The present study examines certain challenges that KDD (Knowledge Discovery in Databases) in general and data mining in particular pose for normative privacy and public policy. In an earlier work (see Tavani, 1999), I argued that certain applications of data-mining technology involving the manipulation of personal data raise special privacy concerns. Whereas the main purpose of the earlier essay was to show what those specific privacy concerns are and to describe how exactly those concerns (...) have been introduced by the use of certain KDD and data-mining techniques, the present study questions whether the use of those techniques necessarily violates the privacy of individuals. This question is considered vis-à-vis a recent theory of privacy advanced by James Moor (1997). The implications of that privacy theory for a data-mining policy are also considered. (shrink)

We examine the relationship between systems and their users from the knowledge discovery perspective. Recently knowledge discovery in databases has made important progress, but it may also bring some potential problems to database design, such as issues related to database security, because an unauthorised user may derive highly sensitive knowledge from unclassified data. In this paper we point out that there is a need for a comprehensive study on knowledge discovery in human-computer symbiosis. Borrowing terms (...) from algorithm design and artificial intelligence literature, we propose a notion called database-user adversarial partnership. We point out that this notion is general enough to cover various knowledge discovery and security of issues related to databases and their users. Furthermore, we point out the notion of database-user adversarial partnership can be further generalised into system-user adversarial partnership. Opportunities provided by knowledge discovery techniques and potential social implications are also discussed. (shrink)

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” (...) evidence such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems. (shrink)

Philosophers committed to the task of coming to grips with reality must face the fact that there are no final solutions and the need to question is fundamental to their project. Taking this as his point of departure Clark proposes that questioning is not confined to the philosopher; it marks every self that is confronted with a given empirical order. Before rendering an analysis of the experience of questioning which is the main thrust of this work, Clark outlines the situation (...) of knowledge in which we find two prevailing viewpoints, the spectator approach, and the performative approach. In the former a subjectivism ensues when the knower passively receives the objects creating a solipsistic prison where the objects take residence precluding any response to the world. In this sense there is no appropriation of meanings as "mine." Clark maintains that this is not what occurs. The self does not merely stand against a world of objects, rather through his intentions he modifies the data conferring meaning on it and in doing so validates his own self. The performative account acknowledges the knower as an active constituting agent who not only intends the facts but uses them in his effort to become a self. A series of historical studies is presented examining these two viewpoints in such philosophers as Plato, Aristotle, Aquinas, Descartes, the empiricists, and finally ending with Kant who represents the synthesis of both perspectives. Using the Critiques as his source, Clark suggests that while the theoretical reason of Kant is a spectator approach, practical reason incorporates the performative account where the "I can" verifies the active self who is responsible for its realization. Kant’s endeavor to construct a transcendental analysis is seen by Clark as primarily an enterprise to set up the necessary conditions for knowledge which can be applied in the practical order. The self only questions for a reason, so that it "can" do something. The "I can" presupposes the "I know," but the "I know" presupposes the "I ask." Clark proposes his own transcendental analysis of questioning and asks what structures are necessary for the experience of questioning in general with total disregard for the content of that question. He attempts to show that the experience involves self discovery, for what is revealed will only be revealed through further self questioning which is always subject to modification. Questioning is a unique and personal response to a world in which I find myself and yet I intend the world, bestowing on it the meaning that is necessary for my fulfillment. There is an analogy between this transcendental analysis and Kant’s, although Clark is quick to point out that he has hesitations about Kant’s treatment of time and space which he has somewhat modified to serve as the necessary structures of the experience of questioning. Philosophy has been pregnant with analyses of the self primarily as a knowing agent and perhaps it is now time for a movement towards the direction that Clark is suggesting. This book may provide the stimulus for more thought on the self as a questioner.—K. R. M. (shrink)

Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect (...) student development and the various relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions. (shrink)

This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. 'Biomedical Big Data' refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that (...) can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward. (shrink)

We examine the performance of some standard causal discovery algorithms, both constraint-based and score-based, from the perspective of how robust they are against failures of the Causal Faithfulness Assumption. For this purpose, we make only the so-called Triangle-Faithfulness assumption, which is a fairly weak consequence of the Faithfulness assumption, and otherwise allows unfaithful distributions. In particular, we allow violations of Adjacency-Faithfulness and Orientation-Faithfulness. We show that the PC algorithm, a representative constraint-based method, can be made more robust against unfaithfulness by (...) incorporating elements of the GES algorithm, a representative score-based method; similarly, the GES algorithm can be made less error-prone by incorporating elements of the conservative PC algorithm. As our simulations demonstrate, the increased robustness seems to matter even when faithfulness is not exactly violated, for with only finite sample, distributions that are not exactly unfaithful may be sufficiently close to being unfaithful to make trouble. (shrink)

One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper (...) presents an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice. (shrink)

Research in Artificial Intelligence (AI) and the Law has maintained an emphasis on knowledge representation and formal reasoning during a period when statistical, data-driven approaches have ascended to dominance within AI as a whole. Electronic discovery is a legal application area, with substantial commercial and research interest, where there are compelling arguments in favor of both empirical and knowledge-based approaches. We discuss the cases for both perspectives, as well as the opportunities for beneficial synergies.

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

In recent years, learning materials have gradually been applied to flipped classrooms. Teachers share learning materials, and students can preview the learning materials before class. During class, the teacher can discuss students' questions from their notes from previewing the learning materials. The social media platform Facebook provides access to learning materials and diversified interactions, such as sharing knowledge, annotating learning materials, and establishing common objectives. Previous studies have explored the effect of flipped classrooms on students' learning engagement, attitudes, and (...) performance. In this paper, we apply educational data mining to explore the relationship between students' viewing behaviors in accessing learning materials and their performance in flipped classrooms. The participants are classified into an experimental group and a control group to engage in flipped classroom activities. The experimental group uses the social media platform Facebook for flipped learning, and the control group uses a learning management system for flipped learning. The results show that there is a significant difference in the learning performance between the two groups, with the average score of the experimental group being higher than that of the control group. Furthermore, we find that the viewing behaviors and performance of the students within the experimental group differ significantly. (shrink)

Web mining refers to the whole of data miningand related techniques that are used toautomatically discover and extract informationfrom web documents and services. When used in abusiness context and applied to some type ofpersonal data, it helps companies to builddetailed customer profiles, and gain marketingintelligence. Web mining does, however, pose athreat to some important ethical values likeprivacy and individuality. Web mining makes itdifficult for an individual to autonomouslycontrol the unveiling and dissemination of dataabout his/her private (...) life. To study thesethreats, we distinguish between `content andstructure mining' and `usage mining.' Webcontent and structure mining is a cause forconcern when data published on the web in acertain context is mined and combined withother data for use in a totally differentcontext. Web usage mining raises privacyconcerns when web users are traced, and theiractions are analysed without their knowledge.Furthermore, both types of web mining are oftenused to create customer files with a strongtendency of judging and treating people on thebasis of group characteristics instead of ontheir own individual characteristics and merits(referred to as de-individualisation). Althoughthere are a variety of solutions toprivacy-problems, none of these solutionsoffers sufficient protection. Only a combinedsolution package consisting of solutions at anindividual as well as a collective level cancontribute to release some of the tensionbetween the advantages and the disadvantages ofweb mining. The values of privacy andindividuality should be respected and protectedto make sure that people are judged and treatedfairly. People should be aware of theseriousness of the dangers and continuouslydiscuss these ethical issues. This should be ajoint responsibility shared by web miners (bothadopters and developers), web users, andgovernments. (shrink)

In recent years, with the development of construction industry, more scientific, systematic, fast, and intelligent calculation methods are needed to coordinate urban development and fierce market competition, and mathematical algorithm library plays an important role in artificial intelligence. Therefore, the author uses computer mathematical algorithm and extension theory to study and analyze the residential building design and intelligent data mining. It is found that the research of the computer-aided expression method of extension building planning is mainly the expression (...) of the input and output system of extension building planning. It includes knowledge representation, system outline design, system flow, and interface expression based on the mathematical database. (shrink)

Early Childhood and Neuroscience is a practical guide to understanding the complex and challenging subject of neuroscience and its use (and misapplication) in early childhood policy and practice. The 2nd edition has been updated throughout and includes three new chapters on: - the effects of childhood trauma - school readiness - neurodiversity It also includes a new Foreword by Laura Jana (Penn State University, USA). The book provides a balanced overview of the debates by weaving discussion on the opportunities of (...) using neuroscience in early childhood practice with examination of the limitations and ethical implications throughout the chapters. This enables students to inform their own opinions about the discipline and its use in their future practice. Clear explanations of the main terms and theories are complemented with illustrative case studies of cutting-edge research from around the world, a glossary of key terms and suggestions for further reading. Reflective discussion questions give students the chance to apply their theoretical knowledge to real-world contexts. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate (...) emergent indirect discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

At present, a series of economic structural changes created by the network economy have brought challenges to the entire economy and society. Traditional social commerce has also suffered severe tests under the background of network economy and global integration, and the rise and development of network commercial activities lack legal constraints. Based on the Big Data technology, in view of the characteristics of data mining services, this paper expands and changes the traditional model and proposes the Big (...) Data alliance data mining service process model. Moreover, this paper uses intelligent decision-making theory and knowledge reasoning methods to construct a fast-responding, service-reusable, and intelligent service model to realize the scalability of data mining services. In addition, this paper constructs system function modules according to the requirements of Internet commercial judicial data mining and verifies the performance of the system constructed in this paper through experimental analysis. The research results show that the system constructed in this paper has certain practical effects. (shrink)

This article responds to two problems confronting social and human sciences: how to relate to digital data, inasmuch as it challenges established social science methods; and how to relate to life sciences, insofar as they produce knowledge that impinges on our own ways of knowing. In a case study of proteomics, we explore how digital devices grapple with large-scale multiples – of molecules, databases, machines and people. We analyse one particular visual device, a cluster-heatmap, produced by scientists by (...) mining data from a large number of experiments on human blood plasma proteins. These proteins make up a myriad multiple whose identity shifts in many ways. Rather than displaying data about proteins, the heatmap constructs a view of the differences and similarities between experiments. We find this attempt to construct a view on many things at once instructive in thinking about multiples more generally. Instead of flattening molecular ‘life itself’, this visual device superimposes layers of digital devices and techniques from a wide variety of disciplines. This layering suggests a different way of relating to the life sciences more generally: rather than what they know, how they know might be of use to social and human sciences when attending to multiplicities. (shrink)

Preventive vaccination is one of the greatest successes of modern medicine. The SARS-CoV-2 epidemic, during which vaccination is the main method of prevention against death and severe disease, gave rise to a resurgence of anti-vaccine movements. The aim of this study was to analyse the attitudes of students towards vaccination and the COVID-19 pandemic. The statistical analysis was performed with the use of the following data-mining methods: correspondence analysis and basket analysis. The obtained results show that students of (...) medicine are characterized with the highest level of knowledge. Students of other medical faculties, on the other hand, have a significantly less uniform views, as do students of non-medical faculties. (shrink)

In recent years, the network has become more complex, and the attacker’s ability to attack is gradually increasing. How to properly understand the network security situation and improve network security has become a very important issue. In order to study the method of extracting information about the security situation of the network based on cloud computing, we recommend the technology of knowledge of the network security situation based on the data extraction technology. It converts each received cyber security (...) event into a standard format that can be defined as multiple brochures, creating a general framework for the cyber security situation. According to the large nature of network security situation data, the Hadoop platform is used to extract aggregation rules, and perform model extraction, pattern analysis, and learning on a network security event dataset to complete network security situation rule mining, and establish a framework for assessing the state of network security. According to the results of the federal rule extraction, the level of network node security risk is obtained in combination with signal reliability, signal severity, resource impact, node protection level, and signal recovery factor. A simulation test is performed to obtain the intrusion index according to the source address of the network security alarm. Through the relevant experiments and analysis of the results, the attack characteristics obtained in this study were obtained after manually reducing the network security event in the 295 h window. The results show that after the security event is canceled, the corresponding window attack index decreases to 0, indicating that this method can effectively implement a network security situation awareness. The proposed technique allows you to accurately sense changes in network security conditions. (shrink)

Inductive Logic Programming employs expressive representation languages, i.e. Prolog Programs, so that ILP can handle structural data which other traditional inductive learner can not or hardly do. In this reason, ILP has been regarded as one of the most important technologies in the area of Data Mining and Knowledge Discovery in Databases recently. However, ILP usually needs enormous computational time to obtain the results from a huge amount of data appearing in such problems as (...) class='Hi'>Data Mining. To cope with this problem and to make ILP more practical, we need more efficient algorithms. Since learning by ILP can be regarded as the search problem, we need to consider the reduction of the number of hypotheses to be evaluated and the efficient evaluation algorithm in order to make ILP more efficient. One of the most effective ways in reducing the hypothesis space to be searched is to compute a Most Specific Hypothesis from one positive example by Inverse Entailment. Since MSH bounds the hypothesis space, it is important to decide which example should be used to generate MSH. In this paper, in order to reduce the computational time, we propose the following three algorithms for ILP systems which employ coverset algorithm, inverse entailment and top-down search strategy: selection of search space, incremental search within one class, integration of hypothesis spaces among the different classes. The main common feature of these three algorithms is that, instead of single MSH, plural MSHs are considered in the search process. Experiments were conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms are useful for reducing the number of candidate hypotheses to be evaluated as well as the total computational time for induction. (shrink)

This paper proposes an agent community based information retrieval method, which uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information related to a user query, an agent uses two histories : a query/retrieved document history(Q/RDH) and a query/sender agent (...) history(Q/SAH). The former is a list of pairs of a query and retrieved documents, where the queries were sent by the agent itself. The latter is a list of pairs of a query and sender agents and shows ``who sent what query to the agent''. This is useful to find a new information source. Making use of the Q/SAH is expected to cause a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads to perform a search. As an agent receives more queries, then more links to new knowledge are achieved. From this behavior, a ``give and take''(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agents Kodama which has been developed in our laboratory, and conducted preliminary experiments to test the hypothesis. The empirical results showed that the method was much more efficient than a naive method employing 'broadcast' techniques only to look up a target agent. (shrink)

The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a (...) technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility. (shrink)

This book defends the constructivist view of science, namely, the view that scientific theories are mental constructions in the mind of the scientist, rather than the realist view that scientific theories are accounts of what nature itself is like. To prove this point, evolution theory is contrasted with "creation science" as two paradigms or extremely divergent theories, each of which, as a mental construct, explains the data or facts of the natural world equally well. Contents: Realism vs. Constructivism; Meaning (...) and Reference in the Natural Sciences; The Role of Paradigm in the Creationism Controversy; Science As Mental Construction. (shrink)

Due to the complexity and versatility of network security alarm data, a cloud-based network security data extraction method is proposed to address the inability to effectively understand the network security situation. The information properties of the situation are generated by creating a set of spatial characteristics classification of network security knowledge, which is then used to analyze and optimize the processing of hybrid network security situation information using cloud computing technology and co-filtering technology. Knowledge and information (...) about the security situation of a hybrid network has been analyzed using cloud computing strategy. The simulation results show that a cyber security crash occurs in window 20, after which the protection index drops to window 500. The increase in the security index of 500 windows is consistent with the effectiveness of the concept of this document method, indicating that this document method can sense changes in the network security situation. Starting from the first attacked window, the defense index began to decrease. In order to simulate the added network defense, the network security events in the 295th time window were reduced in the original data, and the defense index increased significantly in the corresponding time period, which is consistent with the method perception results, which further verifies the effectiveness and reliability of this method on the network security event perception. This method provides high-precision knowledge of network security situations and improves the security and stability of cloud-based networks. (shrink)

Objective To compare assessments of the decision-making competencies of psychiatric inpatients as provided by physicians, nurses, relatives and an assessment tool.Methods This study was carried out at the psychiatry clinic of Kocaeli University Hospital from June 2007 to February 2008. The decision-making competence of the 83 patients who participated in the study was assessed by physicians, nurses, relatives and MacCAT-T.Results Of the 83 patients, the relatives of 73.8% of them, including the parents of 47.7%, were interviewed during the study. A (...) moderately good consistency between the competency assessments of the nurses versus those of the physicians, but a poor consistency between the assessments of the physicians and nurses versus those of the patients’ relatives, was determined. The differences in the competency assessment obtained with the MacCAT-T versus the evaluations of the physicians, nurses and patients’ relatives were statistically significant.Conclusions Our findings demonstrate those physicians, nurses and the patients’ relatives have difficulty in identifying patients lacking decision-making competence. Therefore, an objective competence assessment tool should be used along with the assessments of physicians and nurses, both of whom can provide clinical data, as well as those of relatives, who can offer insights into the patient's moral values and expectations. (shrink)

We present a reference model for finding evidence of discrimination in datasets of historical decision records in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. We formalize the process of direct and indirect discrimination discovery in a rule-based framework, by modelling protected-by-law groups, such as minorities or disadvantaged segments, and contexts where discrimination occurs. Classification rules, extracted from the historical records, allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law (...) groups is evaluated by formalizing existing norms and regulations in terms of quantitative measures. The measures are defined as functions of the contingency table of a classification rule, and their statistical significance is assessed, relying on a large body of statistical inference methods for proportions. Key legal concepts and reasonings are then used to drive the analysis on the set of classification rules, with the aim of discovering patterns of discrimination, either direct or indirect. Analyses of affirmative action, favoritism and argumentation against discrimination allegations are also modelled in the proposed framework. Finally, we present an implementation, called LP2DD, of the overall reference model that integrates induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools. The LP2DD system is put at work on the analysis of a dataset of credit decision records. (shrink)

Data mining technology is an effective knowledge mining and data relationship induction technology based on massive data, which is widely used in data analysis in many fields. In order to improve the utilization effect of students’ performance and meet the teaching needs of modern education, data mining technology can be applied to the existing performance database to mine the data information and treatment. Data mining technology is used to (...) analyse and process the data stored in the student achievement management system, which provides the basis for improving the teaching quality and optimizing the teaching resources. Based on the analysis of the relevant data of large-scale English test results, this paper finds out the relevant rules that affect college English test results, forms the corresponding performance prediction rules, uses data mining technology to more comprehensively analyse the factors that affect students’ performance, establishes a model, and uses data mining tools to mine and analyse students’ English test data. It is of great practical significance to select the model with high accuracy, further optimize the parameters, make good use of the data, and then take targeted measures to guide the teaching reform, help students make more efficient learning plans, and improve and perfect the existing problems in teaching. (shrink)

Logic and Knowledge -/- Editor: Carlo Cellucci, Emily Grosholz and Emiliano Ippoliti Date Of Publication: Aug 2011 Isbn13: 978-1-4438-3008-9 Isbn: 1-4438-3008-9 -/- The problematic relation between logic and knowledge has given rise to some of the most important works in the history of philosophy, from Books VI–VII of Plato’s Republic and Aristotle’s Prior and Posterior Analytics, to Kant’s Critique of Pure Reason and Mill’s A System of Logic, Ratiocinative and Inductive. It provides the title of an important collection (...) of papers by Bertrand Russell (Logic and Knowledge. Essays, 1901–1950). However, it has remained an underdeveloped theme in the last century, because logic has been treated as separate from knowledge. -/- This book does not hope to make up for a century-long absence of discussion. Rather, its ambition is to call attention to the theme and stimulating renewed reflection upon it. The book collects essays of leading figures in the field and it addresses the theme as a topic of current debate, or as a historical case study, or when appropriate as both. Each essay is followed by the comments of a younger discussant, in an attempt to transform what might otherwise appear as a monologue into an ongoing dialogue; each section begins with an historical essay and ends with an essay by one of the editors. -/- Carlo Cellucci is Emeritus Professor of Philosophy at the University of Rome ‘La Sapienza,’ Italy. He is currently completing a book entitled, Remaking Logic: What is Logic Really? -/- Emily Grosholz is Professor of Philosophy at the Pennsylvania State University, USA. She is the author of Representation and Productive Ambiguity in Mathematics and the Sciences (Oxford University Press, 2007). -/- Emiliano Ippoliti is a Research Fellow at the University of Rome ‘La Sapienza,’ Italy. His main interests are heuristics, the logic of discovery, and problem-solving. He is currently working on a book, Ampliating Knowledge: Data, Hypotheses and Novelty. -/- TABLE OF CONTENTS -/- Foreword .................................................................................................... ix Acknowledgements ................................................................................. xxv -/- Section I: Logic and Knowledge -/- Chapter One................................................................................................. 3 The Cognitive Importance of Sight and Hearing in Seventeenthand Eighteenth-Century Logic (Mirella Capozzi) Discussion .............................................................................................. 26 (Chiara Fabbrizi) Chapter Two .............................................................................................. 33 Nominalistic Content (Jody Azzouni) Discussion ............................................................................................... 52 (Silvia De Bianchi) Chapter Three ............................................................................................ 57 A Garden of Grounding Trees (Göran Sundholm) Discussion.......................................................................................... .. 75 (Luca Incurvati) Chapter Four .............................................................................................. 81 Logics and Metalogics (Timothy Williamson) Discussion.......................................................................................... 101 (Cesare Cozzo) Chapter Five ............................................................................................ 109 Is Knowledge the Most General Factive Stative Attitude? (Cesare Cozzo) Discussion.......................................................................................... 117 (Timothy Williamson) Chapter Six .............................................................................................. 123 Classifying and Justifying Inference Rules (Carlo Cellucci) Discussion.......................................................................................... 143 (Norma B. Goethe) -/- Section II: Logic and Science -/- Chapter Seven.......................................................................................... 151 The Universal Generalization Problem and the Epistemic Status of Ancient Medicine: Aristotle and Galen (Riccardo Chiaradonna) Discussion.......................................................................................... 168 (Diana Quarantotto) Chapter Eight........................................................................................... 175 The Empiricist View of Logic (Donald Gillies) Discussion.......................................................................................... 191 (Paolo Pecere) Chapter Nine............................................................................................ 197 Artificial Intelligence and Evolutionary Theory: Herbert Simon’s Unifying Framework (Roberto Cordeschi) Discussion.......................................................................................... 216 (Francesca Ervas) Chapter Ten ............................................................................................. 221 Evolutionary Psychology and Morality: The Renaissance of Emotivism? (Mario De Caro) Discussion.......................................................................................... 232 (Annalisa Paese) Chapter Eleven ........................................................................................ 237 Between Data and Hypotheses (Emiliano Ippoliti) Discussion.......................................................................................... 262 (Fabio Sterpetti) -/- Section III: Logic and Mathematics -/- Chapter Twelve ....................................................................................... 273 Dedekind Against Intuition: Rigor, Scope and the Motives of his Logicism (Michael Detlefsen) Discussion.......................................................................................... 290 (Marianna Antonutti) Chapter Thirteen...................................................................................... 297 Mathematical Intuition: Poincaré, Polya, Dewey (Reuben Hersh) Discussion.......................................................................................... 324 (Claudio Bernardi) Chapter Fourteen ..................................................................................... 329 On the Finite: Kant and the Paradoxes of Knowledge (Carl Posy) Discussion.......................................................................................... 358 (Silvia Di Paolo) Chapter Fifteen ........................................................................................ 363 Assimilation: Not Only Indiscernibles are Identified (Robert Thomas) Discussion.......................................................................................... 380 (Diego De Simone) Chapter Sixteen ....................................................................................... 385 Proofs and Perfect Syllogisms (Dag Prawitz) Discussion.......................................................................................... 403 (Julien Murzi) Chapter Seventeen ................................................................................... 411 Logic, Mathematics, Heterogeneity (Emily Grosholz) Discussion.......................................................................................... 427 (Valeria Giardino) -/- Contributors........................................................................................ ..... 433 Index............................................................................................... ......... 437 -/- Price Uk Gbp: 49.99 Price Us Usd: 74.99. 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Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for social security funds (...) are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to (...) the salivaomics domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches (...) in social epistemology of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation (...) system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered. (shrink)

Purpose The purpose of this paper is to review current practice in sharing and mining medical data revealing benefits, costs and ethical issues. Based on stakeholder perspectives and values, the authors create an ethical code to regulate the sharing and mining of medical information. Design/methodology/approach The framework is based on a review of academic, practitioner and legal research. Findings Owing to the inability of current safeguards to protect consumers from risks related to the disclosure of medical information, (...) the authors develop a framework for ethical sharing and mining of medical data, security, transparency, respect, accountability, community and quality, which espouses security, transparency, respect, accountability, community and quality as the basic tenets of ethical data sharing and mining practice. Research limitations/implications The STRACQ framework is an original, previously unpublished contribution that will require modification over time based on discussion and debate within and among the academy, medical community and public policymakers. Social implications The framework for sharing borrows from the Fair Credit Reporting Act, allowing the collection and dissemination of identified medical data but placing strict limitations on use. Following this framework, benefits of shared and mined medical data are freely available with appropriate safeguards for consumer privacy. Originality/value Mandates for adoption of electronic health-care records require an understanding of medical data mining. This paper presents a review of data mining techniques and reasons for engaging in the practice of identifying benefits, costs and ethical issues. The authors create an original framework, STRACQ, for ethical sharing and mining of medical information, allowing knowledge exploration while protecting consumer privacy. (shrink)

As Active Mining is a new concept among data mining and/or knowledge discovery in databases communities, in order to validate the effectiveness, it is important to carry out empirical studies using practical data. Based on the concept of Active User Reaction, this paper develops a causal model from liver function test data in a medical domain. To develop the model, we have set a problem to predict the values of ICG test from given observation (...) data and experts' background knowledge. We therefore employ a framework of meta-learning and structural equation modeling. In this paper meta-learning means learning about mined results from multiple data-mining techniques. Structural equation modeling enables us to describe flexible models from background knowledge. The construction of the causal model contains two phases: meta-learning and the model building. The meta-learning phase utilizes both the linear regression and the neural network as data mining techniques, then examines the predictability on the given data set. Mining models are n-folded learned from the training data set. Each of the prediction accuracy of the mining models is compared using with the testing data. On the model building phase, we use structural equation modeling to develop a causal model based on results of meta-learning and background knowledge. We again compare the accuracy of the causal model with each of the mining models. Consequently we have developed the causal model, which is comprehensible and have good predictive performance, via the meta-learning phase. Through the empirical study, we have got the conclusion that the framework of meta-learning is effective in data mining in a difficult medical domain. (shrink)

The relationship between students and higher education is seen to have become increasingly transactional. We approach the study of the student–HE relationship in a novel way, by focusing on students’ behaviour post-university, rather than on student narratives. Conceptually, the article builds on multidimensional views of student engagement and the differentiation between psychological transactional contracts – where students who achieve better academic results are more likely to donate – and relational contracts – where students donate more following engagement in social experiences. (...) Making use of longitudinal data on donation behaviour from over 50,000 alumni from an English university over two decades, we find that while students who get better degree outcomes are more likely to donate, the association between participation in social experiences and donations is much stronger. This questions prevalent transactional models of HE and underlines the limitations of purely consumerist views of the relation between students and higher education institutions, even in the marketized UK context. (shrink)

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the (...) decision forest data mining algorithm to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves. (shrink)

Interaction between geology and mining was a decisive element for the development of stratigraphy during the eighteenth century in Germany, Sweden, England, and also Italy. This paper analyses the importance of mining background and experience, and interest in mining, among some eighteenth-century Italian scholars who studied mountains and other terrestrial reliefs paying particular attention to their rocks, strata and formations. Several primary sources are examined, from the early case of Antonio Vallisneri-who, being a physician, used the mines (...) and the quarries as necessary tools of his 'anatomy' of the mountains-up to the geological travels in the Alps and the Apennines promoted in the late eighteenth century with mining perspectives. Rigorous empirical approaches to the study of the Earth's surface-shown by scientists such as Luigi Ferdinando Marsili, Giovanni Targioni Tozzetti and Giovanni Arduino-are here investigated in the light of their mining expertise. This practical knowledge was an essential element of their theoretical work on the 'classification' of mountains. (shrink)

Humans' voice offers the widest variety of motor phenomena of any human activity. However, its clinical evaluation in people with movement disorders such as Parkinson's disease lags behind current knowledge on advanced analytical automatic speech processing methodology. Here, we use deep learning-based speech processing to differentially analyze voice recordings in 14 people with PD before and after dopaminergic medication using personalized Convolutional Recurrent Neural Networks and Phone Attribute Codebooks. p-CRNN yields an accuracy of 82.35% in the binary classification of (...) ON and OFF motor states at a sensitivity/specificity of 0.86/0.78. The PAC-based approach's accuracy was slightly lower with 73.08% at a sensitivity/specificity of 0.69/0.77, but this method offers easier interpretation and understanding of the computational biomarkers. Both p-CRNN and PAC provide a differentiated view and novel insights into the distinctive components of the speech of persons with PD. Both methods detect voice qualities that are amenable to dopaminergic treatment, including active phonetic and prosodic features. Our findings may pave the way for quantitative measurements of speech in persons with PD. (shrink)

Pharmaceutical companies have exploited health information technology to “mine” data from drug prescriptions and use the data to better target their sales pitches to physicians. This article considers the policy arguments and first amendment implications regarding state regulation of data mining. It concludes that the legislative provisions are desirable and should withstand constitutional challenge.

Pharmaceutical companies have long relied on direct marketing of their drugs to physicians through one-on-one meetings with sales representatives. This practice of “detailing” is substantial in its costs and its number of participants. Every year, pharmaceutical companies spend billions of dollars on millions of visits to physicians by tens of thousands of sales representatives.Critics have argued that drug detailing results in sub-optimal prescribing decisions by physicians, compromising patient health and driving up spending on medical care. In this view, physicians often (...) are unduly influenced both by marketing presentations that do not accurately reflect evidence from the medical literature and by the gifts that sales representatives deliver in conjunction with their presentations. (shrink)

The use of information technology in linguistic research gave rise in the 1950s to what is known as Natural Language Processing, but that framework was created without paying due attention to the need for logical reconstruction of linguistic concepts which were borrowed directly from barely formalised structural linguistics. The Computer-aided Acquisition of Semantic Knowledge project based on the Knowledge Discovery in Databases technology enabled us to interact with computers while gathering and improving our knowledge about languages. Thus, (...) with the help of data mining tools, as a result of revisiting two sorts of generally admitted linguistic theories, we succeeded in improving these local linguistic approaches by proposing to unify the Associative Semantics theory with the Meta-Informative Centering theory. The resulting Distributed Grammar program treats, in addition to the above types of information, the third one, para-information which – despite many studies – had no uniform theoretical background in general linguistics. This DG program aims to lay the foundations for creating the theoretical background of Conceptual Linguistics. (shrink)

The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the (...) most active research technique of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm. (shrink)