressen als nostres arxius. El treball que es presenta aqui te com a objectiu establir uns protocols per tal de poder aplicar aquests procediments al nous fons que passin a formar part del nostre patrimoni documental. Finalment, s&#39;ha dut a terme l&#39;experiencia d&#39;entregar al productor d&#39;un fons part de la seva documentacio per tal que ell l&#39;avalues i despres comparar els resultats amb l&#39;avaluacio duta a terme des de l&#39;arxiu. Paraules clau: avaluacio, tria, eliminacio, fotografia born-digital, ingres, metadades, arxiu digital, formats de preservacio

In software or program ontologies described in the literature, the separation between abstract and implementation views is evident. On the other hand, the ontological entities involved in such views are not very well defined in the context of programs life-cycle, having as parts conception, construction plan, construction process and verification. In many papers the ontological entities described are not very well grounded and they are only assumed as premises without top ontological background. To provide answers to some raised questions, this paper presents a computer program ontology based on the Basic Formal Ontology (BFO) and an interpretation of such an ontology. The ontology and its interpretation show how a computer program ontology is complex and need to advance to attend some theoretical or practical needs.

A Feature selection algorithm employ for removing irrelevant, redundant information from the data. Amongst feature subset selection algorithm filter methods are used because of its generality and are usually good choice when numbers of features are large. In cluster analysis, graph-theoretic clustering methods to features are used. In particular, the minimum spanning tree (MST)- based clustering algorithms are adopted. A Fast clustering bAsed feature Selection algoriThm (FAST) is based on MST method. In the FAST algorithm, features are divided into clusters by using graph-theoretic clustering methods and then, the most representative feature that is strongly related to target classes is selected. Features in different clusters are relatively independent. A feature subset selection algorithm (FAST) is used to test high dimensional available image, microarray, and text data sets. Traditionally, feature subset selection research has focused on searching for relevant features. The clust...

We have a set S ⊂ {0, 1} n , together with, for each x ∈ S, the result of some unknown function F : {0, 1} n → {0, 1} applied to x, and a method for generating a hypothesis h ∈ H about F given S. We present theoretical and experimental results on four possible methods (similarity, convexification, prevalence and Hamming distance) for determing, given two elements y, z ∈ {0, 1} n \S, whether we should be more confident that h(y) = F(y), or that h(z) = F(z), or indeed that we should attach the same degree of confidence to both statements. We consider whether it is possible to have an absolute measure of confidence in the statement that h(a) = F(a) for any given a ∈ {0, 1} n \S. We introduce a modification of a standard learning algorithm for Boolean functions, which naturally partitions new examples into three categories: 1,0 and don't know.