Query: Synthesizing open worlds with constraints using locally annealed reversible jump MCMC

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Monte Carlo sampling methods using Markov chains and their applications <=SEP=> A generalization of the sampling method introduced by Metropolis et al. (1953) is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates. Examples of the methods, including the generation of random orthogonal matrices and potential applications of the methods to numerical problems arising in statistics, are discussed.

Metropolis procedural modeling <=SEP=> Procedural representations provide powerful means for generating complex geometric structures. They are also notoriously difficult to control. In this article, we present an algorithm for controlling grammar-based procedural models. Given a grammar and a high-level specification of the desired production, the algorithm computes a production from the grammar that conforms to the specification. This production is generated by optimizing over the space of possible productions from the grammar. The algorithm supports specifications of many forms, including geometric shapes and analytical objectives. We demonstrate the algorithm on procedural models of trees, cities, buildings, and Mondrian paintings.

Markov logic networks <=SEP=> We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

Delayed Rejection in Reversible <=SEP=> In a Metropolis-Hastings algorithm, rejection of proposed moves is an intrinsic part of ensuring that the chain converges to the intended target distribution. However, persistent rejection, perhaps in particular parts of the state space, may indicate that locally the proposal distribution is badly calibrated to the target. As an alternative to careful oo-line tuning of state-dependent proposals, the basic algorithm can be modiied so that on rejection, a second attempt to move is made. A diierent proposal can be generated from a new distribution, that is allowed to depend on the previously rejected proposal. We generalise this idea of delaying the rejection and adapting the proposal distribution, due to Tierney and Mira (1999), to generate a more exible class of methods, that in particular applies to a variable dimension setting. The approach is illustrated by two pedagogical examples, and a more realistic application, to a change-point analysis for point processes.

A Pattern Language - Towns, Buildings, Construction <=SEP=> When there are many people who don't need to expect something more than the benefits to take, we will suggest you to have willing to reach all benefits. Be sure and surely do to take this a pattern language towns buildings construction that gives the best reasons to read. When you really need to get the reason why, this a pattern language towns buildings construction book will probably make you feel curious.

Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids <=SEP=> This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.

Adwords management for third-parties in SEM: An optimisation model and the potential of Twitter <=SEP=> In Search Engine Marketing (SEM), “third-party” partners play an important intermediate role by bridging the gap between search engines and advertisers in order to optimise advertisers' campaigns in exchange of a service fee. In this paper, we present an economic analysis of the market involving a third-party broker in Google AdWords and the broker's customers. We show that in order to optimise his profit, a third-party broker should minimise the weighted average Cost Per Click (CPC) of the portfolio of keywords attached to customer's ads while still satisfying the negotiated customer's demand. To help the broker build and manage such portfolio of keywords, we develop an optimisation framework inspired from the classical Markowitz portfolio management which integrates the customer's demand constraint and enables the broker to manage the tradeoff between return on investment and risk through a single risk aversion parameter. We then propose a method to augment the keywords portfolio with relevant keywords extracted from trending and popular topics on Twitter. Our evaluation shows that such a keywords-augmented strategy is very promising and enables the broker to achieve, on average, four folds larger return on investment than with a non-augmented strategy, while still maintaining the same level of risk.

The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. <=SEP=> We review the modulatory effects of the catecholamine neurotransmitters noradrenaline and dopamine on prefrontal cortical function. The effects of pharmacologic manipulations of these systems, sometimes in comparison with the indoleamine serotonin (5-HT), on performance on a variety of tasks that tap working memory, attentional-set formation and shifting, reversal learning, and response inhibition are compared in rodents, nonhuman primates, and humans using, in a behavioral context, several techniques ranging from microiontophoresis and single-cell electrophysiological recording to pharmacologic functional magnetic resonance imaging. Dissociable effects of drugs and neurotoxins affecting these monoamine systems suggest new ways of conceptualizing state-dependent fronto-executive functions, with implications for understanding the molecular genetic basis of mental illness and its treatment.

Support vector machines under adversarial label contamination <=SEP=> Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood. Intelligent and adaptive attackers may indeed exploit specific vulnerabilities exposed by machine learning techniques to violate system security. Being robust to adversarial data manipulation is thus an important, additional requirement for machine learning algorithms to successfully operate in adversarial settings. In this work, we evaluate the security of Support Vector Machines (SVMs) to well-crafted, adversarial label noise attacks. In particular, we consider an attacker that aims to maximize the SVM’s classification error by flipping a number of labels in the training data. We formalize a corresponding optimal attack strategy, and solve it by means of heuristic approaches to keep the computational complexity tractable. We report an extensive experimental analysis on the effectiveness of the considered attacks against linear and non-linear SVMs, both on synthetic and real-world datasets. We finally argue that our approach can also provide useful insights for developing more secure SVM learning algorithms, and also novel techniques in a number of related research areas, such as semi-supervised and active learning.

Fingerprint classification <=SEP=> A fingerprint classification algorithm is presented in this paper. Fingerprints are classified into five categories: arch, tented arch, left loop, right loop and whorl. The algorithm extracts singular points (cores and deltas) in a fingerprint image and performs classification based on the number and locations of the detected singular points. The classifier is invariant to rotation, translation and small amounts of scale changes. The classifier is rule-based, where the rules are generated independent of a given data set. The classifier was tested on 4000 images in the NIST-4 database and on 5400 images in the NIST-9 database. For he NIST-4 database, classification accuracies of 85.4% for the five-class problem and 91.1% for the four-class problem (with arch and tented arch placed in the same category) were achieved. Using a reject option, the four-class classification error can be reduced to less than 6% with 10% fingerprint images rejected. Similar classification performance was obtained on the NIST-9 database.

Holographic Embeddings of Knowledge Graphs <=SEP=> Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, H OLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state-ofthe-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.

Domain Adaptation via Pseudo In-Domain Data Selection <=SEP=> We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large generaldomain parallel corpus that are most relevant to the target domain. These sentences may be selected with simple cross-entropy based methods, of which we present three. As these sentences are not themselves identical to the in-domain data, we call them pseudo in-domain subcorpora. These subcorpora – 1% the size of the original – can then used to train small domain-adapted Statistical Machine Translation (SMT) systems which outperform systems trained on the entire corpus. Performance is further improved when we use these domain-adapted models in combination with a true in-domain model. The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining inand general-domain systems during decoding.

Observations of achievement and motivation in using cloud computing driven CAD: Comparison of college students with high school and vocational high school backgrounds <=SEP=> 0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.08.001 ⇑ Corresponding author. Tel.: +886 02 7734 3347; f E-mail address: joum@ntnu.edu.tw (M. Jou). Cloud computing technology has matured as it has been integrated with every kind of digitalization processes. It offers numerous advantages for data and software sharing, and thus making the management of complex IT systems much simpler. For education in engineering, cloud computing even provides students with versatile and ubiquitous access to software commonly used in the field without having to step into an actual computer lab. Our study analyzed learning attitudes and academic performances induced by the utilization of resources driven by cloud computing technologies. Comparisons were made between college students with high school and vocational high school backgrounds. One hundred and thirtytwo students who took the computer-aided designing (CAD) course participated in the study. Technology Acceptance Model (TAM) was used as the fundamental framework. Open-ended sets of questionnaires were designed to measure academic performance and causal attributions; the results indicated no significant differences in the cognitive domain between the two groups of students, though it is not so in both the psychomotor and the affective domains. College students with vocational high school background appeared to possess higher learning motivation in CAD applications. 2012 Elsevier Ltd. All rights reserved.

Gang involvement: psychological and behavioral characteristics of gang members, peripheral youth, and nongang youth. <=SEP=> Research has noted the existence of a loose and dynamic gang structure. However, the psychological processes that underpin gang membership have only begun to be addressed. This study examined gang members, peripheral youth, and nongang youth across measures of criminal activity, the importance they attach to status, their levels of moral disengagement, their perceptions of out-group threat, and their attitudes toward authority. Of the 798 high school students who participated in this study, 59 were identified as gang members, 75 as peripheral youth, and 664 as nongang youth. Gang members and peripheral youth were more delinquent than nongang youth overall; however, gang members committed more minor offenses than nongang youth and peripheral youth committed more violent offenses than nongang youth. Gang members were more anti-authority than nongang youth, and both gang and peripheral youth valued social status more than nongang youth. Gang members were also more likely to blame their victims for their actions and use euphemisms to sanitize their behavior than nongang youth, whereas peripheral youth were more likely than nongang youth to displace responsibility onto their superiors. These findings are discussed as they highlight the importance of examining individual differences in the cognitive processes that relate to gang involvement.

Analysis and detection of low quality information in social networks <=SEP=> With social networks like Facebook, Twitter and Google+ attracting audiences of millions of users, they have been an important communication platform in daily life. This in turn attracts malicious users to the social networks as well, causing an increase in the incidence of low quality information. Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My PhD thesis work focuses on the analysis and detection of low quality information in social networks. We introduce social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. For evolutionary study of low quality information, we present the results on large-scale study on Web spam and email spam over a long period of time. Furthermore, we provide activity-based detection approaches to filter out low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumor. Our framework and detection techniques show promising results in analyzing and detecting low quality information in social networks.

African-American rhinoplasty. <=SEP=> Increased width, loss of definition, and lack of projection characterize the stereotypical African-American nose. Early rhinoplasty surgeons attempted strict adherence to neoclassical aesthetic ideals. However, in reality, the anatomy and aesthetic desires of these patients are much more complex. Building dorsal height, achieving nasal tip definition amidst thick skin, and producing a more aesthetically pleasing alar base are the major challenges. Surgical planning should be sensitive to both individual and cultural differences in aesthetic perception and expectations. Here we describe the techniques used by the senior author (R.W.H.K.).

Online Robust Low-Rank Tensor Learning <=SEP=> The rapid increase of multidimensional data (a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these challenges require a new tensor analysis method that can perform tensor data analysis online, which however is still absent. In this paper, we propose an Online Robust Lowrank Tensor Modeling (ORLTM) approach to address these challenges. ORLTM dynamically explores the high-order correlations across all tensor modes for low-rank structure modeling. To analyze mixture data from multiple subspaces, ORLTM introduces a new dictionary learning component. ORLTM processes data streamingly and thus requires quite low memory cost that is independent of data size. This makes ORLTM quite suitable for processing large-scale tensor data. Empirical studies have validated the effectiveness of the proposed method on both synthetic data and one practical task, i.e., video background subtraction. In addition, we provide theoretical analysis regarding computational complexity and memory cost, demonstrating the efficiency of ORLTM rigorously.

Business Case and Technology Analysis for 5G Low Latency Applications <=SEP=> A large number of new consumer and industrial applications are likely to change the classic operator’s business models and provide a wide range of new markets to enter. This paper analyzes the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them, operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analyzed. Conclusions show that the operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.

Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives <=SEP=> In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.

The Semantic Web - ISWC 2003 <=SEP=> The Semantic Network, a component of the Unified Medical Language System® (UMLS), describes core biomedical knowledge consisting of semantic types and relationships. It is a well established, semi-formal ontology in widespread use for over a decade. We expected to “publish” this ontology on the Semantic Web, using OWL, with relatively little effort. However, we ran into a number of problems concerning alternative interpretations of the SN notation and the inability to express some of the interpretations in OWL. We detail these problems, as a cautionary tale to others planning to publish pre-existing ontologies on the Semantic Web, as a list of issues to consider when describing formally concepts in any ontology, and as a collection of criteria for evaluating alternative representations, which could form part of a methodology of ontology development.

The Basal Ganglia and Chunking of Action Repertoires <=SEP=> The basal ganglia have been shown to contribute to habit and stimulus-response (S-R) learning. These forms of learning have the property of slow acquisition and, in humans, can occur without conscious awareness. This paper proposes that one aspect of basal ganglia-based learning is the recoding of cortically derived information within the striatum. Modular corticostriatal projection patterns, demonstrated experimentally, are viewed as producing recoded templates suitable for the gradual selection of new input-output relations in cortico-basal ganglia loops. Recordings from striatal projection neurons and interneurons show that activity patterns in the striatum are modified gradually during the course of S-R learning. It is proposed that this recoding within the striatum can chunk the representations of motor and cognitive action sequences so that they can be implemented as performance units. This scheme generalizes Miller's notion of information chunking to action control. The formation and the efficient implementation of action chunks are viewed as being based on predictive signals. It is suggested that information chunking provides a mechanism for the acquisition and the expression of action repertoires that, without such information compression would be biologically unwieldy or difficult to implement. The learning and memory functions of the basal ganglia are thus seen as core features of the basal ganglia's influence on motor and cognitive pattern generators.

ADVANCED POLARIMETRIC CONCEPTS – PART 1 ( Polarimetric Target Description , Speckle filtering and Decomposition Theorems ) <=SEP=> There is currently a great deal of interest in the use of polarimetry for radar remote sensing. In this context, different and important objectives are to classify Earth terrain components within a fully polarimetric SAR image and then extract physical information from the observed scattering of microwaves by surface and volume structures. The most important observable measured by such radar systems is the 3x3-coherency matrix [T]. This matrix accounts for local variations in the scattering matrix and is the lowest order operator suitable to extract polarimetric parameters for distributed scatterers in the presence of additive (system) and/or multiplicative (speckle) noise. In the first part of this paper, the most important Target Polarimetry descriptors: Sinclair Matrix, target vectors, coherency matrix and the covariance matrix as well are presented, their interconnections and equivalences will be shown together with the respective transformations.

Chapter 2 INTRUSION DETECTION : A SURVEY <=SEP=> This chapter provides the overview of the state of the art in intrusion detection research. Intrusion detection systems are software and/or hardware components that monitor computer systems and analyze events occurring in them for signs of intrusions. Due to widespread diversity and complexity of computer infrastructures, it is difficult to provide a completely secure computer system. Therefore, there are numerous security systems and intrusion detection systems that address different aspects of computer security. This chapter first provides taxonomy of computer intrusions, along with brief descriptions of major computer attack categories. Second, a common architecture of intrusion detection systems and their basic characteristics are presented. Third, taxonomy of intrusion detection systems based on five criteria (information source, analysis strategy, time aspects, architecture, response) is given. Finally, intrusion detection systems are classified according to each of these categories and the most representative research prototypes are briefly described.

Reforming education sector through Big Data <=SEP=> Big data, which is maturing over time, has a high impact in analyzing scenarios and coming up with decision making strategies pertaining to any sector. Consequently the number of applications are also increasing where in the past, they were limited to the confines of company sales departments. In lieu, this paper is primarily focused on applying Data Sciences within the education sector. We will be discussing the overall application to address higher-education issues. We will also present a framework for capturing, analyzing it and concluding on data in order to benefit decision-makers, thus ultimately manifesting in informing design and finalizing policy.

Agile Modeling with the UML <=SEP=> This paper discusses a model-based approach to software development. It argues that an approach using models as central development artifact needs to be added to the portfolio of software engineering techniques, to further increase efficiency and flexibility of the development as well as quality and reusability of the results. Two major and strongly related techniques are identified and discussed: Test case modeling and an evolutionary approach to model transformation.

Near-Saddle-Node Bifurcation Behavior as Dynamics in Working Memory for Goal-Directed Behavior <=SEP=> In consideration of working memory as a means for goal-directed behavior in nonstationary environments, we argue that the dynamics of working memory should satisfy two opposing demands: long-term maintenance and quick transition. These two characteristics are contradictory within the linear domain. We propose the near-saddle-node bifurcation behavior of a sigmoidal unit with a self-connection as a candidate of the dynamical mechanism that satisfies both of these demands. It is shown in evolutionary programming experiments that the near-saddle-node bifurcation behavior can be found in recurrent networks optimized for a task that requires efficient use of working memory. The result suggests that the near-saddle-node bifurcation behavior may be a functional necessity for survival in nonstationary environments.

Data Base Sublanguage Founded on the Relational Calculus <=SEP=> Three principal types of language for data base manipulation are identified: the low-level, procedure-oriented (typified by the CODASYL-proposed DML), the intermediate level, algebraic (typified by the Project MAC MacAIMS language), and the high level, relational calculus-based data sublanguage, an example of which is described in this paper. The language description is informal and stresses concepts and principles. Following this, arguments are presented for the superiority of the calculus-based type of data base sub-language over the algebraic, and for the algebraic over the low-level procedural. These arguments are particularly relevant to the questions of inter-system compatibility and standardization.

A Systematic Mapping Study on Software Reuse <=SEP=> Context: Software reuse is considered as the key to a successful software development because of its potential to reduce the time to market, increase quality and reduce costs. This increase in demand made the software organizations to envision the use of software reusable assets which can also help in solving recurring problems. Till now, software reuse is confined to reuse of source code in the name of code scavenging. Now a day, software organizations are extending the concepts of software reuse to other life cycle objects as they realized that reuse of source code alone does not save money. The academia has put forward some assets as reusable and presented methods or approaches for reusing them. Also, for a successful software reuse the organizations should assess the value of reuse and keep track on their reuse programs. The other area which is vital for software reuse is the maintenance. Maintenance of reusable software has direct impact on the cost of the software. In this regard, academia has presented a number of techniques, methods, metrics and models for assessing the value of reuse and for maintaining the reusable software. Objectives: In our thesis, we investigate on the reusable assets and the methods/ approaches that are put forward by the academia for reusing those assets. Also a systematic mapping study is performed to investigate what techniques, methods, models and metrics for assessing the value of reuse and for maintaining the reused software are proposed and we also investigate their validation status as well. Methods: The databases like IEEE Xplore, ACM digital library, Inspec, Springer and Google scholar were used to search for the relevant studies for our systematic mapping study. We followed basic inclusion criteria along with detailed inclusion/exclusion criteria for selecting the appropriate article. Results: Through our systematic mapping study, we could summarize the list of 14 reusable assets along with the approaches/methods for reusing them. Taxonomy for assessing the value of reuse and taxonomy for maintaining the reusable software are presented. We also presented the methods/metrics/models/techniques for measuring reuse to assess its value and for maintaining the reusable software along with their validation status and areas in focus. Conclusion: We conclude that, there is a need for defining a standard set of reusable assets that are commonly accepted by the researchers in the field of software reuse. Most metrics/models/methods/approaches presented for assessing the value of reuse and for maintaining the reuse software are academically validated. Efforts have to be put on industrially validating them using the real data.

The art of appeal in electronic commerce: Understanding the impact of product and website quality on online purchases <=SEP=> Purpose – This study advances product appeal and website appeal as focal psychological mechanisms that can be invoked by business-to-consumer (B2C) e-commerce sites to mitigate problems of information asymmetry via signaling to bolster consumers’ purchase intention under the influence of trust. Design/methodology/approach – Survey approach was employed to validate our research model. Findings – Website appeal partially mediates the positive effect of product appeal on consumers’ purchase intention. Trust in e-commerce sites not only increases purchase intention directly, but it also reinforces the positive relationship between website appeal and purchase intention while attenuating the positive relationship between product appeal and purchase intention. Service content quality, search delivery quality, and enjoyment are confirmed as positive antecedents of website appeal whereas diagnosticity and justifiability are established as positive antecedents of product appeal. Research implications – This study not only delineates product and website appeal as complementary drivers of consumer purchase on e-commerce sites, but it also derives five signals that aid in bolstering both product and website appeal. Trust is revealed to exert a moderating influence on the impact of product and website appeal on purchase intention. Practical implications – Practitioners should prioritize their resource allocation to enhance qualities most pertinent to product and website appeal. E-commerce sites should offer product-oriented functionalities to facilitate product diagnosticity and reassure consumers of their purchase decisions. Originality/value – This study distinguishes between product and website appeal as well as between their respective antecedents. It also uncovers how trust can alter the effects of both website and product appeal on consumers’ purchase intention.

From Classical To Hip-Hop : Can Machines Learn Genres ? <=SEP=> The ability to classify the genre of a song is an easy task for the human ear. After listening to a song for just several seconds, it is often not difficult to take note of that song’s characteristics and subsequently identify this genre. However, this task is one that computers have historically not been able to solve well, at least not until the advent of more sophisticated machine learning techniques. Our team explored several of these techniques over the course of this project and successfully built a system that achieves 61.873% accuracy in classifying music genres. This paper discusses the methods we used for exploratory data analysis, feature selection, hyperparameter optimization, and eventual implementation of several algorithms for classification.