Reviewerly/exHarmony huggingface dataset:
Balancing Reconstruction Error and Kullback-Leibler Divergence in Variational Autoencoders

Likelihood-based generative frameworks are receiving increasing attention in the deep learning community, mostly on account of their strong probabilistic foundation. Among them, Variational Autoencoders (VAEs) are reputed for their fast and tractable sampling and relatively stable training, but if not properly tuned they may easily produce poor generative performances. The loss function of Variational Autoencoders is the sum of two components, with somehow contrasting effects: the reconstruction loss, improving the quality of the resulting images, and the Kullback-Leibler divergence, acting as a regularizer of the latent space. Correctly balancing these two components is a delicate issue, and one of the major problems of VAEs. Recent techniques address the problem by allowing the network to learn the balancing factor during training, according to a suitable loss function. In this article, we show that learning can be replaced by a simple deterministic computation, expressing the balancing factor in terms of a running average of the reconstruction error over the last minibatches. As a result, we keep a constant balance between the two components along training: as reconstruction improves, we proportionally decrease KL-divergence in order to prevent its prevalence, that would forbid further improvements of the quality of reconstructions. Our technique is simple and effective: it clarifies the learning objective for the balancing factor, and it produces faster and more accurate behaviours. On typical datasets such as Cifar10 and CelebA, our technique sensibly outperforms all previous VAE architectures with comparable parameter capacity.
A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

The problem of class imbalance is extensive for focusing on numerous applications in the real world.In such a situation, nearly all of the examples are labeled as one class called majority class, while far fewer examples are labeled as the other class usually, the more important class is called minority.Over the last few years, several types of research have been carried out on the issue of class imbalance, including data sampling, costsensitive analysis, Genetic Programming based models, bagging, boosting, etc.Nevertheless, in this survey paper, we enlisted the 24 related studies in the years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of single, hybrid and ensemble method design to understand the current status of improving classification output in machine learning techniques to fix problems with class imbalances.This survey paper also includes a statistical analysis of the classification algorithms under various methods and several other experimental conditions, as well as datasets used in different research papers.
Zero-Shot Cross-Lingual Transfer with Meta Learning

Learning what to share between tasks has become a topic of great importance, as strategic sharing of knowledge has been shown to improve downstream task performance.This is particularly important for multilingual applications, as most languages in the world are under-resourced.Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English.We show that this challenging setup can be approached using meta-learning: in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.We experiment using standard supervised, zero-shot cross-lingual, as well as fewshot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering).Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages.We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset).A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.
Reinforcement Learning for Improving Agent Design

In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent's physical structure is rarely optimized for the task at hand. In this work, we explore the possibility of learning a version of the agent's design that is better suited for its task, jointly with the policy. We propose an alteration to the popular OpenAI Gym framework, where we parameterize parts of an environment, and allow an agent to jointly learn to modify these environment parameters along with its policy. We demonstrate that an agent can learn a better structure of its body that is not only better suited for the task, but also facilitates policy learning. Joint learning of policy and structure may even uncover design principles that are useful for assisted-design applications.
Diagnosing Barren Plateaus with Tools from Quantum Optimal Control

Variational Quantum Algorithms (VQAs) have received considerable attention due to their potential for achieving near-term quantum advantage. However, more work is needed to understand their scalability. One known scaling result for VQAs is barren plateaus, where certain circumstances lead to exponentially vanishing gradients. It is common folklore that problem-inspired ansatzes avoid barren plateaus, but in fact, very little is known about their gradient scaling. In this work we employ tools from quantum optimal control to develop a framework that can diagnose the presence or absence of barren plateaus for problem-inspired ansatzes. Such ansatzes include the Quantum Alternating Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others. With our framework, we prove that avoiding barren plateaus for these ansatzes is not always guaranteed. Specifically, we show that the gradient scaling of the VQA depends on the degree of controllability of the system, and hence can be diagnosed through the dynamical Lie algebra<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="fraktur">g</mml:mi></mml:mrow></mml:math>obtained from the generators of the ansatz. We analyze the existence of barren plateaus in QAOA and HVA ansatzes, and we highlight the role of the input state, as different initial states can lead to the presence or absence of barren plateaus. Taken together, our results provide a framework for trainability-aware ansatz design strategies that do not come at the cost of extra quantum resources. Moreover, we prove no-go results for obtaining ground states with variational ansatzes for controllable system such as spin glasses. Our work establishes a link between the existence of barren plateaus and the scaling of the dimension of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="fraktur">g</mml:mi></mml:mrow></mml:math>.
Traffic sign recognition based on deep learning

Abstract Intelligent Transportation System (ITS), including unmanned vehicles, has been gradually matured despite on road. How to eliminate the interference due to various environmental factors, carry out accurate and efficient traffic sign detection and recognition, is a key technical problem. However, traditional visual object recognition mainly relies on visual feature extraction, e.g., color and edge, which has limitations. Convolutional neural network (CNN) was designed for visual object recognition based on deep learning, which has successfully overcome the shortcomings of conventional object recognition. In this paper, we implement an experiment to evaluate the performance of the latest version of YOLOv5 based on our dataset for Traffic Sign Recognition (TSR), which unfolds how the model for visual object recognition in deep learning is suitable for TSR through a comprehensive comparison with SSD (i.e., single shot multibox detector) as the objective of this paper. The experiments in this project utilize our own dataset. Pertaining to the experimental results, YOLOv5 achieves 97.70% in terms of mAP@0.5 for all classes, SSD obtains 90.14% mAP in the same term. Meanwhile, regarding recognition speed, YOLOv5 also outperforms SSD.
Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns

Although opinion spam (or fake review) detection has attracted significant research attention in recent years, the problem is far from solved. One key reason is that there is no large-scale ground truth labeled dataset available for model building. Some review hosting sites such as Yelp.com and Dianping.com have built fake review filtering systems to ensure the quality of their reviews, but their algorithms are trade secrets. Working with Dianping, we present the first large-scale analysis of restaurant reviews filtered by Dianping's fake review filtering system. Along with the analysis, we also propose some novel temporal and spatial features for supervised opinion spam detection. Our results show that these features significantly outperform existing state-of-art features.
Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015

Objective: Financial fraud has been a big concern for many organizations across industries; billions of dollars are lost yearly because of this fraud. So businesses employ data mining techniques to address this continued and growing problem. This paper aims to review research studies conducted to detect financial fraud using data mining tools within one decade and communicate the current trends to academic scholars and industry practitioners. Method: Various combinations of keywords were used to identify the pertinent articles. The majority of the articles retrieved from Science Direct but the search spanned other online databases (e.g., Emerald, Elsevier, World Scientific, IEEE, and Routledge - Taylor and Francis Group). Our search yielded a sample of 65 relevant articles (58 peer-reviewed journal articles with 7 conference papers). One fifth of the articles was found in Expert Systems with Applications (ESA) while about one-tenth found in Decision Support Systems (DSS). Results: 41 data mining techniques were used to detect fraud across different financial applications such as health insurance and credit card. Logistic regression model appeared to be the leading data mining tool in detecting financial fraud with a 13% of usage.In general, supervised learning tool have been used more frequently than the unsupervised ones. Financial statement fraud and bank fraud are the two largest financial applications being investigated in this area – about 63%, which corresponds to 41 articles out of the 65 reviewed articles. Also, the two primary journal outlets for this topic are ESA and DSS. Conclusion: This review provides a fast and easy-to-use source for both researchers and professionals, classifies financial fraud applications into a high level and detailed-level framework, shows the most significant data mining techniques in this domain, and reveals the most countries exposed to financial fraud.
Single Underwater Image Restoration Based on Depth Estimation and Transmission Compensation

For undersea activities, underwater vehicles usually carry optical imaging systems for recording. The captured images and videos frequently suffer from two displeasing problems: First, color distortion; and second, poor visibility. This is mainly because that the light is exponentially attenuated while penetrating through water and the strength of attenuation is color dependent. This paper develops an effective single underwater image restoration framework based on the depth estimation and the transmission compensation. To address the consequences of scattering and absorption, our restoration scheme consists of five major phases: 1) background light estimation; 2) submerged dark channel prior; 3) transmission refinement and radiance recovery; 4) point spread function deconvolution; and 5) transmission and color compensation. A wide variety of underwater images with various scenarios were exploited to assess the restoration performance of the proposed algorithm. Various evaluation metrics were employed to analyze the experimental results. It was suggested that this new restoration algorithm outperformed many state-of-the-art methods both qualitatively and quantitatively. In addition, potential applications regarding autopilot and three-dimensional visualization were demonstrated. We believe that our underwater image restoration technique is promising in many undersea activities that require high-quality images.
Oracles for Gauss's law on digital quantum computers

Formulating a lattice gauge theory using only physical degrees of freedom generically leads to nonlocal interactions. A local Hamiltonian is desirable for quantum simulation, and this is possible by treating the Hilbert space as a subspace of a much larger Hilbert space in which Gauss's law is not automatic. Digital quantum simulations of this local formulation will wander into unphysical sectors due to errors from Trotterization or from quantum noise. In this work, oracles are constructed that use local Gauss law constraints to projectively distinguish physical and unphysical wave functions in Abelian lattice gauge theories. Such oracles can be used to detect errors that break Gauss's law.
Multiphoton quantum-state engineering using conditional measurements

Abstract The quantum theory of electromagnetic radiation predicts characteristic statistical fluctuations for light sources as diverse as sunlight, laser radiation, and molecule fluorescence. Indeed, these underlying statistical fluctuations of light are associated with the fundamental physical processes behind their generation. In this contribution, we experimentally demonstrate that the manipulation of the quantum electromagnetic fluctuations of two-mode squeezed vacuum states leads to a family of quantum-correlated multiphoton states with tunable mean photon numbers and degree of correlation. Our technique relies on the use of conditional measurements to engineer the excitation mode of the field through the simultaneous subtraction of photons from two-mode squeezed vacuum states. The experimental generation of nonclassical multiphoton states by means of photon subtraction unveils novel mechanisms to control fundamental properties of light. As a remarkable example, we demonstrate the engineering of a quantum state of light with up to ten photons, exhibiting nearly Poissonian photon statistics, that constitutes an important step towards the generation of entangled lasers. Our technique enables a robust protocol to prepare quantum states with multiple photons in high-dimensional spaces and, as such, it constitutes a novel platform for exploring quantum phenomena in mesoscopic systems.
Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle

In this paper, we present a novel 2D–3D pedestrian tracker designed for applications in autonomous vehicles. The system operates on a tracking by detection principle and can track multiple pedestrians in complex urban traffic situations. By using a behavioral motion model and a non-parametric distribution as state model, we are able to accurately track unpredictable pedestrian motion in the presence of heavy occlusion. Tracking is performed independently, on the image and ground plane, in global, motion compensated coordinates. We employ Camera and LiDAR data fusion to solve the association problem where the optimal solution is found by matching 2D and 3D detections to tracks using a joint log-likelihood observation model. Each 2D–3D particle filter then updates their state from associated observations and a behavioral motion model. Each particle moves independently following the pedestrian motion parameters which we learned offline from an annotated training dataset. Temporal stability of the state variables is achieved by modeling each track as a Markov Decision Process with probabilistic state transition properties. A novel track management system then handles high level actions such as track creation, deletion and interaction. Using a probabilistic track score the track manager can cull false and ambiguous detections while updating tracks with detections from actual pedestrians. Our system is implemented on a GPU and exploits the massively parallelizable nature of particle filters. Due to the Markovian nature of our track representation, the system achieves real-time performance operating with a minimal memory footprint. Exhaustive and independent evaluation of our tracker was performed by the KITTI benchmark server, where it was tested against a wide variety of unknown pedestrian tracking situations. On this realistic benchmark, we outperform all published pedestrian trackers in a multitude of tracking metrics.
Motion-artifact-free dynamic 3D shape measurement with hybrid Fourier-transform phase-shifting profilometry

Fourier-transform profilometry (FTP) and phase-shifting profilometry (PSP) are two mainstream fringe projection techniques widely used for three-dimensional (3D) shape measurement. The former is well known for its single-shot nature and the latter for its higher measurement resolution and precision. However, when it comes to measuring the dynamic objects, neither approach is able to produce high-resolution, high-accuracy measurement results that are free from any depth ambiguities and motion-related artifacts. Furthermore, for scenes consisting of both static and dynamic objects, a trade-off between measurement precision and efficiency has to be made, suggesting that using a single approach can yield only suboptimal results. To this end, we propose a novel hybrid Fourier-transform phase-shifting profilometry method to integrate the advantages of both approaches. The motion vulnerability of multi-shot PSP can be overcome, or at least significantly alleviated, through the combination of single-shot FTP, while the high accuracy of PSP can also be preserved when the object is motionless. We design a phase-based, pixel-wise motion detection strategy that can accurately outline the moving object regions from their motionless counterparts. The final measurement result is obtained by fusing the determined regions where the PSP or FTP is applied correspondingly. To validate the proposed hybrid approach, we develop a real-time 3D shape measurement system for measuring multiple isolated moving objects. Experimental results demonstrate that our method achieves significantly higher precision and better robustness compared with conventional approaches where PSP or FTP is applied separately.
Round-Robin Synchronization: Mitigating Communication Bottlenecks in Parameter Servers

Deep learning is usually performed in GPU clusters where each worker machine iteratively refines the model parameters by communicating the update with the Parameter Server (PS). More often than not, workers communicate in a synchronous manner, so as to avoid using out-of-dated parameters and make high-quality refinement in each iteration. However, as all workers synchronize with the PS simultaneously, the communication becomes a severe bottleneck. To address this problem, in this paper we propose the Round-Robin Synchronous Parallel (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SP) scheme, which coordinates workers to make updates in an evenly-gapped, round-robin manner. This way, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SP can minimize the network contention at a minimum cost of the refinement quality. We further extend R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SP to heterogeneous clusters by adaptively tuning the batch size of each worker based on its processing capability. We have implemented R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SP as a ready-to-use python library for status-quo deep learning frameworks. EC2 deployment in GPU clusters show that R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SP effectively mitigates the communication bottlenecks, accelerating the training of popular image classification models by up to 25%.
Fast Discrete Collaborative Multi-Modal Hashing for Large-Scale Multimedia Retrieval

Many achievements have been made on learning to hash for uni-modal and cross-modal retrieval. However, it is still an unsolved problem that how to directly and efficiently learn discriminative discrete hash codes for the multimedia retrieval, where both query and database samples are represented with heterogeneous multi-modal features. With this motivation, we propose a Fast Discrete Collaborative Multi-modal Hashing (FDCMH) method in this paper. We first propose an efficient collaborative multi-modal mapping that first transforms heterogeneous multi-modal features into the unified factors to exploit the complementarity of multi-modal features and preserve the semantic correlations in multiple modalities with linear computation and space complexity. Such shared factors also bridge the heterogeneous modality gap and remove the inter-modality redundancy. Further, we develop an asymmetric hashing learning module to simultaneously correlate the learned hash codes with low-level data distribution and high-level semantics. In particular, this design could avoid the challenging symmetric semantic matrix factorization and O(n2) memory cost (n is the number of training samples). It can support both computation and memory efficient discrete hash optimization. Experiments on several public multimedia retrieval datasets demonstrate the superiority of the proposed approach compared with state-of-the-art hashing techniques, in terms of both model learning efficiency and retrieval accuracy.
Do investors herd in cryptocurrencies – and why?

We investigate herding and its possible determinants in the cryptocurrency market for the December 2013 – July 2018 period. Herding is significant (irrespective of Bitcoin’s presence and trends over time) and strongly asymmetric (appearing stronger during up-markets, low volatility and high volume days), with smaller cryptocurrencies enhancing its magnitude. Our findings suggest that the cryptocurrency market entails strong destabilizing potential, the latter being of particular relevance to the authorities entrusted with its regulatory treatment.
International policy coordination for blockchain supply chains

Abstract From the adoption of the shipping container to coordinated trade liberalization, reductions in trade costs have propelled modern globalization. In this paper, we analyse the application of blockchain to reduce the trade costs of producing and coordinating trusted information along supply chains. Consumers, producers, and governments increasingly demand information about the quality, characteristics, and provenance of traded goods. Partially due to the risks of error and fraud, this information is costly to produce and to maintain between dispersed parties. Recent efforts have sought to overcome these costs—such as paperless trade agendas—through the application of new technologies. Our focus is on how blockchain technology can form a new decentralized economic infrastructure for supply chains by governing decentralized dynamic ledgers of information about goods as they move. We outline the potential economic consequences of blockchain supply chains before examining policy. Effective adoption faces a range of policy challenges including regulatory recognition and interoperability across jurisdictions. We propose a high‐level policy forum in the Asia‐Pacific region to coordinate issues such as open standards and regulatory compatibility.