Defeating the Credit Card Scams Through Machine Learning Algorithms

Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art

Experimentation and Scientific Inference Building in the Study of Hominin Behavior through Stone Artifact Archaeology

Since the beginning of prehistoric archaeology, various methods and approaches have been developed to describe and explain stone artifact variability. However, noticeably less attention has been paid to the ontological nature of stone artifacts and the adequateness of the inferential reasoning for drawing archaeological interpretations from these artifacts. This dissertation takes a scientific perspective to rethink critically the ways that current lithic approaches generate knowledge about past hominin behavior from stone artifacts through experimentation (Chapter 2), and further, to explore the use of controlled experiments and uniformitarian principles for deriving inferences. The latter is presented as two case studies about Late Pleistocene Neanderthal behavior in southwestern France (Chapter 3 & 4). Archaeological reasoning is inescapably analogical, and archaeological knowledge is bound to be established on the basis on modern observations. However, simplistic treatments of archaeological analogs often result in inferences of questionable validity. In this dissertation, it is argued that greater attention is required to consider the implication of experimental design, variable control, and analogic reasoning in the construction of archaeological inference from stone artifacts. It is argued that the ability to move beyond the constraint of modern analogs in archaeological knowledge production lies in the use of uniformitarian principles that operate independently from the research questions archaeologists wish to evaluate. By examining the uniformitarian connection between platform attributes and flake morphology, the first case study explores how the production of unretouched flakes can be altered in ways that increase their relative utility, as reflected in the ratio of edge length to mass. Application of this relationship to Middle Paleolithic assemblages shows two modes of flake production pattern, possibly related to different ways Neanderthal groups managed the utility of transported tool-kits. The second case study applies a geometric model to assess the lithic cortex proportion in the Middle Paleolithic study assemblages. An excess or deficit of cortex relative to artifact volume provides an indication of possible artifact transport to or from the assemblage locality. Results show correlation between assemblage cortex proportions and paleoenvironmental conditions, suggesting possible shifts in Neanderthal artifact transport pattern and land use during the late Pleistocene

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Efficient Bayesian methods for clustering.

One of the most important goals of unsupervised learning is to discover meaningful clusters in data. Clustering algorithms strive to discover groups, or clusters, of data points which belong together because they are in some way similar. The research presented in this thesis focuses on using Bayesian statistical techniques to cluster data. We take a model-based Bayesian approach to defining a cluster, and evaluate cluster membership in this paradigm. Due to the fact that large data sets are increasingly common in practice, our aim is for the methods in this thesis to be efficient while still retaining the desirable properties which result from a Bayesian paradigm. We develop a Bayesian Hierarchical Clustering (BHC) algorithm which efficiently addresses many of the drawbacks of traditional hierarchical clustering algorithms. The goal of BHC is to construct a hierarchical representation of the data, incorporating both finer to coarser grained clusters, in such a way that we can also make predictions about new data points, compare different hierarchies in a principled manner, and automatically discover interesting levels of the hierarchy to examine. BHC can also be viewed as a fast way of performing approximate inference in a Dirichlet Process Mixture model (DPM), one of the cornerstones of nonparametric Bayesian Statistics. We create a new framework for retrieving desired information from large data collections, Bayesian Sets, using Bayesian clustering techniques. Unlike current retrieval methods, Bayesian Sets provides a principled framework which leverages the rich and subtle information provided by queries in the form of a set of examples. Whereas most clustering algorithms are completely unsupervised, here the query provides supervised hints or constraints as to the membership of a particular cluster. We call this "clustering on demand", since it involves forming a cluster once some elements of that cluster have been revealed. We use Bayesian Sets to develop a content-based image retrieval system. We also extend Bayesian Sets to a discriminative setting and use this to perform automated analogical reasoning. Lastly, we develop extensions of clustering in order to model data with more complex structure than that for which traditional clustering is intended. Clustering models traditionally assume that each data point belongs to one and only one cluster, and although they have proven to be a very powerful class of models, this basic assumption is somewhat limiting. For example, there may be overlapping regions where data points actually belong to multiple clusters, like movies which can each belong to multiple genres. We extend traditional mixture models to create a statistical model for overlapping clustering, the Infinite Overlapping Mixture Model (IOMM), in a non-parametric Bayesian setting, using the Indian Buffet Process (IBP). We also develop a Bayesian Partial Membership model (BPM), which allows data points to have partial membership in multiple clusters via a continuous relaxation of a finite mixture model

Land division and identity in later prehistoric Dartmoor, south-west Britain: Translocating tenure.

Tenure is an important aspect of relations involving people and material things. Archaeologists often evoke tenure but less often subject this concept to sustained examination. In this thesis I explore the subject of tenure. The root of the word tenure is the French verb 'tem'r' (to hold). It is thus concerned with possession, and is related to the concept of property. Dictionary definitions of tenure outline three main senses in which the word tenure is used: Firstly, tenure refers to the holding or possession of something, especially of property and land Secondly, it also means the duration, term or conditions on possession, and thus encompasses a greater range of relations than can be described by 'property' Thirdly, it is also possible to speak of 'getting tenure'---by which is meant the attainment of a permanent office, linked to achieving a certain personal status within a profession. At first sight this third sense seems very different to the first two. However it points to the history of a concept that is closely bound up with personhood. For example, the word 'property' derives from the Latin 'proprius' and French 'propiete'. The words property and propriety thus overlap indicating the historical connections between property and ideas of moral personhood ('self-possession'). 'Ownership', related to the German 'eigen', also refers to identity through its historical link with 'belonging'---the word was once used to describe blood ties between kin as well as possession of objects (Verdery & Humphrey, 2004a: 5). The concept of tenure is more complicated than it may at first appear, referring to many different sense and forms of possession simultaneously

Preference-based Representation Learning for Collections

In this thesis, I make some contributions to the development of representation learning in the setting of external constraints and noisy supervision. A setting of external constraints refers to the scenario in which the learner is forced to output a latent representation of the given data points while enforcing some particular conditions. These conditions can be geometrical constraints, for example forcing the vector embeddings to be close to each other based on a particular relations, or forcing the embedding vectors to lie in a particular manifold, such as the manifold of vectors whose elements sum to 1, or even more complex constraints. The objects of interest in this thesis are elements of a collection X in an abstract space that is endowed with a similarity function which quantifies how similar two objects are. A collection is defined as a set of items in which the order is ignored but the multiplicity is relevant. Various types of collections are used as inputs or outputs in the machine learning field. The most common are perhaps sequences and sets. Besides studying representation learning approaches in presence of external constraints, in this thesis we tackle the case in which the evaluation of this similarity function is not directly possible. In recent years, the machine learning setting of having only binary answers to some comparisons for tuples of elements has gained interest. Learning good representations from a scenario in which a clear distance information cannot be obtained is of fundamental importance. This problem is opposite to the standard machine learning setting where the similarity function between elements can be directly evaluated. Moreover, we tackle the case in which the learner is given noisy supervision signals, with a certain probability for the label to be incorrect. Another research question that was studied in this thesis is how to assess the quality of the learned representations and how a learner can convey the uncertainty about this representation. After the introductory Chapter 1, the thesis is structured in three main parts. In the first part, I present the results of representation learning based on data points that are sequences. The focus in this part is on sentences and permutations, particular types of sequences. The first contribution of this part consists in enforcing analogical relations between sentences and the second is learning appropriate representations for permutations, which are particular mathematical objects, while using neural networks. The second part of this thesis tackles the question of learning perceptual embeddings from binary and noisy comparisons. In machine learning, this problem is referred as ordinal embedding problem. This part contains two chapters which elaborate two different aspects of the problem: appropriately conveying the uncertainty of the representation and learning the embeddings from aggregated and noisy feedback. Finally the third part of the thesis, contains applications of the findings of the previous part, namely unsupervised alignment of clouds of embedding vectors and entity set extension

Shaping decision-making behavior by perceiving the dynamic patterns of interpersonal coordination in futsal

Doutoramento em Motricidade Humana na especialidade de Ciências do DesportoThe aim of this thesis was to investigate the informational constraints that guide performance of individuals and teams in sport. An initial meta-analysis of the effects of expertise on decision-making revealed stronger effects with more homogeneous results in performance contexts and when individuals were allowed to perform sport actions. Based on this conclusion, all of our empirical experiments were developed using a specific sub-phase of competitive futsal games. Analysis of ball passing performance revealed that the decision to pass a ball to a teammate was regulated by spatial constraints through the coupling of interpersonal distance values between players at the moment of pass initiation. Furthermore, the success of the pass was well predicted by a proposed variable defined as Time to Ball Interception. In order to understand how ball dynamics and goal position constrained interpersonal relations between players we also investigated how patterns of interpersonal coordination between players emerged during different sub-phases of the game. It was observed that the ball and the goal represent key performance constraints which shape the emergent patterns of coordination between players and teams. Different coordination dynamics for defenders and attackers were observed, which was consistent with different team objectives In conclusion, all the studies contributed to a better understanding of how individual players or teams adapted their behaviors to the changing conditions of the performance environment, in order to successfully perform.FCT - Fundação para a Ciência e a Tecnologi

Conceptual metaphor and spatial representations of time : the role of affect

Conceptual metaphor involves understanding abstract concepts (e.g., time) in terms of more concrete bodily experiences (e.g., spatial location and movement). Research has identified two different spatio-temporal metaphorical perspectives on time, as reflected in the contrast between ”Christmas is coming” and “We are approaching Christmas”. It has been found that which perspective is chosen can depend on the perceiver’s situation and experience. Four recent studies (Hauser, Carter, & Meier, 2009; Lee & Ji, 2014; Margolies & Crawford, 2008; Richmond, Clare Wilson, & Zinken, 2012) examined the role of emotion on choice of temporal perspective. The current project sought to address the anomalous results and several key issues arising from those studies. First, a series of critical questions were developed and discussed from interrogating the wider research literature on the two spatio-temporal metaphors and from conducting a research synthesis that examined methodological and statistical issues in that wider literature. This was followed by two experiments. The first experiment tested which of two emotion-induction methods, text or film, would be more effective. The second experiment examined the effect of induced emotion (via text) and event valence on choice of spatio-temporal metaphor. Participants (n = 504) were randomly assigned to one of nine experimental conditions, each participant having either a positive, negative, or neutral emotion induced and responding about an event that was either positive, negative or neutral. Additional measures were taken of trait test anxiety, social anxiety, and more general negative emotional states. Emotion induction was effective and there was a significant difference in some responses for traits and for more general negative emotional states. No other significant differences were found. The combined results of the literature interrogation, research synthesis, and experiments are discussed in light of the changing climate in psychology favouring a broader approach to science that includes conceptual analysis, null results, and replications. It is argued that the project has highlighted a previously unacknowledged relationship between emotion, event valence, and temporal perspective, and has revealed a general misunderstanding regarding the interpretation of responses on the standard measures used. This suggests redirection along more fruitful lines of future research into the effect of emotion on choice of spatio-temporal metaphor

The impact of sources of inspiration on the genesis of creative ideas

Innovation fundamentally begins with a good idea. But where do good ideas come from? Much research suggests that innovative breakthroughs are often inspired by past experience: things and ideas that one has interacted with in the world. However, the same experiences that can inspire innovation can sometimes constrain or harm innovation through focus on previously unsuccessful solutions. In this dissertation, I explore principles for guiding interactions with sources of inspiration (previous/other ideas) to maximize their benefits and minimize their pitfalls, focusing on the role of conceptual distance and diversity of sources. I analyze thousands of ideas for complex innovation challenges (e.g., increasing accessibility in elections, revitalizing struggling urban areas) posted to an online crowd-sourced innovation platform that required contributors to cite sources of ideas, tracing the impact of the distance and diversity of sources in ideas’ conceptual genealogies on their creative success (as judged by an expert panel). In this dissertation, I make three primary contributions to the literature. First, leveraging techniques from natural language processing and machine learning, I develop a validated computational methodology for studying conceptual distance and diversity with complex design concepts, which addresses significant issues of efficiency and scalability faced in prior work. Second, I challenge the widespread but unevenly supported notion that far sources provide the best insights for creative ideation; addressing key methodological issues in prior work (time scale, statistical power, and problem variation), I show that overreliance on far sources can harm ideation success, and that good ideas can often come from very near sources. Finally, I demonstrate the potential value of incorporating a temporal dimension into analyses of the impact of sources of inspiration: I find evidence of differential impacts of source distance and diversity (viz., increased problem variation for the effect of source distance, and a more robust positive effect of source diversity) when considering sources farther back in ideas’ conceptual genealogies