Reviewerly/exHarmony huggingface dataset:
Shallow aquifer monitoring using handpump vibration data

We present a novel technology for monitoring changes in aquifer depth using handpump vibration data. This builds on our previous works using data to track handpump usage and facilitate handpump maintenance systems in rural parts of Kenya. Our motivation is to develop a cost-effective and scalable infrastructure to monitor shallow aquifers in regions where handpumps are already part of water infrastructure, but where traditional sources of groundwater monitoring data may be limited or non-existent. The data is generated using accelerometer sensors attached to the handles of nine handpumps in the study site in Kenya, instrumented for a year. These time-series data from handpumps are individually modelled using machine learning methods to track the changes in the water level with respect to the bottom of the rising main. Results show promise in modelling handpump vibration data with machine learning approaches to provide useful aquifer monitoring information from the “accidental infrastructure” of community handpumps. This technology is intended to complement existing hydrogeological modelling, and one of our key future goals is to integrate these machine learning outputs with hydrogeological information to develop more refined and robust models for shallow aquifer monitoring.
Soft Gradient Boosting Machine

Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base learner has to take the sequential order, it is infeasible to parallelize the training process among base learners for speed-up. In addition, under online or incremental learning settings, GBMs achieved sub-optimal performance due to the fact that the previously trained base learners can not adapt with the environment once trained. In this work, we propose the soft Gradient Boosting Machine (sGBM) by wiring multiple differentiable base learners together, by injecting both local and global objectives inspired from gradient boosting, all base learners can then be jointly optimized with linear speed-up. When using differentiable soft decision trees as base learner, such device can be regarded as an alternative version of the (hard) gradient boosting decision trees with extra benefits. Experimental results showed that, sGBM enjoys much higher time efficiency with better accuracy, given the same base learner in both on-line and off-line settings.
Proof-Producing Synthesis of CakeML from Monadic HOL Functions

Abstract We introduce an automatic method for producing stateful ML programs together with proofs of correctness from monadic functions in HOL. Our mechanism supports references, exceptions, and I/O operations, and can generate functions manipulating local state, which can then be encapsulated for use in a pure context. We apply this approach to several non-trivial examples, including the instruction encoder and register allocator of the otherwise pure CakeML compiler, which now benefits from better runtime performance. This development has been carried out in the HOL4 theorem prover.
Graph embedded rules for explainable predictions in data streams

Understanding the reason why a prediction has been made by a machine is crucial to grant trust to a human decision-maker. However, data mining based decision support systems are, in general, not designed to promote interpretability; instead, they are developed to improve accuracy. Interpretability becomes a more challenging issue in the context of data stream mining. Where the prediction model has to deal with enormous volumes of data gathered continuously at a fast rate and whose underlying distribution may change over time. On the one hand, the majority of the methods that address classification in a data stream are black-box models or white-box models into ensembles. Either do not provide a clear view of why a particular decision has been made. On the other hand, white-box models, such as rule-based models, do not provide acceptable accuracy to be considered in many applications. This paper proposes modeling the data as a special graph, which is built over the attribute space, and from which interpretable rules can be extracted. To overcome concept drift and enhance model accuracy, different variants of such graphs are considered within an ensemble that is updated over time. The proposed approach has shown the best overall classification results when compared to six rule-based algorithms in twelve streaming domains.
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning

Recently, deep multiagent reinforcement learning (MARL) has become a highly active research area as many real-world problems can be inherently viewed as multiagent systems. A particularly interesting and widely applicable class of problems is the partially observable cooperative multiagent setting, in which a team of agents learns to coordinate their behaviors conditioning on their private observations and commonly shared global reward signals. One natural solution is to resort to the centralized training and decentralized execution paradigm. During centralized training, one key challenge is the multiagent credit assignment: how to allocate the global rewards for individual agent policies for better coordination towards maximizing system-level's benefits. In this paper, we propose a new method called Q-value Path Decomposition (QPD) to decompose the system's global Q-values into individual agents' Q-values. Unlike previous works which restrict the representation relation of the individual Q-values and the global one, we leverage the integrated gradient attribution technique into deep MARL to directly decompose global Q-values along trajectory paths to assign credits for agents. We evaluate QPD on the challenging StarCraft II micromanagement tasks and show that QPD achieves the state-of-the-art performance in both homogeneous and heterogeneous multiagent scenarios compared with existing cooperative MARL algorithms.
Improving GAN Training with Probability Ratio Clipping and Sample  Reweighting

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation. To solve this issue, we propose a new variational GAN training framework which enjoys superior training stability. Our approach is inspired by a connection of GANs and reinforcement learning under a variational perspective. The connection leads to (1) probability ratio clipping that regularizes generator training to prevent excessively large updates, and (2) a sample re-weighting mechanism that improves discriminator training by downplaying bad-quality fake samples. Moreover, our variational GAN framework can provably overcome the training issue in many GANs that an optimal discriminator cannot provide any informative gradient to training generator. By plugging the training approach in diverse state-of-the-art GAN architectures, we obtain significantly improved performance over a range of tasks, including text generation, text style transfer, and image generation.
Structural Profiling of Web Sites in the Wild

The paper reports results of a large-scale survey of 708 websites, in order to measure various features related to their size and structure: DOM tree size, maximum degree, depth, diversity of element types and CSS classes, among others. The goal of this research is to serve as a reference point for studies that include an empirical evaluation on samples of web pages.
Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation

In this paper, we apply self-attention (SA) mechanism to boost the performance of deep metric learning. However, due to the pairwise similarity measurement, the cost of storing and manipulating the complete attention maps makes it infeasible for large inputs. To solve this problem, we propose a compressed self-attention with low-rank approximation (CSALR) module, which significantly reduces the computation and memory costs without sacrificing the accuracy. In CSALR, the original attention map is decomposed into a landmark attention map and a combination coefficient map with a small number of landmark feature vectors sampled from the input feature map by average pooling. Thanks to the efficiency of CSALR, we can apply CSALR to high-resolution shallow convolutional layers and implement a multi-head form of CSALR, which further boosts the performance. We evaluate the proposed CSALR on person reidentification which is a typical metric learning task. Extensive experiments shows the effectiveness and efficiency of CSALR in deep metric learning and its superiority over the baselines.
Predicting Session Length for Product Search on E-commerce Platform

Estimation of session duration for an e-commerce search engine is important for various downstream applications, including user satisfaction prediction, personalization, and diversification of search results. It has been shown in previous studies that search session length has a strong correlation with user's explore vs specific purchase intent. Based on previous work [14], we hypothesize that early prediction of session length distribution can be used to control the degree of explore vs exploit (loosely related to diversification v/s personalization) for Search Engine Result Pages (SERPs) in the user's session to follow. In this work, we try to early predict the user's session length, which will enable the control on explore v/s exploit of the search results. Towards this end, based on previous work and strong empirical evidence, we hypothesize session lengths are Weibull distributed and propose its parameters being modeled by a Recurrent Neural Network over actions in user's search sessions. Through experimentation, we demonstrate that our method performs better as compared to strong baselines for the same.
The classical double slit experiment–a study of the distribution of interference fringes formed on distant screens of varied shapes

Abstract The double slit experiment was the first demonstrative proof of the wave nature of light. It was expounded by the English physician-physicist Thomas Young in 1801 and it soon helped lay to rest the then raging Newton–Huygens debate on whether light consisted of a fast-moving stream of particles or a train of progressive waves in the ether medium. In the experiment, light is made to pass through two very narrow slits spaced closely apart. A screen placed on the other side captures a pattern of alternating bright and dark bands called fringes which are formed as a result of the phenomenon of interference. In prior work by the same author, it was shown that the conventional analysis of Young’s experiment that is used in many introductory physics textbooks, suffers from a number of limitations in regards to its ability to accurately predict the positions of these fringes on the distant screen. This was owing to the adoption of some needless and paradoxical assumptions to help simplify the geometry of the slit barrier-screen arrangement. In the new analysis however, all such approximations were discarded and a hyperbola theorem was forwarded which was then suitably applied to determine the exact fringe positions on screens of varied shapes (linear, semi-circular, semi-elliptical). This paper further builds on that work by laying down the mathematical framework necessary for counting fringes and then comparing their distributions on differently shaped screens, using MATLAB software package for numerical–graphical simulation. In addition, a pair of equivalent laws of proportionality are predicted that govern the distribution of fringes independent of the shape of the detection screen employed.
Learning Flat Latent Manifolds with VAEs

Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, where similarity/distance is estimated in a more compact latent space. Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry. We propose an extension to the framework of variational auto-encoders allows learning flat latent manifolds, where the Euclidean metric is a proxy for the similarity between data points. This is achieved by defining the latent space as a Riemannian manifold and by regularising the metric tensor to be a scaled identity matrix. Additionally, we replace the compact prior typically used in variational auto-encoders with a recently presented, more expressive hierarchical one---and formulate the learning problem as a constrained optimisation problem. We evaluate our method on a range of data-sets, including a video-tracking benchmark, where the performance of our unsupervised approach nears that of state-of-the-art supervised approaches, while retaining the computational efficiency of straight-line-based approaches.
Implementasi Cloud Computing Technology dan Dampaknya Terhadap Kelangsungan Bisnis Perusahaan Dengan Menggunakan Metode Agile dan Studi Literatur

Cloud Computing technology is a new paradigm in the delivery of computing services, Cloud Computing has many advantages compared to conventional systems usually. The development of business at this time began to compete globally to be able to satisfy customers, so companies need access to information that is fast and accurate. In order to access information quickly, companies need to build Information and Communication Technology infrastructure that requires large capital, on the other hand cloud computing is present as an alternative solution. Services provided by cloud computing include hardware, infrastructure, platforms and applications. Cloud computing is very useful, because it can reduce computing costs, increase reliability and provide considerable opportunities for the industrial world. One company that provides cloud computing services is Google. The features of cloud computing are believed to be far more efficient and satisfying. The problem that arises is how companies implement Cloud Computing using Google Cloud and how to carry out the process of deployment and provisioning of Google Cloud SQL Database. The purpose of this research is to provide a study of the benefits of implementing Google Cloud for companies to improve business competitiveness so tightly. This research uses Agile method and literature study, which begins with planning, implementation, testing, documentation, deployment, maintenance, problem identification and analysis and discussion related to Google Cloud, where the results of this research are that Google Cloud can create a model business is more flexible, and faster. The purpose of writing this journal is to provide an overview of the understanding of cloud computing and its development as well as the advantages and disadvantages of companies that use Cloud Computing technology. Based on these searches it is known that Cloud Computing as a technology that utilizes internet services uses a virtual central server for the purpose of maintaining data and applications. The existence of Cloud Computing itself has caused changes in the workings of information technology systems in a company. Security and data storage systems are important for companies. Cloud Computing technology has provided huge benefits for most companies. To carry out the process, the device needs to be prepared include hardware such as HPE DL 20 Gen 10 E-2136-4 SFF Server Computer, 2x1 TB SATA 7.2K 3.5 "Hard Drive, 256GB RAM, minimum bandwidth of 5 Mbps and software requirements including Windows Server 2012 R2 Enterprise, VMM, Google Cloud Platform Enterprise, SQL Server 2012
Binary classification with classical instances and quantum labels

Abstract In classical statistical learning theory, one of the most well-studied problems is that of binary classification. The information-theoretic sample complexity of this task is tightly characterized by the Vapnik-Chervonenkis (VC) dimension. A quantum analog of this task, with training data given as a quantum state has also been intensely studied and is now known to have the same sample complexity as its classical counterpart. We propose a novel quantum version of the classical binary classification task by considering maps with classical input and quantum output and corresponding classical-quantum training data. We discuss learning strategies for the agnostic and for the realizable case and study their performance to obtain sample complexity upper bounds. Moreover, we provide sample complexity lower bounds which show that our upper bounds are essentially tight for pure output states. In particular, we see that the sample complexity is the same as in the classical binary classification task w.r.t. its dependence on accuracy, confidence and the VC-dimension.
Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities

Incomplete GDL-based algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. Generic Domain Pruning (GDP) technique reduces the computational effort by performing a one-shot pruning to filter out suboptimal entries. Unfortunately, GDP could perform poorly when dealing with dense local utilities and ties which widely exist in many domains. In this paper, we present several novel sorting-based acceleration algorithms by alleviating the effect of densely distributed local utilities. Specifically, instead of one-shot pruning in GDP, we propose to integrate both search and pruning to iteratively reduce the search space. Besides, we cope with the utility ties by organizing the search space of tied utilities into AND/OR trees to enable branch-and-bound. Finally, we propose a discretization mechanism to offer a tradeoff between the reconstruction overhead and the pruning efficiency. We demonstrate the superiorities of our algorithms over the state-of-the-art from both theoretical and experimental perspectives.
Auxiliary Template-Enhanced Generative Compatibility Modeling

In recent years, there has been a growing interest in the fashion analysis (e.g., clothing matching) due to the huge economic value of the fashion industry. The essential problem is to model the compatibility between the complementary fashion items, such as the top and bottom in clothing matching. The majority of existing work on fashion analysis has focused on measuring the item-item compatibility in a latent space with deep learning methods. In this work, we aim to improve the compatibility modeling by sketching a compatible template for a given item as an auxiliary link between fashion items. Specifically, we propose an end-to-end Auxiliary Template-enhanced Generative Compatibility Modeling (AT-GCM) scheme, which introduces an auxiliary complementary template generation network equipped with the pixel-wise consistency and compatible template regularization. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed approach.
Subpixel Dense Refinement Network for Skeletonization

Skeletonization is the process of reducing a shape image to its approximate medial axis representation while preserving the topology and geometry of the image. Skeletonization is an important step for topological and geometric shape analysis. In this paper a novel skeleton extraction architecture - Subpixel Dense Refinement Network is introduced which is trained and evaluated on the Pixel SkelNetOn Challenge dataset. The proposed architecture is a three-stage encoder-decoder network with dense interconnections between the decoder networks of each stage. The architecture replaces general up-sampling layers and transposed convolution layers with subpixel convolutions for minimizing the information loss during up-sampling of the encoded features. The deep network is trained end-to-end with intermediate supervision in each stage. The proposed single architecture achieved an F1-score of 0.7708 on the validation set of the Pixel SkelNetOn Challenge dataset.
Faster PAC Learning and Smaller Coresets via Smoothed Analysis

PAC-learning usually aims to compute a small subset ($\varepsilon$-sample/net) from $n$ items, that provably approximates a given loss function for every query (model, classifier, hypothesis) from a given set of queries, up to an additive error $\varepsilon\in(0,1)$. Coresets generalize this idea to support multiplicative error $1\pm\varepsilon$. Inspired by smoothed analysis, we suggest a natural generalization: approximate the \emph{average} (instead of the worst-case) error over the queries, in the hope of getting smaller subsets. The dependency between errors of different queries implies that we may no longer apply the Chernoff-Hoeffding inequality for a fixed query, and then use the VC-dimension or union bound. This paper provides deterministic and randomized algorithms for computing such coresets and $\varepsilon$-samples of size independent of $n$, for any finite set of queries and loss function. Example applications include new and improved coreset constructions for e.g. streaming vector summarization [ICML'17] and $k$-PCA [NIPS'16]. Experimental results with open source code are provided.
Gender Identification in Social Media Using Transfer Learning

Social networks have modified the way we communicate. It is now possible to talk to a large number of people we have never met. Knowing the traits of a person from what he/she writes has become a new area of computational linguistics called Author Profiling. In this paper, we introduce a method for applying transfer learning to address the gender identification problem, which is a subtask of Author Profiling. Systems that use transfer learning are trained in a large number of tasks and then tested in their ability to learn new tasks. An example is to classify a new image into different possible classes, giving an example of each class. This differs from the traditional approach of standard machine learning techniques, which are trained in a single task and are evaluated in new examples of that task. The aim is to train a gender identification model on Twitter users using only their text samples in Spanish. The difference with other related works consists in the evaluation of different preprocessing techniques so that the transfer learning-based fine-tuning is more efficient.
A Reversible Contrast Enhancement Scheme For Color Images

Recently, reversible contrast enhancement (CE) is achieved so that an original image can be recovered after its contrast has been changed. As most of the reversible CE methods are primarily proposed for gray-level images, directly applying them to color images may cause visual distortions. To cope with this issue, a new reversible CE scheme is proposed based on the HSV (hue, saturation, and value) color model. In particular, hue component is preserved during histogram equalization of value component and a new preprocessing is designed for original RGB (red, green, and blue) images. The experimental results show that exact recovery of all test images can be achieved with the proposed scheme. Moreover, several metrics are adopted to measure CE effects, image quality and color difference, respectively. Compared with separately conducting the CE process on the RGB channels, less color differences and better image quality can be simultaneously obtained with the proposed scheme.