Reviewerly/exHarmony huggingface dataset:
Dynamic Random Walk for Superpixel Segmentation

In this paper, we propose a novel random walk model, called Dynamic Random Walk (DRW), which adds a new type of dynamic node to the original RW model and reduces redundant calculation by limiting the walk range. To solve the seed-lacking problem of the proposed DRW, we redefine the energy function of the original RW and use the first arrival probability among each node pair to avoid the interference for each partition. Relaxation of our DRW is performed with the help of a greedy strategy and the Weighted Random Walk Entropy(WRWE) that uses the gradient feature to approximate the stationary distribution. The proposed DRW not only can enhance the boundary adherence but also can run with linear time complexity. To extend our DRW for superpixel segmentation, a seed initialization strategy is proposed. It can evenly distribute seeds in both 2D and 3D space and generate superpixels in only one iteration. The experimental results demonstrate that our DRW is faster than existing RW models and better than the state-of-the-art superpixel segmentation algorithms with respect to both efficiency and segmentation effects.
Paradoxes of Intermediation in Aadhaar: Human Making of a Digital Infrastructure

E-governance projects in India are often purported to minimise the role of human intermediaries. They rely on machines to curtail corruption and bring more efficiency to service delivery. However, studies have found human intermediation and its affordances to be crucial, especially in developing countries where social endowment gaps pose considerable barriers to the effective use of information and communication technologies. Human intermediation in technological infrastructure is thus invisible yet inevitable. I unravel this paradox of intermediation by examining the everyday practices of two sets of intermediaries in Andhra Pradesh and Rajasthan associated with everyday workings of Aadhaar, India’s digital biometric identity infrastructure. Looking at biometric technologies as ‘situated practice’, I draw on the notion of ‘infrastructuring’ to argue that the discretionary practices of human intermediaries in fact stabilise the technological system, anchoring it to a human infrastructure. So, as disruptive technologies such as Aadhaar are implemented, their effective usage will invariably rely on the potential agency of human intermediaries and their collaborative work practices.
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

Capturing long-range dependencies in feature representations is crucial for many visual recognition tasks. Despite recent successes of deep convolutional networks, it remains challenging to model non-local context relations between visual features. A promising strategy is to model the feature context by a fully-connected graph neural network (GNN), which augments traditional convolutional features with an estimated non-local context representation. However, most GNN-bascd approaches require computing a dense graph affinity matrix and hence have difficulty in scaling up to tackle complex real-world visual problems. In this work, we propose an efficient and yet flexible non-local relation representation based on a novel class of graph neural networks. Our key idea is to introduce a latent space to reduce the complexity of graph, which allows us to use a low-rank representation for the graph affinity matrix and to achieve a linear complexity in computation. Extensive experimental evaluations on three major visual recognition tasks show that our method outperforms the prior works with a large margin while maintaining a low computation cost. © 36th International Conference on Machine Learning, ICML 2019. All rights reserved.
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in zeroshot image retrieval and clustering (ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this ’good’ embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this ’good’ embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning (ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to ’confuse’ the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to ’confuse’ the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-theart performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. Code available at http://www.bhchen.cn/.
Dynamic Feature Selection for Clustering High Dimensional Data Streams

Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This type of change has not received as much attention as concept-level change. Furthermore, a lot of the methods proposed for clustering streams (density-based, graph-based, and grid-based) rely on some form of distance as a similarity metric and this is problematic in high-dimensional data where the curse of dimensionality renders distance measurements and any concept of “density” difficult. To address these two challenges we propose combining them and framing the problem as a feature selection problem, specifically a dynamic feature selection problem. We propose a dynamic feature mask for clustering high dimensional data streams. Redundant features are masked and clustering is performed along unmasked, relevant features. If a feature's perceived importance changes, the mask is updated accordingly; previously unimportant features are unmasked and features which lose relevance become masked. The proposed method is algorithm-independent and can be used with any of the existing density-based clustering algorithms which typically do not have a mechanism for dealing with feature drift and struggle with high-dimensional data. We evaluate the proposed method on four density-based clustering algorithms across four high-dimensional streams; two text streams and two image streams. In each case, the proposed dynamic feature mask improves clustering performance and reduces the processing time required by the underlying algorithm. Furthermore, change at the feature level can be observed and tracked.
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel

Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the Neural Tangent Kernel (NTK). This analysis leads to global convergence results but does not work when there is a standard $\ell_2$ regularizer, which is useful to have in practice. We show that sample efficiency can indeed depend on the presence of the regularizer: we construct a simple distribution in $d$ dimensions which the optimal regularized neural net learns with $O(d)$ samples but the NTK requires $\Omega(d^2)$ samples to learn. To prove this, we establish two analysis tools: i) for multi-layer feedforward ReLU nets, we show that the global minimizer of a weakly-regularized cross-entropy loss is the max normalized margin solution among all neural nets, which generalizes well; ii) we develop a new technique for proving lower bounds for kernel methods, which relies on showing that the kernel cannot focus on informative features. Motivated by our generalization results, we study whether the regularized global optimum is attainable. We prove that for infinite-width two-layer nets, noisy gradient descent optimizes the regularized neural net loss to a global minimum in polynomial iterations.
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction

We propose a deep video prediction model conditioned on a single image and an action class. To generate future frames, we first detect keypoints of a moving object and predict future motion as a sequence of keypoints. The input image is then translated following the predicted keypoints sequence to compose future frames. Detecting the keypoints is central to our algorithm, and our method is trained to detect the keypoints of arbitrary objects in an unsupervised manner. Moreover, the detected keypoints of the original videos are used as pseudo-labels to learn the motion of objects. Experimental results show that our method is successfully applied to various datasets without the cost of labeling keypoints in videos. The detected keypoints are similar to human-annotated labels, and prediction results are more realistic compared to the previous methods.
Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality: A Conceptual Framework

Originality/value: This research is among the few publications that study customer attitudes towards robots in travel, tourism and hospitality. The authors develop a matrix of users’ attitudes and behaviours when using robots in travel, tourism and hospitality.
Accurate Pedestrian Detection by Human Pose Regression

Pedestrian detection with high detection and localization accuracy is increasingly important for many practical applications. Due to the flexible structure of the human body, it is hard to train a template-based pedestrian detector that achieves a high detection rate and a good localization accuracy simultaneously. In this paper, we utilize human pose estimation to improve the detection and localization accuracy of pedestrian detection. We design two kinds of pose-indexed features that can considerably improve the discriminability of the detector. In addition to employing a two-stage pipeline to carry out these two tasks, we unify pose estimation and pedestrian detection into a cascaded decision forest in which they can cooperate sufficiently. To prevent irregular positive examples, such as truncated ones, from distracting the pedestrian detection and the pose regression, we clean the positive training data by realigning the bounding boxes and rejecting the wrong positive samples. Experimental results on the Caltech test dataset demonstrate the effectiveness of our proposed method. Our detector achieves 11.1% MR-2, outperforming all existing detectors without using the convolutional neural network (CNN). Moreover, our method can be assembled with other detectors based on CNNs to improve detection and localization performance. By collaborating with the recent CNN-based method, our detector achieves 5.5% MR-2 on the Caltech test dataset, outperforming the state-of-the-art methods.
Social Media Web Scraping using Social Media Developers API and Regex

Nowadays many information can be easily accessed through the internet. Some of social media web applications, such as Facebook, Twitter and Instagram, even provide user with an easy information sharing features. However, the information is presented in the form of a timeline or a feed, which is sometimes not relevant to the user or quite hard to accessed by user because of the redundancy of the information. This situation can be resolved with a Web Scraping method proposed in this paper, that able to search information, combine and present it in a better way according to user preferences. A system is developed to implement the proposed method by using an API that Facebook Developers and Twitter Developers provided. In addition, regular expression (or Regex) which is a language construction that can be used for matching text by using some patterns. Based on experiment conducted in this research, overload information could be suppressed into structure data that store in a database, less redundancy information is presented, and information relevancy could be adjusted to user preferences.
A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit

In intensive care unit (ICU), it is essential to predict the mortality of patients and mathematical models aid in improving the prognosis accuracy. Recently, recurrent neural network (RNN), especially long short-term memory (LSTM) network, showed advantages in sequential modeling and was promising for clinical prediction. However, ICU data are highly complex due to the diverse patterns of diseases; therefore, instead of single LSTM model, an ensemble algorithm of LSTM (eLSTM) is proposed, utilizing the superiority of the ensemble framework to handle the diversity of clinical data. The eLSTM algorithm was evaluated by the acknowledged database of ICU admissions Medical Information Mart for Intensive Care III (MIMIC-III). The investigation in total of 18415 cases shows that compared with clinical scoring systems SAPS II, SOFA, and APACHE II, random forests classification algorithm, and the single LSTM classifier, the eLSTM model achieved the superior performance with the largest value of area under the receiver operating characteristic curve (AUROC) of 0.8451 and the largest area under the precision-recall curve (AUPRC) of 0.4862. Furthermore, it offered an early prognosis of ICU patients. The results demonstrate that the eLSTM is capable of dynamically predicting the mortality of patients in complex clinical situations.
Dual Isogenies and Their Application to Public-Key Compression for Isogeny-Based Cryptography

The isogeny-based protocols SIDH and SIKE have received much attention for being post-quantum key agreement candidates that retain relatively small keys. A recent line of work has proposed and further improved compression of public keys, leading to the inclusion of public-key compression in the SIKE proposal for Round 2 of the NIST Post-Quantum Cryptography Standardization effort. We show how to employ the dual isogeny to significantly increase performance of compression techniques, reducing their overhead from 160–182% to 77–86% for Alice’s key generation and from 98–104% to 59–61% for Bob’s across different SIDH parameter sets. For SIKE, we reduce the overhead of (1) key generation from 140–153% to 61–74%, (2) key encapsulation from 67–90% to 38–57%, and (3) decapsulation from 59–65% to 34–39%. This is mostly achieved by speeding up the pairing computations, which has until now been the main bottleneck, but we also improve (deterministic) basis generation.
Non-Autoregressive Dialog State Tracking

Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself. These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values. However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among (domain, slot) pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns. In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level. Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.
Destructive Error Interference in Product-Formula Lattice Simulation

Quantum computers can efficiently simulate the dynamics of quantum systems. In this Letter, we study the cost of digitally simulating the dynamics of several physically relevant systems using the first-order product-formula algorithm. We show that the errors from different Trotterization steps in the algorithm can interfere destructively, yielding a much smaller error than previously estimated. In particular, we prove that the total error in simulating a nearest-neighbor interacting system of $n$ sites for time $t$ using the first-order product formula with $r$ time slices is $O(nt/r+n{t}^{3}/{r}^{2})$ when $n{t}^{2}/r$ is less than a small constant. Given an error tolerance $ϵ$, the error bound yields an estimate of $\mathrm{max}{O({n}^{2}t/ϵ),O({n}^{2}{t}^{3/2}/{ϵ}^{1/2})}$ for the total gate count of the simulation. The estimate is tighter than previous bounds and matches the empirical performance observed in Childs et al. [Proc. Natl. Acad. Sci. U.S.A. 115, 9456 (2018)]. We also provide numerical evidence for potential improvements and conjecture an even tighter estimate for the gate count.
Do non-free LCD codes over finite commutative Frobenius rings exist?

In this paper, we clarify some aspects of LCD codes in the literature. We first prove that non-free LCD codes do not exist over finite commutative Frobenius local rings. We then obtain a necessary and sufficient condition for the existence of LCD codes over a finite commutative Frobenius ring. We later show that a free constacyclic code over a finite chain ring is an LCD code if and only if it is reversible, and also provide a necessary and sufficient condition for a constacyclic code to be reversible. We illustrate the minimum Lee distance of LCD codes over some finite commutative chain rings with examples. We found some new optimal cyclic codes over $${\mathbb {Z}}_4$$ of different lengths which are LCD codes using computer algebra system MAGMA.
Euclidean Distance Degree of the Multiview Variety

The Euclidean distance degree of an algebraic variety is a well-studied topic in applied algebra and geometry. It has direct applications in geometric modeling, computer vision, and statistics. We use nonproper Morse theory to give a topological interpretation of the Euclidean distance degree of an affine variety in terms of Euler characteristics. As a concrete application, we solve the open problem in computer vision of determining the Euclidean distance degree of the affine multiview variety.
A Joint Relationship Aware Neural Network for Single-Image 3D Human Pose Estimation

This paper studies the task of 3D human pose estimation from a single RGB image, which is challenging without depth information. Recently many deep learning methods are proposed and achieve great improvements due to their strong representation learning. However, most existing methods ignore the relationship between joint features. In this paper, a joint relationship aware neural network is proposed to take both global and local joint relationship into consideration. First, a whole feature block representing all human body joints is extracted by a convolutional neural network. A Dual Attention Module (DAM) is applied on the whole feature block to generate attention weights. By exploiting the attention module, the global relationship between the whole joints is encoded. Second, the weighted whole feature block is divided into some individual joint features. To capture salient joint feature, the individual joint features are refined by individual DAMs. Finally, a joint angle prediction constraint is proposed to consider local joint relationship. Quantitative and qualitative experiments on 3D human pose estimation benchmarks demonstrate the effectiveness of the proposed method.
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation

Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing (NLP) and yield state-of-the-art results on many downstream tasks. Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure, re-designing the pre-training tasks, and leveraging external data and knowledge. The fine-tuning strategy itself has yet to be fully explored. In this paper, we improve the fine-tuning of BERT with two effective mechanisms: self-ensemble and self-distillation. The self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher model. In order to transfer knowledge from the teacher model to the student model efficiently, we further use knowledge distillation, which is called self-distillation because the distillation comes from the model itself through the time dimension. Experiments on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or knowledge. We conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms, and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.
Optimal Tilt Angle Determination for PV Panels Using Real Time Data Acquisition

Abstract Solar energy is one of the promising renewable energy sources which has the potential to meet the future energy demand around the world. To maximize the irradiance fall, solar panels are generally equipped with a motor tracking system and are placed at a specific tilt angle. However, tracking methods are not cost‐effective and a fixed tilt angle is not productive. This study proposes a method for harnessing maximum output from photovoltaic (PV) panels throughout the year by determining the optimal tilt angle. The investigation is performed on real‐time solar PV panels of 5 kWp rated capacity installed at 10°, 20°, 25°, 30°, and 40° angle on the rooftop of engineering institute situated at Chandigarh, India. The real‐time power generation response for a year is used to find the optimal tilt angle. The results obtained from the practical setup are validated by comparing it with the simulation results of the regression analysis. In addition, the impact of the optimal angle on total power generation and carbon emissions is analyzed. The results reveal that the proposed approach is quite effective to increase the power generation of PV panels up to 7–8% and can be practically implemented in any location throughout the world.
Tracking the Dynamics of an Ideal Quantum Measurement

The existence of ideal quantum measurements is one of the fundamental predictions of quantum mechanics. In theory, an ideal measurement projects a quantum state onto the eigenbasis of the measurement observable, while preserving coherences between eigenstates that have the same eigenvalue. The question arises whether there are processes in nature that correspond to such ideal quantum measurements and how such processes are dynamically implemented in nature. Here we address this question and present experimental results monitoring the dynamics of a naturally occurring measurement process: the coupling of a trapped ion qutrit to the photon environment. By taking tomographic snapshots during the detection process, we show that the process develops in agreement with the model of an ideal quantum measurement with an average fidelity of 94%.