Reviewerly/exHarmony huggingface dataset:
Investigation of integer neural networks for low cost embedded systems

Neural networks have been a topic of study for almost half a century and have become one of the predominant methods used for intelligent systems. During this time much progress has been made on improving the accuracy and expanding the capabilities of neural networks. This thesis is an investigation in a different direction, that is to reduce the computational requirements of neural networks to make them more suitable for implementation on very low end microcontrollers and DSPs. The goal is to understand the trade offs in cost, accuracy, and execution time on these low cost processors. To do this, two tests are performed. The first compares execution speed of a simple neural network on low cost hardware. This test demonstrates the advantages to using integer neural networks, and DSP operations. The second test, is a contract of the accuracy of an integer neural network and a floating-point neural network. This test uses a real world example and allows for testing multiple levels of quantization. The test results show the effects of quantization due to the use of integers, and show that there is a strong case for using integer neural networks on low cost microcontrolers, and that significant cost savings can be achieved in exchange for a very small reduction accuracy.
Introduction to the R language

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.
Arpeggios

This chapter offers material for arpeggio study suitable for beginners through advanced players. Arpeggio textures are one of the most commonly used textures in music for the classical guitar, and the number of arpeggio études in the literature reflects this. Material begins with simple right-hand arpeggio patterns that make use of sympathetic movement of the fingers between thumb strokes (that is, the right-hand fingers flex together or in sequence, and no finger moves in the opposite direction during the arpeggio pattern) and continues through increasingly complex patterns involving opposed motion among the fingers (that is, right-hand fingers are used more than once in a pattern between thumb strokes), sometimes in combination with an independent voice played by the thumb. The cultivated study of sympathetic and opposed motion will help guitarists design more fluid and coordinated finger movements to enhance their arpeggio technique.
Linked data

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
The drivers of user responses to social media campaigns: a field study

This research examines the influence of the social media campaign characteristics on the Jordanian student's responses. A quantitative approach was used to develop an integrated model of the factors enhancing participants responses to these campaigns from a purposive sample of Jordanian students (N = 400). In this study, the main characteristics of SM campaigns were found to have a statistically significant effect on customers' responses, in addition to a strong effect for demographic characteristics.
The Usage of Probability Theory Concepts for Analysing Big Data Uncertainty Reasoning

Partial observability, nondeterminism or a combination of the two develop the problem of uncertainty a common occurrence in big data. An agent is needed to handle this uncertainty. This paper aims to see how an agent can tame uncertainty with the degree of belief and to design an agent program that implements the agent function, the mapping from percepts to actions, especially in the field of big data where volumes of data needs to be handled. The agent program more often takes the current percept as input from the sensors and return an action. Uncertainty arises because of ignorance or volumes of data; The agent’s lack to express the truth of the event in the sentence due to uncertainty that prevails, which can be expressed using probability. Probabilities summarises the agent’s belief relative to the evidence and probability distribution is used to specify the probability that exist in assigning to any random variables. Partial observability of the world brings in unobserved aspects, these can be resolved by estimating the values using probability, that help better agent decision in any field including big data. The agent program come into being through learning methods. An agent is designed to form representations of a complex world, the world with huge voluminous data, use a process of inference to derive new representations about the world, and use these new representations to deduce what to do.
Peningkatan Motivasi Hasil Belajar Ekonomi Melalui Model Pembelajaran Student Centered Learning

In the process of learning Economics (Accounting) required a certain strategy and strong motivation. Teachers play a very important role, especially in terms of designing learning scenarios. Teachers should be able to choose a learning model that can increase the motivation of learners, in accordance with the characteristics of subjects, and characteristics of learners so that learning can take place optimally. The aim of this classroom action research is to improve the learning of learners using the student centered learning model. This research was conducted in class XI IPS1, MAN 1 Cilegon semester 1, material Posting from Juku Kebuku Besar Journals, post to a subsidiary "using cycles, in this study the author tries to 2 cycles. The results show that in each cycle of understanding the accounting, cycle of a merchandising company on post material from a special journal to the ledger, posting to the auxiliary book, gained increased motivation and learning outcomes. In general it can be concluded that the use of student centered learning model can improve the motivation and learning outcomes of learners.&#x0D; Key words: Student Centered Learning Model, Learn Economics Accounting
Second Mind Matters Initiative research symposium: registration open

Veterinary RecordVolume 184, Issue 24 p. ii-ii Careers Second Mind Matters Initiative research symposium: registration open First published: 15 June 2019 https://doi.org/10.1136/vr.l4205Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume184, Issue24June 2019Pages ii-ii RelatedInformation
Smart Utilization of Ground Water Resources in Agriculture Field using the Internet of things

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Formal semantics and functional semantics

This joint article represents an attempt to clarify relations between two rather different approaches to the description of linguistic meaning: truthconditional semantics (as represented by Peter Widell) and functional semantics (as represented by Peter Harder).The two approaches are anchored in different traditions, are based on different theoretical premises, and have different objectives.Truth-conditional semantics is based on a philosophical tradition with strong relations to logic, while functional semantics is based on a linguistic tradition with borrowings from evolutionary biology (and also from speech acts philosophy).We argue that an integrated picture that accommodates both approaches can be achieved by a mutual recognition of the different aims as well as of a central area in which the different aims give rise to compatible insights about linguistic meaning.
Tickets now on sale for CSI Portsmouth 2014

CSI Portsmouth part of Portsmouth BookFest, where crime fiction meets crime fact, on 8 November with crime authors, police and forensic experts.
SciPy 1.0: fundamental algorithms for scientific computing in Python

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
A ConvNet for the 2020s

The “Roaring 20s” of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually “modernize” a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the-art methods. To address the topics, we propose a trainable bag-of-freebies oriented solution. We combine the flexible and efficient training tools with the proposed architecture and the compound scaling method. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. Source code is released in https://github.com/WongKinYiu/yolov7.
High-Resolution Image Synthesis with Latent Diffusion Models

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.
Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities.Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks.Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center.However, this results in critical issues related to unacceptable latency and communication inefficiency.To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced.However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers.Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced.In FL, end devices use their local data to train an ML model required by the server.The end devices then send the model updates rather than raw data to the server for aggregation.FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization.However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved.This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale.In this survey, we begin with an introduction to the background and fundamentals of FL.Then, we highlight the aforementioned challenges of FL implementation and review existing solutions.Furthermore, we present the applications of FL for mobile edge network optimization.Finally, we discuss the important challenges and future research directions in FL.
Masked Autoencoders Are Scalable Vision Learners

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3× or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pretraining and shows promising scaling behavior.
Deep Learning for Image Super-Resolution: A Survey

Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.
Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
code2vec: learning distributed representations of code

We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. To this end, code is first decomposed to a collection of paths in its abstract syntax tree. Then, the network learns the atomic representation of each path while simultaneously learning how to aggregate a set of them. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 12M methods. We show that code vectors trained on this dataset can predict method names from files that were unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. A comparison of our approach to previous techniques over the same dataset shows an improvement of more than 75%, making it the first to successfully predict method names based on a large, cross-project corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at http://code2vec.org. The code, data and trained models are available at https://github.com/tech-srl/code2vec.
Unsupervised K-Means Clustering Algorithm

The k-means algorithm is generally the most known and used clustering method.There are various extensions of k-means to be proposed in the literature.Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori.That is, the k-means algorithm is not exactly an unsupervised clustering method.In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters.That is, we propose a novel unsupervised k-means (Uk-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection.The computational complexity of the proposed U-k-means clustering algorithm is also analyzed.Comparisons between the proposed U-k-means and other existing methods are made.Experimental results and comparisons actually demonstrate these good aspects of the proposed U-kmeans clustering algorithm.
Fine-Tuning CNN Image Retrieval with No Human Annotation

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representation, and search efficiency. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where a high quality of annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automated manner. Reconstructed 3D models obtained by the state-of-the-art retrieval and structure-from-motion methods guide the selection of the training data. We show that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval. CNN descriptor whitening discriminatively learned from the same training data outperforms commonly used PCA whitening. We propose a novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and average pooling and show that it boosts retrieval performance. Applying the proposed method to the VGG network achieves state-of-the-art performance on the standard benchmarks: Oxford Buildings, Paris, and Holidays datasets.