Reviewerly/exHarmony huggingface dataset:
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation.

In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is hard to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA). We show that WUDA provably ruins all UDA methods if taking no care of label noise in SD, and to this end, we propose a Butterfly framework, a panacea for all difficulties in WUDA. Butterfly maintains four models (e.g., deep networks) simultaneously, where two take care of all adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and SD-to-TD-distributional) and then the other two can focus on classification in TD. As a consequence, Butterfly possesses all the necessary components for all the challenges in WUDA. Experiments demonstrate that under WUDA, Butterfly significantly outperforms existing baseline methods.
Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings

Gijs Wijnholds, Mehrnoosh Sadrzadeh. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Extremely wide-angle lens with transmissive and catadioptric integration

Wide-angle lenses can be generally classified into two structures: a transmitted fish-eye structure and a catadioptric structure with a panoramic annular lens (PAL). Compared with the former, the latter exhibits better imaging capability for a super-large field of view. However, the PAL causes this type of optical system to lose its imaging ability in a small field of view in front of the lens. In this study, a novel extremely wide-angle lens is designed by combining the fish-eye and catadioptric structures. The designed lens integrates the transmissive and catadioptric structures and can simultaneously observe two fields of view. The two fields of view of the lens complement each other and completely eliminate the central observation blind area of ordinary catadioptric wide-angle lens. The forward field of view of the lens is 360°×(0°–56°), and the annular field of view is 360°×(55°–115°). The total field of view of the optical system reaches 230° when only spherical lenses are used for all the optical elements. The optical model shows that the imaging quality of the extremely wide-angle lens is good, which proves the advantages of the new optical system in a large field of view.
Person Re-Identification over Encrypted Outsourced Surveillance Videos

Person re-identification (Re-ID) has attracted extensive attention due to its potential to identify a person of interest from different surveillance videos.With the increasing amount of the surveillance videos, high computation and storage costs have posed a great challenge for the resource-constrained users.In recent years, the cloud storage services have made a large volume of video data outsourcing become possible.However, person Re-ID over outsourced surveillance videos could lead to a security threat, i.e., the privacy leakage of the innocent person in these videos.Therefore, we propose an efFicient privAcy-preseRving peRson Re-ID Scheme (FARRIS) over outsourced surveillance videos, which can ensure the privacy of the detected person while providing the person Re-ID service.Specifically, FARRIS exploits the convolutional neural network (CNN) and kernels based supervised hashing (KSH) to extract the efficient person Re-ID feature.Then, we design a secret sharing based Hamming distance computation protocol to allow cloud servers to calculate similarities among obfuscated feature indexes.Furthermore, a dual Merkle hash trees based verification is proposed, which permits users to validate the correctness of the matching results.The extensive experimental results and security analysis demonstrate that FARRIS can work efficiently, without compromising the privacy of the involved person.
Fast optimization of non-negative matrix tri-factorization

Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-dimensional feature representation of relational data. Currently, NMTF learns a representation of a dataset through an optimization procedure that typically uses multiplicative update rules. This procedure has had limited success, and its failure cases have not been well understood. We here perform an empirical study involving six large datasets comparing multiplicative update rules with three alternative optimization methods, including alternating least squares, projected gradients, and coordinate descent. We find that methods based on projected gradients and coordinate descent converge up to twenty-four times faster than multiplicative update rules. Furthermore, alternating least squares method can quickly train NMTF models on sparse datasets but often fails on dense datasets. Coordinate descent-based NMTF converges up to sixteen times faster compared to well-established methods.
A model for initiating research data management services at academic libraries

Background: Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM. Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) a teaching tool kit to teach an introductory RDM class, or (3) strategies for hosting a data class series.Case Presentation: Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules. Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes. All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library.Conclusion: The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services. The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services. The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions.
Splitting Epistemic Logic Programs

Epistemic logic programs constitute an extension of the stable models semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some regular literal is true in all or some stable models. As it can be imagined, the associated semantics has proved to be non-trivial, as the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. We formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing proposals fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond in 1991.
Locally private bayesian inference for count models

We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the social sciences. Our method satisfies limited precision local privacy, a generalization of local differential privacy, which we introduce to formulate privacy guarantees appropriate for sparse count data. We develop an MCMC algorithm that approximates the locally private posterior over model parameters given data that has been locally privatized by the geometric mechanism (Ghosh et al., 2012). Our solution is based on two insights: 1) a novel reinterpretation of the geometric mechanism in terms of the Skellam distribution (Skellam, 1946) and 2) a general theorem that relates the Skellam to the Bessel distribution (Yuan & Kalbfleisch, 2000). We demonstrate our method in two case studies on real-world email data in which we show that our method consistently outperforms the commonly-used naive approach, obtaining higher quality topics in text and more accurate link prediction in networks. On some tasks, our privacy-preserving method even outperforms non-private inference which conditions on the true data.
Entropy-based global and local weight adaptive image segmentation models

This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese (CV) model , while the image's local entropy is used to establish the index for measuring the image's gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process. Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms. Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.
An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography

Ataxias are a group of neurodegenerative disorders characterized by cerebellar dysfunction that cause irregularities in the rate, rhythm, amplitude, and force of voluntary movements. The electrooculogram (EOG) may provide clues about ataxic disorders because most of these patients have difficulty with visual tracking and fixing their gaze. Using electrodes, EOG records the biopotentials generated by eye movements. In this paper, three surface electrodes are placed around the eye socket, and the biopotentials generated by eye movements are acquired using a commercial bioamplifier device. Next, the signals are sent to the computer to be digitally processed to extract the rate of saccades as well as the delay and deviation of the gaze in response to a stimulus. These features are analysed in a novel software application designed to help physicians in evaluating ataxia. After applying several tests to both healthy and ataxia-affected patients, differences in EOG results were found. The evaluation of the reliability of the designed software application is made according to three metrics: sensitivity, specificity, and accuracy. The results indicate the proposed system’s viability as an affordable method for evaluation of ataxic disorders.
Enforcing trustworthy cloud SLA with witnesses: A game theory–based model using smart contracts

Summary There lacks trust between the cloud customer and provider to enforce traditional cloud SLA (Service Level Agreement) where the blockchain technique seems a promising solution. However, current explorations still face challenges to prove that the off‐chain SLO (Service Level Objective) violations really happen before recorded into the on‐chain transactions. In this paper, a witness model is proposed implemented with smart contracts to solve this trust issue. The introduced role, “Witness”, gains rewards as an incentive for performing the SLO violation report, and the payoff function is carefully designed in a way that the witness has to tell the truth, for maximizing the rewards. This fact that the witness has to be honest is analyzed and proved using the Nash Equilibrium principle of game theory. For ensuring the chosen witnesses are random and independent, an unbiased selection algorithm is proposed to avoid possible collusions. An auditing mechanism is also introduced to detect potential malicious witnesses. Specifically, we define three types of malicious behaviors and propose quantitative indicators to audit and detect these behaviors. Moreover, experimental studies based on Ethereum blockchain demonstrate the proposed model is feasible, and indicate that the performance, ie, transaction fee, of each interface follows the design expectations.
Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis

Neural source-filter (NSF) models are deep neural networks that produce waveforms given input acoustic features. They use dilated-convolution-based neural filter modules to filter sine-based excitation for waveform generation, which is different from WaveNet and flow-based models. One of the NSF models, called harmonic-plus-noise NSF (h-NSF) model, uses separate pairs of source and neural filters to generate harmonic and noise waveform components. It is close to WaveNet in terms of speech quality while being superior in generation speed. The h-NSF model can be improved even further. While h-NSF merges the harmonic and noise components using pre-defined digital low- and high-pass filters, it is well known that the maximum voice frequency (MVF) that separates the periodic and aperiodic spectral bands are time-variant. Therefore, we propose a new h-NSF model with time-variant and trainable MVF. We parameterize the digital low- and high-pass filters as windowed-sinc filters and predict their cut-off frequency (i.e., MVF) from the input acoustic features. Our experiments demonstrated that the new model can predict a good trajectory of the MVF and produce high-quality speech for a text-to-speech synthesis system.
WS-AM: Weakly Supervised Attention Map for Scene Recognition

Recently, convolutional neural networks (CNNs) have achieved great success in scene recognition. Compared with traditional hand-crafted features, CNN can be used to extract more robust and generalized features for scene recognition. However, the existing scene recognition methods based on CNN do not sufficiently take into account the relationship between image regions and categories when choosing local regions, which results in many redundant local regions and degrades recognition accuracy. In this paper, we propose an effective method for exploring discriminative regions of the scene image. Our method utilizes the gradient-weighted class activation mapping (Grad-CAM) technique and weakly supervised information to generate the attention map (AM) of scene images, dubbed WS-AM—weakly supervised attention map. The regions, where the local mean and the local center value are both large in the AM, correspond to the discriminative regions helpful for scene recognition. We sampled discriminative regions on multiple scales and extracted the features of large-scale and small-scale regions with two different pre-trained CNNs, respectively. The features from two different scales were aggregated by the improved vector of locally aggregated descriptor (VLAD) coding and max pooling, respectively. Finally, the pre-trained CNN was used to extract the global feature of the image in the fully- connected (fc) layer, and the local features were combined with the global feature to obtain the image representation. We validated the effectiveness of our method on three benchmark datasets: MIT Indoor 67, Scene 15, and UIUC Sports, and obtained 85.67%, 94.80%, and 95.12% accuracy, respectively. Compared with some state-of-the-art methods, the WS-AM method requires fewer local regions, so it has a better real-time performance.
Non-interactive Private Decision Tree Evaluation

In this paper, we address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the server's private model. The goal of the computation is to obtain the classification while preserving the privacy of both—the decision tree and the client input. After the computation, the client learns the classification result and nothing else, and the server learns nothing. Existing privacy-preserving protocols that address this problem use or combine different generic secure multiparty computation approaches resulting in several interactions between the client and the server. Our goal is to design and implement a novel client-server protocol that delegates the complete tree evaluation to the server while preserving privacy and reducing the overhead. The idea is to use fully (somewhat) homomorphic encryption and evaluate the tree on ciphertexts encrypted under the client's public key. However, since current somewhat homomorphic encryption schemes have high overhead, we combine efficient data representations with different algorithmic optimizations to keep the computational overhead and the communication cost low. As a result, we are able to provide the first non-interactive protocol, that allows the client to delegate the evaluation to the server by sending an encrypted input and receiving only the encryption of the result. Our scheme has only one round and evaluates a complete tree of depth 10 within seconds.
Training a Camera to Perform Long-Distance Eye Tracking by Another Eye-Tracker

Appearance-based gaze estimation techniques have been greatly advanced in these years. However, using a single camera for appearance-based gaze estimation has been limited to short distance in previous studies. In addition, labeling of training samples has been a time-consuming and unfriendly step in previous appearance-based gaze estimation studies. To bridge these significant gaps, this paper presents a new long-distance gaze estimation paradigm: train a camera to perform eye tracking by another eye tracker, named Learning-based Single Camera eye tracker (LSC eye-tracker). In the training stage, the LSC eye-tracker simultaneously acquired gaze data by a commercial trainer eye tracker and face appearance images by a long-distance trainee camera, based on which deep convolutional neural network (CNN) models are utilized to learn the mapping from appearance images to gazes. In the application stage, the LSC eye-tracker works alone to predict gazes based on the acquired appearance images by the single camera and the trained CNN models. Our experimental results show that the LSC eye-tracker enables both population-based eye tracking and personalized eye tracking with promising accuracy and performance.
Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction

Automatic helpfulness prediction aims to prioritize online product reviews by quality. Existing methods have combined review content and star ratings for automatic helpfulness prediction. However, the relationship between review content and star ratings is not explicitly captured, which limits the capability of rating information in influencing review content. This paper proposes a deep neural architecture to learn the explicit content-rating interaction (ECRI) for automatic helpfulness prediction. Specifically, ECRI explores two methods to interact review content with star ratings and adaptively specify the amount of rating information needed by review content. ECRI is evaluated against state-of-the-art methods on six real-world domains of the Amazon 5-core dataset. Experimental results demonstrate that exploiting the explicit content-rating interaction improves automatic helpfulness prediction. The source code of ECRI can be obtained from https://github.com/tokawah/ECRI .
Cross-modal Image-Text Retrieval with Multitask Learning

In this paper, we propose a multi-task learning approach for cross-modal image-text retrieval. First, a correlation network is proposed for relation recognition task, which helps learn the complicated relations and common information of different modalities. Then, we propose a correspondence cross-modal autoencoder for cross-modal input reconstruction task, which helps correlate the hidden representations of two uni-modal autoencoders. In addition, to further improve the performance of cross-modal retrieval, two regularization terms (variance and consistency constraints) are introduced to the cross-modal embeddings such that the learned common information has large variance and is modality invariant. Finally, to enable large-scale cross-modal similarity search, a flexible binary transform network is designed to convert the text and image embeddings into binary codes. Extensive experiments on two benchmark datasets demonstrate that our model has robust superiority over the compared strong baseline methods. Source code is available at \urlhttps://github.com/daerv/DAEVR.
Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks.

Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. How pervasive is this bias, and where does it come from? We find that, when trained on datasets of images with conflicting shape and texture, CNNs learn to classify by shape at least as easily as by texture. What factors, then, produce the texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have small but significant and largely independent effects on the level of texture bias. However, all objectives and architectures still lead to models that make texture-based classification decisions a majority of the time, even if shape information is decodable from their hidden representations. The effect of data augmentation is much larger. By taking less aggressive random crops at training time and applying simple, naturalistic augmentation (color distortion, noise, and blur), we train models that classify ambiguous images by shape a majority of the time, and outperform baselines on out-of-distribution test sets. Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not primarily from differences in their internal workings, but from differences in the data that they see.
Beyond scandal? Blockchain technologies and the fragile legitimacy of post-2008 finance

Abstract How do applications of emergent technologies contribute to the social legitimacy of finance? To address this question, we examine a set of technologies that have received increasing industry, media, and scholarly attention over the past decade: blockchains. Harnessing the concepts of ‘moral economy’ and ‘scandal’, we identify both possibilities and limits for blockchain applications to legitimate a range of monetary and investment activities. However, we also find that a persistent individualisation of responsibility for failures and shortcomings with ‘live’ blockchain experimentation has undermined the potentially legitimating aspects of this technology. Combining a reliance on technological fixes with a persistent individualist moral economy, we conclude, works against efforts to confront head-on the tensions underpinning the on-going legitimacy crises facing finance.