Evaluating matrix representations for error-tolerant computing

Infant cry recognition is a challenging task as it is hard to determine the speech features that can allow researchers to clearly separate between different types of cries. However, baby cry is treated as a different way of communication of speech. The types of baby cry can be differentiated using Mel-Frequency Cepstral Coefficient (MFCC) with appropriate artificial intelligence model. Stacked restricted Boltzmann machine (RBN) is popular in providing few layers of neural networks to convert the high dimensional data to lower dimensional data to fine tune the input data to a better initialized weight for the neural networks. Usually RBN is used with another deep neural network to form the deep belief networks (DBN), and the studies in this direction is heading towards the convolutional-RBN variant. The study on RBN to pre-train Convolutional neural networks (CNN) without convolution function in the RBN meanwhile is scarce due to the Back propagation and principal component analysis can be applied directly to the CNN. In this paper, we describe the hybrid system between RBN and CNN for learning class specific features for baby cry recognition using the feature of Mel-Frequency Cepstral Coefficient. We archived an 78.6% of accuracy on 5 types of baby cries by validating the proposed model on baby cry recognition.

Unsupervised learning is a branch of machine learning that tries to find hidden structures within unlabeled data and derive insights from it. Clustering, data dimensionality-reduction techniques, noise reduction, segmentation, anomaly detection, fraud detection, and other rich methods rely on unsupervised learning to drive analytics. Today, with so much data around us, it is impossible to label all data for supervised learning. This makes unsupervised learning all the more important. Restricted Boltzmann machines and auto-encoders are unsupervised methods that are based on artificial neural networks. They have a wide range of uses in data compression and dimensionality reduction, noise reduction from data, anomaly detection, generative modeling, collaborative filtering, and initialization of deep neural networks, among other things. We will go through these topics in detail and then touch upon a couple of unsupervised pre-processing techniques for images, namely PCA (principal component analysis) whitening and ZCA (Mahalanobis) whitening. Also, since restricted Boltzmann machines use sampling techniques during training, I have briefly touched upon Bayesian inference and Markov Chain Monte Carlo sampling for reader's benefit.

Sparse matrix-vector multiplication (SpMV) is a challenging computational kernel in linear algebra applications, like data mining, image processing, and machine learning. The performance of this kernel is greatly dependent on the size of the input matrix and the underlying hardware features. Various sparse matrix storage formats referred to commonly as sparse formats have been proposed in the literature to reduce the size of the matrix. In modern multi-core and many-core architectures, the performance of the kernel is mainly dependent on memory wall and power wall problem. Normally review on sparse formats is done with specific architecture or with specific application. This chapter presents a comparative study on various sparse formats in cross platform architecture like CPU, graphics processor unit (GPU), and single instruction multiple data stream (SIMD) registers. Space complexity analysis of various formats with its representation is discussed. Finally, the merits and demerits of each format have been summarized into a table.

This paper presents a comprehensive review of movie recommendation systems and their applications in the entertainment industry. The paper highlights the different techniques used in movie recommendation systems such as collaborative filtering, content-based filtering, and hybrid filtering. The strengths and weaknesses of each technique are discussed, along with their respective algorithms. The paper also explores the challenges faced by movie recommendation systems such as data sparsity, cold start problem, and user bias. Furthermore, the paper examines the current state-of-the-art movie recommendation systems, their performance evaluation metrics, and their implementation in real world scenarios. The practical application of these systems by popular online streaming platforms is also discussed. The study concludes that movie recommendation systems are essential for enhancing user experience and engagement in the entertainment industry, and future research in this field is crucial to improving the accuracy and effectiveness of these systems. Key Words: Recommendation System, PyCharm, Jupiter Notebook, Artificial Intelligence & Machine Learning.

Today's linear algebra and machine learning applications (ML) continue to grow in size and complexity, placing rapidly increasing demands on the underlying hardware and software systems. To address these issues, hardware designers have proposed using custom accelerators explicitly designed for these demanding workloads. However, realizing the full potential of custom accelerators requires effective hardware/software (HW/SW) co-design strategies that enable seamless integration and optimization across both domains, which is currently a major challenge in accelerator development. This thesis presents an integrated solution to facilitate HW/SW accelerator design. We also address issues in accelerator deployment, enabling rapid prototyping, integrated benchmarking, and comprehensive performance analysis of custom accelerators. In this thesis, we demonstrate the value of a lightweight system modeling library integrated into the build/execution environment of a production ML runtime engine, leveraging TensorFlow Lite for deployment. We explore custom accelerators performance by analyzing the trade-offs between various design choices, including parameter settings and accelerator configurations, from small to large scale, to identify the most effective combinations for specific workloads. Secondly, we employ the Multi-Level Intermediate Representation (MLIR) compiler framework to automatically partition host code from accelerator code, pre-optimizing the latter for improved high-level synthesis (HLS) designs and high-quality accelerated kernels. Lastly, we present compiler extensions to automate the generation and optimization of communication between the host CPU and AXI-based accelerators. We present novel solutions that enable more efficient and effective design space exploration, optimization, and deployment of custom accelerators. The utility of these approaches is demonstrated through experiments with specific accelerator designs and key linear algebra and ML workloads. Most importantly, these solutions empower high-level language users, such as domain scientists, to participate in the design of new accelerator features.--Author's abstract

The Internet serves as a major source in various sectors to communicate and share data. It is being used by every individual across the world for several purposes. The main objective of the recommender system is to provide users with suitable products and information. The movie recommendation system also helps in providing customized decisions to the user, thus improving the experience of customer or user. Content-based filtering manipulates item attributes to suggest other items that are relevant to user likes, this suggestion is made using past actions of customers or users. Cosine Similarity is an evaluation metric that helps in finding how similar the items are regardless of their dimensions. The proposed study, furnish users with suggestions that rest on the prevalence and category of a movie. Content based recommendation system is built using cosine similarity. The movie recommendation system has its separate algorithms for making suggestions by examining users' past data. Many attributes are considered like rating of each movie, name of the director and genre of the film. The system make recommendation by considering one attribute or mixture of attributes. For the proposed system, two different datasets used namely, the credits dataset and the movie dataset. The analysis of data is done with help of Python.

Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set. In this article, we propose to apply RBM to fMRI time courses instead of volumes for BSS. The proposed method not only interprets fMRI time courses explicitly to take advantages of deep learning models in latent feature learning but also substantially reduces model complexity and increases the scale of training set to improve training efficiency. Our experimental results based on Human Connectome Project (HCP) datasets demonstrated the superiority of the proposed method over ICA and the one that applied RBM to fMRI volumes in identifying task-related components, resulted in more accurate and specific representations of task-related activations. Moreover, our method separated out components representing intermixed effects between task events, which could reflect inherent interactions among functionally connected brain regions. Our study demonstrates the value of RBM in mining complex structures embedded in large-scale fMRI data and its potential as a building block for deeper models in fMRI data analysis.

An effective collaborative movie recommender system with cuckoo search

In today's very busy world, recommendation systems are becoming increasingly crucial. In a 24-hour period, people are always pressed for time because of the many activities they must do. Deshalb, the Movie recommendation systems are vital, as they help cinephiles find the finest movies to watch without having to waste their cognitive resources which are limited. This project aims to propose a movie recommendation system using the concept of knowledge graph and particle filtering. While every existing movie recommendation system depends on machine learning and clustering algorithms. This research work proposes a novel movie recommender system that operates directly on the database and is extremely efficient by utilizing the expressive potential of Knowledge Graphs and provides the user with the best recommendations. Using particle filtering and a knowledge graph database, this movie recommendation system provides the user with the best suitable movies depending on numerous parameters such as the user's favourite Directors, genre, etc. Unlike other systems, this recommendation system does not keep track of the browser history for future recommendations.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Automated detection of abnormal events in video surveillance is an important task in research and practical applications. This is, however, a challenging problem due to the growing collection of data without the knowledge of what to be defined as “abnormal”, and the expensive feature engineering procedure. In this paper we introduce a unified framework for anomaly detection in video based on the restricted Boltzmann machine (\(\text {RBM}\)), a recent powerful method for unsupervised learning and representation learning. Our proposed system works directly on the image pixels rather than hand-crafted features, it learns new representations for data in a completely unsupervised manner without the need for labels, and then reconstructs the data to recognize the locations of abnormal events based on the reconstruction errors. More importantly, our approach can be deployed in both offline and streaming settings, in which trained parameters of the model are fixed in offline setting whilst are updated incrementally with video data arriving in a stream. Experiments on three publicly benchmark video datasets show that our proposed method can detect and localize the abnormalities at pixel level with better accuracy than those of baselines, and achieve competitive performance compared with state-of-the-art approaches. Moreover, as RBM belongs to a wider class of deep generative models, our framework lays the groundwork towards a more powerful deep unsupervised abnormality detection framework.

In this paper, we propose and evaluate a simple mechanism to accelerate iterative machine learning algorithms implemented in Hadoop map-reduce (stock), and Apache Spark. In particular, we describe a technique that enables data parallel tasks in map-reduce and Spark to be dynamically and adaptively scheduled on CPU or GPU, based on availability and load. We examine the extent of performance improvements, and correlate them to various parameters of the algorithms studied. We focus on end-to-end performance impact, including overheads associated with transferring data into and out of the GPU, and conversion between data representations in the JVM and on GPU. We also present three optimizations that, in our analysis, can be generalized across many iterative machine learning applications. We present a case study where we accelerate four iterative machine learning applications – multinomial logistic regression, multiple linear regression, K-Means clustering and principal components analysis using singular value decomposition, implemented in three data analytics frameworks – Hadoop Map-Reduce (HMR), IBM Main-Memory Map-Reduce (M3R) and Spark. We observe that the use of GPGPUs decreases the execution time of these applications on HMR by up to 8X, M3R by up to 18X, and Spark by up to 25X. Through our empirical analysis, we offer several insights that can be helpful in designing middleware and cluster managers to accelerate map-reduce and Spark applications using GPUs.

This chapter discusses the application of multivariate techniques and machine learning (ML) to the analysis of data originating from the sensory analysis of dairy products. The main multivariate techniques used in the analysis of sensory data concerning dairy products are principal component analysis (PCA), correspondence analysis (CA), hierarchical cluster analysis (HCA), generalised Procrustes analysis (GPA) and multiple-factor analysis (MFA), allowing us to determine the drivers of liking, characterise the sensory profile, determine sensory acceptance and observe relationships between emotional profiles and sensory acceptance. These techniques can be used to segment consumers or products based on overall liking, type of processing or intra-cultural differences. Comparisons between different sensory methodologies could be made. ML can evaluate the interaction of physical and chemical characteristics with sensory acceptance and predict consumer responses based on descriptive tests. It is essential to observe the peculiarities and limitations of each multivariate statistical techniques or ML, the characteristics of the data set and the objectives of the study, to choose the appropriate analysis to be applied.

A restricted Boltzmann machine (RBM) processor (RBM-P) supporting on-chip learning and inference is proposed for machine learning applications in this paper. Featuring neural network (NN) model reduction for external memory bandwidth saving, low power neuron binarizer (LPNB) with dynamic clock gating and area-efficient NN-like activation function calculators, user-defined connection map (UDCM) for both computation time and bandwidth saving, and early stopping (ES) mechanism in learning process, the proposed system integrates 32 RBM cores with maximal 4k neurons per layer and 128 candidates per sample for machine learning applications. Implemented in 65nm CMOS technology, the proposed RBM-P chip costs 2.2M gates and 128kB SRAM with 8.8mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area. Operated at 1.2V and 210MHz, this chip respectively achieves 114.3x and 3.9x faster processing time than CPU and GPGPU. And the proposed RBM-P chip consumes 41.3pJ and 26.7pJ per neuron weight (NW) for learning and inference, respectively.

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of each technique are listed and explained briefly. In addition, the actual application’s results are also presented in this paper. To evaluate the performance of the techniques, some of the important datasets that are used in evaluating recommender systems are also discussed along with measurement metrics to determine the effectiveness of technique. Example metrics used are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and so on. This paper synthesizes existing research to evaluate the advantages and limitations of diverse recommendation techniques, which aims to give suggestion on how to improve the design of movie recommendation systems so that the performance of the technique can be improved.