Reviewerly/exHarmony huggingface dataset:
Benchmark and Survey of Automated Machine Learning Frameworks

Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.
Unsupervised Speech Representation Learning Using WaveNet Autoencoders

We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. Since the learned representation is tuned to contain only phonetic content, we resort to using a high capacity WaveNet decoder to infer information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

We present a brief history of the field of interpretable machine learning (IML), give an overview of state-of-the-art interpretation methods and discuss challenges. Research in IML has boomed in recent years. As young as the field is, it has over 200 years old roots in regression modeling and rule-based machine learning, starting in the 1960s. Recently, many new IML methods have been proposed, many of them model-agnostic, but also interpretation techniques specific to deep learning and tree-based ensembles. IML methods either directly analyze model components, study sensitivity to input perturbations, or analyze local or global surrogate approximations of the ML model. The field approaches a state of readiness and stability, with many methods not only proposed in research, but also implemented in open-source software. But many important challenges remain for IML, such as dealing with dependent features, causal interpretation, and uncertainty estimation, which need to be resolved for its successful application to scientific problems. A further challenge is a missing rigorous definition of interpretability, which is accepted by the community. To address the challenges and advance the field, we urge to recall our roots of interpretable, data-driven modeling in statistics and (rule-based) ML, but also to consider other areas such as sensitivity analysis, causal inference, and the social sciences.
UP-Fall Detection Dataset: A Multimodal Approach

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.
Blockchain-Based Anonymous Authentication With Key Management for Smart Grid Edge Computing Infrastructure

Achieving low latency and providing real-time services are two of several key challenges in conventional cloud-based smart grid systems, and hence, there has been an increasing trend of moving to edge computing. While there have been a number of cryptographic protocols designed to facilitate secure communications in smart grid systems, existing protocols generally do not support conditional anonymity and flexible key management. Thus, in this article, we introduce a blockchain-based mutual authentication and key agreement protocol for edge-computing-based smart grid systems. Specifically, leveraging blockchain, the protocol can support efficient conditional anonymity and key management, without the need for other complex cryptographic primitives. The security analysis shows that the protocol achieves reasonable security assurance, and the comparative summary for security and efficiency also suggests the potential of the proposed protocol in a smart grid deployment.
A generative modeling approach for benchmarking and training shallow quantum circuits

Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to assist the characterization of quantum devices and to train shallow circuits for generative tasks. The procedure leverages quantum hardware capabilities to its fullest extent by using native gates and their qubit connectivity. We demonstrate that our approach can learn an optimal preparation of the Greenberger-Horne-Zeilinger states, also known as "cat states". We further demonstrate that our approach can efficiently prepare approximate representations of coherent thermal states, wave functions that encode Boltzmann probabilities in their amplitudes. Finally, complementing proposals to characterize the power or usefulness of near-term quantum devices, such as IBM's quantum volume, we provide a new hardware-independent metric called the qBAS score. It is based on the performance yield in a specific sampling task on one of the canonical machine learning data sets known as Bars and Stripes. We show how entanglement is a key ingredient in encoding the patterns of this data set; an ideal benchmark for testing hardware starting at four qubits and up. We provide experimental results and evaluation of this metric to probe the trade off between several architectural circuit designs and circuit depths on an ion-trap quantum computer.
Sidechain technologies in blockchain networks: An examination and state-of-the-art review

In the last decade, blockchain has emerged as one of the most influential innovations in software architecture and technology. Ideally, blockchains are designed to be architecturally and politically decentralized, similar to the Internet. In recent times, however, blockchain-based systems have faced stumbling blocks in the form of challenges related to scalability, privacy, security, etc. Several new methods have been proposed both by the research and professional communities to mitigate these challenges. One such recent advancement proposed is the use of sidechains. A sidechain is a secondary blockchain connected to the main blockchain with a two-way peg. Sidechains may have their own consensus protocols, which could be completely different from the mainchain's protocol. Theoretically, a sidechain can add new functionalities, improve privacy, and security of traditionally vanilla blockchains. To this date, however, little is known or discussed regarding factors related to design choices, feasibility, limitations and other issues in adopting the sidechain technology. Moreover, there is a lack of studies discussing how and where it can effectively be integrated into blockchains to remedy current issues in a clear context. Hence, this paper provides the first comprehensive review of the state-of-the-art sidechains and platforms, identifying current advancements and analyzing their impact from various viewpoints, highlighting their limitations and discussing possible remedies for the overall improvement of the blockchain domain.
Jet tagging via particle clouds

How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a ``particle cloud.'' Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.
FS-Net: A Flow Sequence Network For Encrypted Traffic Classification

With more attention paid to user privacy and communication security, the volume of encrypted traffic rises sharply, which brings a huge challenge to traditional rule-based traffic classification methods. Combining machine learning algorithms and manual-design features has become the mainstream methods to solve this problem. However, these features depend on professional experience heavily, which needs lots of human effort. And these methods divide the encrypted traffic classification problem into piece-wise sub-problems, which could not guarantee the optimal solution. In this paper, we apply the recurrent neural network to the encrypted traffic classification problem and propose the Flow Sequence Network (FS-Net). The FS-Net is an end-to-end classification model that learns representative features from the raw flows, and then classifies them in a unified framework. Moreover, we adopt a multi-layer encoder-decoder structure which can mine the potential sequential characteristics of flows deeply, and import the reconstruction mechanism which can enhance the effectiveness of features. Our comprehensive experiments on the real-world dataset covering 18 applications indicate that FS-Net achieves an excellent performance (99.14% TPR, 0.05% FPR and 0.9906 FTF) and outperforms the state-of-the-art methods.
ODE-Inspired Network Design for Single Image Super-Resolution

Single image super-resolution, as a high dimensional structured prediction problem, aims to characterize fine-grain information given a low-resolution sample. Recent advances in convolutional neural networks are introduced into super-resolution and push forward progress in this field. Current studies have achieved impressive performance by manually designing deep residual neural networks but overly relies on practical experience. In this paper, we propose to adopt an ordinary differential equation (ODE)-inspired design scheme for single image super-resolution, which have brought us a new understanding of ResNet in classification problems. Not only is it interpretable for super-resolution but it provides a reliable guideline on network designs. By casting the numerical schemes in ODE as blueprints, we derive two types of network structures: LF-block and RK-block, which correspond to the Leapfrog method and Runge-Kutta method in numerical ordinary differential equations. We evaluate our models on benchmark datasets, and the results show that our methods surpass the state-of-the-arts while keeping comparable parameters and operations.
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is used to intensity -modulate the original optical signal with no reduction in processing speed. Our scheme allows for complete nonlinear on-off contrast in transmission at relatively low optical power thresholds and eliminates the requirement of having additional optical sources between each of the layers of the network Moreover, the activation function is reconfigurable via electrical bias, allowing it to be programmed or trained to synthesize a variety of nonlinear responses. Using numerical simulations, we demonstrate that this activation function significantly improves the expressiveness of optical neural networks, allowing them to perform well on two benchmark machine learning tasks: learning a multi-input exclusive-OR (XOR) logic function and classification of images of handwritten numbers from the MNIST dataset. The addition of the nonlinear activation function improves test accuracy on the MNIST task from 85% to 94%.
A Cascaded R-CNN With Multiscale Attention and Imbalanced Samples for Traffic Sign Detection

In recent years, the deep learning is applied to the field of traffic sign detection methods which achieves excellent performance.However, there are two main challenges in traffic sign detection to be solve urgently.For one thing, some traffic signs of small size are more difficult to detect than those of large size so that the small traffic signs are undetected.For another, some false signs are always detected because of interferences caused by the illumination variation, bad weather and some signs similar to the true traffic signs.Therefore, to solve the undetection and false detection, we first propose a cascaded R-CNN to obtain the multiscale features in pyramids.Each layer of the cascaded network except the first layer fuses the output bounding box of the previous one layer for joint training.This method contributes to the traffic sign detection.Then, we propose a multiscale attention method to obtain the weighted multiscale features by dot-product and softmax, which is summed to fine the features to highlight the traffic sign features and improve the accuracy of the traffic sign detection.Finally, we increase the number of difficult negative samples for dataset balance and data augmentation in the training to relieve the interference by complex environment and similar false traffic signs.The data augment method expands the German traffic sign training dataset by simulation of complex environment changes.We conduct numerous experiments to verify the effectiveness of our proposed algorithm.The accuracy and recall rate of our method are 98.7% and 90.5% in GTSDB, 99.7% and 83.62% in CCTSDB and 98.9% and 85.6% in Lisa dataset respectively.
Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems

Three simple metaphor-less optimization algorithms are developed in this paper for solving the unconstrained and constrained optimization problems.These algorithms are based on the best and worst solutions obtained during the optimization process and the random interactions between the candidate solutions.These algorithms require only the common control parameters like population size and number of iterations and do not require any algorithm-specific control parameters.The performance of the proposed algorithms is investigated by implementing these on 23 benchmark functions comprising 7 unimodal, 6 multimodal and 10 fixed-dimension multimodal functions.Additional computational experiments are conducted on 25 unconstrained and 2 constrained optimization problems.The proposed simple algorithms have shown good performance and are quite competitive.The research community may take advantage of these algorithms by adapting the same for solving different unconstrained and constrained optimization problems.
Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs.We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents.We frame it as an inference problem on a graph.Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within-and crossdocument coreference).Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning.Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset,
Multi-Level Semantic Feature Augmentation for One-Shot Learning

The ability to quickly recognize and learn new visual concepts from limited samples enable humans to quickly adapt to new tasks and environments. This ability is enabled by the semantic association of novel concepts with those that have already been learned and stored in memory. Computers can start to ascertain similar abilities by utilizing a semantic concept space. A concept space is a high-dimensional semantic space in which similar abstract concepts appear close and dissimilar ones far apart. In this paper, we propose a novel approach to one-shot learning that builds on this core idea. Our approach learns to map a novel sample instance to a concept, relates that concept to the existing ones in the concept space and, using these relationships, generates new instances, by interpolating among the concepts, to help learning. Instead of synthesizing new image instance, we propose to directly synthesize instance features by leveraging semantics using a novel auto-encoder network called dual TriNet. The encoder part of the TriNet learns to map multi-layer visual features from CNN to a semantic vector. In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet. Two strategies in the semantic space are explored. Notably, this seemingly simple strategy results in complex augmented feature distributions in the image feature space, leading to substantially better performance.
Channel Attention Is All You Need for Video Frame Interpolation

Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. Our algorithm employs a special feature reshaping operation, referred to as PixelShuffle, with a channel attention, which replaces the optical flow computation module. The main idea behind the design is to distribute the information in a feature map into multiple channels and extract motion information by attending the channels for pixel-level frame synthesis. The model given by this principle turns out to be effective in the presence of challenging motion and occlusion. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical flow computation.
Document Ranking with a Pretrained Sequence-to-Sequence Model

This work proposes the use of a pretrained sequence-to-sequence model for document ranking.Our approach is fundamentally different from a commonly adopted classificationbased formulation based on encoder-only pretrained transformer architectures such as BERT.We show how a sequence-to-sequence model can be trained to generate relevance labels as "target tokens", and how the underlying logits of these target tokens can be interpreted as relevance probabilities for ranking.Experimental results on the MS MARCO passage ranking task show that our ranking approach is superior to strong encoderonly models.On three other document retrieval test collections, we demonstrate a zeroshot transfer-based approach that outperforms previous state-of-the-art models requiring indomain cross-validation.Furthermore, we find that our approach significantly outperforms an encoder-only architecture in a data-poor setting.We investigate this observation in more detail by varying target tokens to probe the model's use of latent knowledge.Surprisingly, we find that the choice of target tokens impacts effectiveness, even for words that are closely related semantically.This finding sheds some light on why our sequence-to-sequence formulation for document ranking is effective.Code and models are available at pygaggle.ai.
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works show that joint learning can result in a noticeable performance gain. However, they usually involve sequential interrelated steps and suffer from the problem of exposure bias. At training time, they predict with the ground truth conditions while at inference it has to make extraction from scratch. This discrepancy leads to error accumulation. To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while being immune from the exposure bias. TPLinker formulates joint extraction as a token pair linking problem and introduces a novel handshaking tagging scheme that aligns the boundary tokens of entity pairs under each relation type. Experiment results show that TPLinker performs significantly better on overlapping and multiple relation extraction, and achieves state-of-the-art performance on two public datasets.