Reviewerly/exHarmony huggingface dataset:
Beyond Black &amp; White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning

Supervised learning assumes that a ground truth label exists.However, the reliability of this ground truth depends on human annotators, who often disagree.Prior work has shown that this disagreement can be helpful in training models.We propose a novel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network.We measure the divergence between the predictions and the target soft labels with several loss-functions and evaluate the models on various NLP tasks.We find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities and thereby mitigates overfitting.It significantly improves performance across tasks beyond the standard approach and prior work.
Stability of Transaction Fees in Bitcoin: A Supply and Demand Perspective

Cryptocurrencies such as Bitcoin are breakthrough financial technologies that promise to revolutionize the digital economy. Unfortunately, their long-term adoption in the business world is imperiled by a lack of stability that manifests as dramatic swings in transaction fees and severe participant dissatisfaction. To date, there has been little academic effort to study how system participants react to volatility in fee movements. Our study addresses this research gap by conceptualizing the Bitcoin platform as a data space market and studying how market equilibrium forms between users who demand data space while trying to avoid transaction delays, and miners who supply data space while trying to maximize fee revenues. Our empirical analysis based on past bitcoin transactions reveals the existence of a relatively flat downward-sloping demand curve and a much steeper upward-sloping supply curve. Regarding users, the inelastic nature of demand signals the utility of Bitcoin as a niche platform for transactions that are otherwise difficult to conduct. This result challenges the belief that users may easily abandon Bitcoin technology given rising transaction costs. We also find that the use of bitcoins as a trading asset is associated with higher levels of tolerance to fees. Regarding miners, the comparatively elastic nature of supply indicates that higher fees stimulate mining by a larger magnitude than suppressing demand. This finding implies that, ceteris paribus, the Bitcoin system turns to self-regulate transaction fees in an efficient manner. Our work has implications for the management of congestion in blockchain-based systems and more broadly for the stability of cryptocurrency markets.
Look Before You Leap: Learning Landmark Features for One-Stage Visual Grounding

An LBYL (‘Look Before You Leap’) Network is proposed for end-to-end trainable one-stage visual grounding. The idea behind LBYL-Net is intuitive and straightforward: we follow a language’s description to localize the target object based on its relative spatial relation to ‘Landmarks’, which is characterized by some spatial positional words and some descriptive words about the object. The core of our LBYL-Net is a landmark feature convolution module that transmits the visual features with the guidance of linguistic description along with different directions. Consequently, such a module encodes the relative spatial positional relations between the current object and its context. Then we combine the contextual information from the landmark feature convolution module with the target’s visual features for grounding. To make this landmark feature convolution light-weight, we introduce a dynamic programming algorithm (termed dynamic max pooling) with low complexity to extract the landmark feature. Thanks to the landmark feature convolution module, we mimic the human behavior of ‘Look Before You Leap’ to design an LBYL-Net, which takes full consideration of contextual information. Extensive experiments show our method’s effectiveness in four grounding datasets. Specifically, our LBYL-Net outperforms all state-of-the-art two-stage and one-stage methods on ReferitGame. On RefCOCO and RefCOCO+, Our LBYL-Net also achieves comparable results or even better results than existing one-stage methods. Code is available at https://github.com/svip-lab/LBYLNet.
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features

Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text.We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence.Existing datasets contain a mix of conversational responses that are faithful to selected evidence as well as more subjective or chit-chat style responses.We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity.At training time, additional inputs based on these evaluation measures are given to the dialogue model.At generation time, these additional inputs act as stylistic controls that encourage the model to generate responses that are faithful to the provided evidence.We also investigate the usage of additional controls at decoding time using resampling techniques.In addition to automatic metrics, we perform a human evaluation study where raters judge the output of these controlled generation models to be generally more objective and faithful to the evidence compared to baseline dialogue systems.
Sensing leg movement enhances wearable monitoring of energy expenditure

Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.
Deconstructing “Decentralization”

This chapter first describes the current use of the term “decentralized” as applied to permissionless blockchains like Bitcoin and Ethereum. Then, it analyzes the complex, contested nature of the term, delving into issues such as the different domains where power is exercised in blockchain systems and the fluid nature of power concentration and diffusion in these systems. Next, it provides examples of events that reveal sites of concentrated power in permissionless blockchain systems, focusing on the activities of software developers and miners. Finally, it explores the significant implications for law of using a fuzzy term like “decentralized” to make legal decisions, as misunderstandings about power hidden in the term can lead to flawed decisions across a wide swath of legal fields.
BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection

In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Codes have been released.
Representation Degeneration Problem in Training Natural Language Generation Models.

We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the \emph{representation degeneration problem}. We observe that when training a model for natural language generation tasks through likelihood maximization with the weight tying trick, especially with big training datasets, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms.
On the Optimal Size of Candidate Feature Set in Random forest

Random forest is an ensemble method that combines many decision trees. Each level of trees is determined by an optimal rule among a candidate feature set. The candidate feature set is a random subset of all features, and is different at each level of trees. In this article, we investigated whether the accuracy of Random forest is affected by the size of the candidate feature set. We found that the optimal size differs from data to data without any specific pattern. To estimate the optimal size of feature set, we proposed a novel algorithm which uses the out-of-bag error and the ‘SearchSize’ exploration. The proposed method is significantly faster than the standard grid search method while giving almost the same accuracy. Finally, we demonstrated that the accuracy of Random forest using the proposed algorithm has increased significantly compared to using a typical size of feature set.
Correction of Scanning Electron Microscope Imaging Artifacts in a Novel Digital Image Correlation Framework

The combination of digital image correlation (DIC) and scanning electron microscopy (SEM) enables to extract high resolution full field displacement data, based on the high spatial resolution of SEM and the sub-pixel accuracy of DIC. However, SEM images may exhibit a considerable amount of imaging artifacts, which may seriously compromise the accuracy of the displacements and strains measured from these images. The current study proposes a unified general framework to correct for the three dominant types of SEM artifacts, i.e. spatial distortion, drift distortion and scan line shifts. The artifact fields are measured alongside the mechanical deformations to minimize the artifact induced errors in the latter. To this purpose, Integrated DIC (IDIC) is extended with a series of hierarchical mapping functions that describe the interaction of the imaging process with the mechanics. A new IDIC formulation based on these mapping functions is derived and the potential of the framework is tested by a number of virtual experiments. The effect of noise in the images and different regularization options for the artifact fields are studied. The error in the mechanical displacement fields measured for noise levels up to 5% is within the usual DIC accuracy range for all the cases studied, while it is more than 4 pixels if artifacts are ignored. A validation on real SEM images at three different magnifications confirms that all three distortion fields are accurately captured. The results of all virtual and real experiments demonstrate the accuracy of the methodology proposed, as well as its robustness in terms of convergence.
Multi-Task Learning for Abstractive and Extractive Summarization

The abstractive method and extractive method are two main approaches for automatic document summarization. In this paper, to fully integrate the relatedness and advantages of both approaches, we propose a general unified framework for abstractive summarization which incorporates extractive summarization as an auxiliary task. In particular, our framework is composed of a shared hierarchical document encoder, a hierarchical attention mechanism-based decoder, and an extractor. We adopt multi-task learning method to train these two tasks jointly, which enables the shared encoder to better capture the semantics of the document. Moreover, as our main task is abstractive summarization, we constrain the attention learned in the abstractive task with the labels of the extractive task to strengthen the consistency between the two tasks. Experiments on the CNN/DailyMail dataset demonstrate that both the auxiliary task and the attention constraint contribute to improve the performance significantly, and our model is comparable to the state-of-the-art abstractive models. In addition, we cut half number of labels of the extractive task, pretrain the extractor, and jointly train the two tasks using the estimated sentence salience of the extractive task to constrain the attention of the abstractive task. The results do not decrease much compared with using full-labeled data of the auxiliary task.
A Training Data Set Cleaning Method by Classification Ability Ranking for the $k$ -Nearest Neighbor Classifier

The k-nearest neighbor (KNN) rule is a successful technique in pattern classification due to its simplicity and effectiveness. As a supervised classifier, KNN classification performance usually suffers from low-quality samples in the training data set. Thus, training data set cleaning (TDC) methods are needed for enhancing the classification accuracy by cleaning out noisy, or even wrong, samples in the original training data set. In this paper, we propose a classification ability ranking (CAR)-based TDC method to improve the performance of a KNN classifier, namely CAR-based TDC method. The proposed classification ability function ranks a training sample in terms of its contribution to correctly classify other training samples as a KNN through the leave-one-out (LV1) strategy in the cleaning stage. The training sample that likely misclassifies the other samples during the KNN classifications according to the LV1 strategy is considered to have lower classification ability and will be cleaned out from the original training data set. Extensive experiments, based on ten real-world data sets, show that the proposed CAR-based TDC method can significantly reduce the classification error rates of KNN-based classifiers, while reducing computational complexity thanks to a smaller cleaned training data set.
Two-party quantum private comparison protocol with maximally entangled seven-qubit state

Quantum private comparison (QPC), whose security is based on some laws of quantum mechanics (e.g. quantum noncloning theorem and Heisenbergs uncertainty principle), allows [Formula: see text] parties who do not trust each other to judge whether their secret data are the same while maintaining data privacy. In this paper, we investigate the utility of the maximally entangled seven-qubit state for QPC, and we propose a new protocol which enables two parties to compare their secret data with each other for equality without disclosing their secret data. In our protocol, single particle measurements and Bell-basis measurements are employed, both of which can be implemented with current technologies. In addition to quantum measurements, our protocol does not use other quantum technologies such as entanglement swapping and unitary operations. A semi-honest third-party who assists two parties in implementing the protocol is assumed in our protocol. Furthermore, we use the entanglement correlations of the maximally entangled seven-qubit state and collaborative computing between parties for privacy protection, and we use quantum key distribution (QKD) to ensure the security of the cooperative computing when two parties are in different locations. What is more, we show that the security towards both outsider and insider attacks can be guaranteed.
Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings

We introduce the use of Poincaré embeddings to improve existing state-of-the-art approaches to domain-specific taxonomy induction from text as a signal for both relocating wrong hyponym terms within a (pre-induced) taxonomy as well as for attaching disconnected terms in a taxonomy.This method substantially improves previous state-of-the-art results on the SemEval-2016 Task 13 on taxonomy extraction.We demonstrate the superiority of Poincaré embeddings over distributional semantic representations, supporting the hypothesis that they can better capture hierarchical lexical-semantic relationships than embeddings in the Euclidean space.
A comparison of statistical association measures for identifying dependency-based collocations in various languages.

This paper presents an exploration of different statistical association measures to automatically identify collocations from corpora in English, Portuguese, and Spanish.To evaluate the impact of the association measures we manually annotated corpora with three different syntactic patterns of collocations (adjective-noun, verb-object and nominal compounds).We took advantage of the PARSEME 1.1 Shared Task corpora by selecting a subset of 155k tokens in the three referred languages, in which we annotated 1, 526 collocations with their Lexical Functions according to the Meaning-Text Theory.Using the resulting gold-standard, we have carried out a comparison between frequency data and several well-known association measures, both symmetric and asymmetric.The results show that the combination of dependency triples with raw frequency information is as powerful as the best association measures in most syntactic patterns and languages.Furthermore, and despite the asymmetric behaviour of collocations, directional approaches perform worse than the symmetric ones in the extraction of these phraseological combinations.
Why reinventing the wheels? An empirical study on library reuse and re-implementation

Nowadays, with the rapid growth of open source software (OSS), library reuse becomes more and more popular since a large amount of third- party libraries are available to download and reuse. A deeper understanding on why developers reuse a library (i.e., replacing self-implemented code with an external library) or re-implement a library (i.e., replacing an imported external library with self-implemented code) could help researchers better understand the factors that developers are concerned with when reusing code. This understanding can then be used to improve existing libraries and API recommendation tools for researchers and practitioners by using the developers concerns identified in this study as design criteria. In this work, we investigated the reasons behind library reuse and re-implementation. To achieve this goal, we first crawled data from two popular sources, F-Droid and GitHub. Then, potential instances of library reuse and re-implementation were found automatically based on certain heuristics. Next, for each instance, we further manually identified whether it is valid or not. For library re-implementation, we obtained 82 instances which are distributed in 75 repositories. We then conducted two types of surveys (i.e., individual survey to corresponding developers of the validated instances and another open survey) for library reuse and re-implementation. For library reuse individual survey, we received 36 responses out of 139 contacted developers. For re-implementation individual survey, we received 13 responses out of 71 contacted developers. In addition, we received 56 responses from the open survey. Finally, we perform qualitative and quantitative analysis on the survey responses and commit logs of the validated instances. The results suggest that library reuse occurs mainly because developers were initially unaware of the library or the library had not been introduced. Re-implementation occurs mainly because the used library method is only a small part of the library, the library dependencies are too complicated, or the library method is deprecated. Finally, based on all findings obtained from analyzing the surveys and commit messages, we provided a few suggestions to improve the current library recommendation systems: tailored recommendation according to users’ preferences, detection of external code that is similar to a part of the users’ code (to avoid duplication or re-implementation), grouping similar recommendations for developers to compare and select the one they prefer, and disrecommendation of poor-quality libraries.
Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype

Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure to make progress is attributed to the extreme heterogeneity of this condition, enormous complexity of the human brain, limitations of extant research paradigms, and inadequacy of traditional statistical methods to integrate or interpret increasingly large amounts of multidimensional information relevant to unravelling brain function. Fortunately, the rapidly developing science of machine learning appears to provide tools capable of addressing each of these impediments. Enthusiasm about the potential of machine learning methods to break the current impasse is reflected in the steep increase in the number of scientific publication about the application of machine learning to the study of schizophrenia. Machine learning approaches are, however, poorly understood by schizophrenia researchers and clinicians alike. In this paper, we provide a simple description of the nature and techniques of machine learning and their application to the study of schizophrenia. We then summarize its potential and constraints with illustrations from six studies of machine learning in schizophrenia and address some common misconceptions about machine learning. We suggest some guidelines for researchers, readers, science editors and reviewers of the burgeoning machine learning literature in schizophrenia. In order to realize its enormous promise, we suggest the need for the disciplined application of machine learning methods to the study of schizophrenia with a clear recognition of its capability and challenges accompanied by a concurrent effort to improve machine learning literacy among neuroscientists and mental health professionals.