Reviewerly/exHarmony huggingface dataset:
Adversarial Binary Coding for Efficient Person Re-Identification

Person re-identification (ReID) aims at associating persons with the same identity across different views/scenes. Most existing methods improve matching accuracy by proposing high-dimensional real-valued features to represent person images comprehensively. However, considering the increasing data scale in real-world applications, the storage and matching efficiencies should be paid attention to as well. In this paper, we propose a binary coding approach for efficient ReID, inspired by the recent advances in adversarial learning. Specifically, the proposed Adversarial Binary Coding (ABC) implicitly fits the feature distribution to the expected binary one by optimizing the Wasserstein distance. To further enhance the semantic discriminability of binary codes, we seamlessly embed the ABC into a similarity measuring deep neural network. By end-to-end learning the framework, compact and discriminative binary features are generated for efficient and accurate ReID. Extensive experiments on large-scale benchmarks demonstrate the superiority of our approach over the state-of-the-art methods in both efficiency and accuracy.
How to Build User Simulators to Train RL-based Dialog Systems

User simulators are essential for training reinforcement learning (RL) based dialog models.The performance of the simulator directly impacts the RL policy.However, building a good user simulator that models real user behaviors is challenging.We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly.We present implementations of six user simulators trained with different dialog planning and generation methods.We then calculate a set of automatic metrics to evaluate the quality of these simulators both directly and indirectly.We also ask human users to assess the simulators directly and indirectly by rating the simulated dialogs and interacting with the trained systems.This paper presents a comprehensive evaluation framework for user simulator study and provides a better understanding of the pros and cons of different user simulators, as well as their impacts on the trained systems.
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r>.7, p<.05). To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level. Finally, we open-source the interactive evaluation platform we built and the dataset we collected to allow researchers to efficiently deploy and evaluate dialog models.
Fully Secure Lightweight Certificateless Signature Scheme for IIoT

In recent years, Industrial Internet of Things (IIoT) has become increasingly important for applications in the industry. Inevitably, security for IIoT has become a priority in order to deploy secure applications. Amongst available cryptographic tools, certificateless signature schemes offer sound authentication solutions and avoid public-key certification from Trusted Third Parties (TTP). Certificateless signatures solve the key escrow problem against the dishonest Private Key Generator (PKG) and has considered to be a useful tool for IIoT applications. Recently, Karati et al. (IEEE Trans. Industrial Informatics, vol.14, no. 8, 2018) presented a lightweight certificateless signature scheme for IIoT Environments. This scheme was then broken by Zhang et al. (IEEE Access, vol. 8, 2018) by simply allowing to change the public key of the signer and using the homomorphic property of the original scheme. In this paper, we introduce a new attack to the scheme against the existential unforgeability, which is universal since we do not have to assume homomorphic property. We then introduce an entirely new lightweight certificateless signature scheme, which has been proven to be fully secure against all attacks found earlier. Our scheme is the first lightweight certificateless signature scheme with full security and is the most efficient in comparison with other existing schemes. It is desirable for IIoT applications. We also provide experimental results to justify our claims.
A Soft Computing Approach Based on Fractional Order DPSO Algorithm Designed to Solve the Corneal Model for Eye Surgery

In this research, a soft computing approach based on a Nature-inspired technique, the Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm, is hybridized with feed-forward artificial neural network (FF-ANN) to suggest and calculate better solutions for non-linear second-order ordinary differential equation (ODE) representing the corneal shape model (CSM).The unknown weights involved in approximate solutions obtained through ANN are tuned with the help of FO-DPSO.To test the robustness of our approach and conditionality of CSM, we have considered several cases of CSM with different aspects of the problem.Solutions obtained by Adam's method are used as a reference point for the sake of comparison.We establish it that FO-DPSO is a suitable technique for tuning the unknown weights involved in the solution designed with ANNs.Our results suggest that the proposed approach is a suitable candidate for solving real-world problems involving differential equations.
Herding Behavior and Cryptocurrency: Market Asymmetries, Inter-Dependency and Intra-Dependency

The study investigates herding behavior in cryptocurrencies in different situations.This study employs daily returns of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018.Quantile regression method is employed to test the herding effect in market asymmetries, inter-dependency and intra-dependency cases.Findings confirm the presence of herding in cryptocurrency in upper quantiles in bullish and high volatility periods because of overexcitement among investors, which lead to high volume trading.Major cryptocurrencies cause herding in sub-major cryptocurrencies, but it is a unidirectional relation.However, no intra-dependency effect among cryptocurrencies and equity market is observed.Results indicate that in the CKK model herding exists at upper quantile in market that may be due when the market is moving fast, continuously trading, and bullish trend are prevailing.Further analysis confirms this narrative as, at upper quantile, the beta of bullish regime is negative and significant, meaning the main source of market herding is a bullish trend in investment, which increases market turbulence and gives investors opportunity to herd.Also, we found that herding in cryptocurrencies exits in high volatility periods, but this herding mostly depends on market activity, not market movement.
Model-Based Joint Bit Allocation Between Geometry and Color for Video-Based 3D Point Cloud Compression

Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint bit allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the bit allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future.
Robo-investment aversion

In five experiments (N = 3,828), we investigate whether people prefer investment decisions to be made by human investment managers rather than by algorithms ("robos"). In all of the studies we investigate morally controversial companies, as it is plausible that a preference for humans as investment managers becomes exacerbated in areas where machines are less competent, such as morality. In Study 1, participants rated the permissibility of an algorithm to autonomously exclude morally controversial stocks from investment portfolios as lower than if a human fund manager did the same; this finding was not different if participants were informed that such exclusions might be financially disadvantageous for them. In Study 2, we show that this robo-investment aversion manifests itself both when considering investment in controversial and non-controversial industries. In Study 3, our findings show that robo-investment aversion is also present when algorithms are given the autonomy to increase investment in controversial stocks. In Studies 4 and 5, we investigate choices between actual humans and an algorithm. In Study 4 -which was incentivized-participants show no robo-investment aversion, but are significantly less likely to choose machines as investment managers for controversial stocks. In contrast, in Study 5 robo-investment aversion is present, but it is not different across controversial and non-controversial stocks. Overall, our findings show a considerable mean effect size for robo-investment aversion (d = -0.39 [-0.45, -0.32]). This suggests that algorithm aversion extends to the financial realm, supporting the existence of a barrier for the adoption of innovative financial technologies (FinTech).
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using a diverse set of solutions. The top-performing methods set a new state-of-the-art for the burst super-resolution task.
Editable free-viewpoint video using a layered neural representation

Generating free-viewpoint videos is critical for immersive VR/AR experience but recent neural advances still lack the editing ability to manipulate the visual perception for large dynamic scenes. To fill this gap, in this paper we propose the first approach for editable photo-realistic free-viewpoint video generation for large-scale dynamic scenes using only sparse 16 cameras. The core of our approach is a new layered neural representation, where each dynamic entity including the environment itself is formulated into a space-time coherent neural layered radiance representation called ST-NeRF. Such layered representation supports fully perception and realistic manipulation of the dynamic scene whilst still supporting a free viewing experience in a wide range. In our ST-NeRF, the dynamic entity/layer is represented as continuous functions, which achieves the disentanglement of location, deformation as well as the appearance of the dynamic entity in a continuous and self-supervised manner. We propose a scene parsing 4D label map tracking to disentangle the spatial information explicitly, and a continuous deform module to disentangle the temporal motion implicitly. An object-aware volume rendering scheme is further introduced for the re-assembling of all the neural layers. We adopt a novel layered loss and motion-aware ray sampling strategy to enable efficient training for a large dynamic scene with multiple performers, Our framework further enables a variety of editing functions, i.e., manipulating the scale and location, duplicating or retiming individual neural layers to create numerous visual effects while preserving high realism. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality, photo-realistic, and editable free-viewpoint video generation for dynamic scenes.
Checking Only When It Is Necessary: Enabling Integrity Auditing Based on the Keyword With Sensitive Information Privacy for Encrypted Cloud Data

The public cloud data integrity auditing technique is used to check the integrity of cloud data through the Third Party Auditor (TPA).In order to make it more practical, we propose a new paradigm called integrity auditing based on the keyword with sensitive information privacy for encrypted cloud data.This paradigm is designed for one of the most common scenario, that is, the user concerns the integrity of a portion of encrypted cloud files that contain his/her interested keywords.In our proposed scheme, the TPA who is only provided with the encrypted keyword, can audit the integrity of all encrypted cloud files that contain the user's interested keyword.Meanwhile, the TPA cannot deduce the sensitive information about which files contain the keyword and how many files contain this keyword.These salient features are realized by leveraging a newly proposed Relation Authentication Label (RAL).The RAL can not only authenticate the relation that files contain the queried keyword, but also be used to generate the auditing proof without sensitive information exposure.We give concrete security analysis showing that the proposed scheme satisfies correctness, auditing soundness and sensitive information privacy.We also conduct the detailed experiments to show the efficiency of our scheme.
Measuring Association Between Labels and Free-Text Rationales

In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance.While prior work focuses on extractive rationales (a subset of the input words), we investigate their lessstudied counterpart: free-text natural language rationales.We demonstrate that pipelines, models for faithful rationalization on informationextraction style tasks, do not work as well on "reasoning" tasks requiring free-text rationales.We turn to models that jointly predict and rationalize, a class of widely used high-performance models for free-text rationalization.We investigate the extent to which the labels and rationales predicted by these models are associated, a necessary property of faithful explanation.Via two tests, robustness equivalence and feature importance agreement, we find that stateof-the-art T5-based joint models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales. 1
MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer

Adapter modules have emerged as a general parameter-efficient means to specialize a pretrained encoder to new domains.Massively multilingual transformers (MMTs) have particularly benefited from additional training of language-specific adapters.However, this approach is not viable for the vast majority of languages, due to limitations in their corpus size or compute budgets.In this work, we propose MAD-G (Multilingual ADapter Generation), which contextually generates language adapters from language representations based on typological features.In contrast to prior work, our time-and space-efficient MAD-G approach enables (1) sharing of linguistic knowledge across languages and (2) zero-shot inference by generating language adapters for unseen languages.We thoroughly evaluate MAD-G in zero-shot crosslingual transfer on part-of-speech tagging, dependency parsing, and named entity recognition.While offering (1) improved fine-tuning efficiency (by a factor of around 50 in our experiments), (2) a smaller parameter budget, and (3) increased language coverage, MAD-G remains competitive with more expensive methods for language-specific adapter training across the board.Moreover, it offers substantial benefits for low-resource languages, particularly on the NER task in low-resource African languages.Finally, we demonstrate that MAD-G's transfer performance can be further improved via: (i) multi-source training, i.e., by generating and combining adapters of multiple languages with available taskspecific training data; and (ii) by further finetuning generated MAD-G adapters for languages with monolingual data.
A U-Net Ensemble for breast lesion segmentation in DCE MRI

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been recognized as an effective tool for Breast Cancer (BC) diagnosis. Automatic BC analysis from DCE-MRI depends on features extracted particularly from lesions, hence, lesions need to be accurately segmented as a prior step. Due to the time and experience required to manually segment lesions in 4D DCE-MRI, automating this task is expected to reduce the workload, reduce observer variability and improve diagnostic accuracy. In this paper we propose an automated method for breast lesion segmentation from DCE-MRI based on a U-Net framework. The contributions of this work are the proposal of a modified U-Net architecture and the analysis of the input DCE information. In that sense, we propose the use of an ensemble method combining three U-Net models, each using a different input combination, outperforming all individual methods and other existing approaches. For evaluation, we use a subset of 46 cases from the TCGA-BRCA dataset, a challenging and publicly available dataset not reported to date for this task. Due to the incomplete annotations provided, we complement them with the help of a radiologist in order to include secondary lesions that were not originally segmented. The proposed ensemble method obtains a mean Dice Similarity Coefficient (DSC) of 0.680 (0.802 for main lesions) which outperforms state-of-the art methods using the same dataset, demonstrating the effectiveness of our method considering the complexity of the dataset.
Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.
Automatic Sparse Connectivity Learning for Neural Networks

Since sparse neural networks usually contain many zero weights, these unnecessary network connections can potentially be eliminated without degrading network performance. Therefore, well-designed sparse neural networks have the potential to significantly reduce the number of floating-point operations (FLOPs) and computational resources. In this work, we propose a new automatic pruning method-sparse connectivity learning (SCL). Specifically, a weight is reparameterized as an elementwise multiplication of a trainable weight variable and a binary mask. Thus, network connectivity is fully described by the binary mask, which is modulated by a unit step function. We theoretically prove the fundamental principle of using a straight-through estimator (STE) for network pruning. This principle is that the proxy gradients of STE should be positive, ensuring that mask variables converge at their minima. After finding Leaky ReLU, Softplus, and identity STEs can satisfy this principle, we propose to adopt identity STE in SCL for discrete mask relaxation. We find that mask gradients of different features are very unbalanced; hence, we propose to normalize mask gradients of each feature to optimize mask variable training. In order to automatically train sparse masks, we include the total number of network connections as a regularization term in our objective function. As SCL does not require pruning criteria or hyperparameters defined by designers for network layers, the network is explored in a larger hypothesis space to achieve optimized sparse connectivity for the best performance. SCL overcomes the limitations of existing automatic pruning methods. Experimental results demonstrate that SCL can automatically learn and select important network connections for various baseline network structures. Deep learning models trained by SCL outperform the state-of-the-art human-designed and automatic pruning methods in sparsity, accuracy, and FLOPs reduction.
Weighted Random Search for CNN Hyperparameter Optimization

Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work [11], we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we compare the WRS method with several state-of-the art hyperparameter optimization methods with respect to Convolutional Neural Network (CNN) hyperparameter optimization. The criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. According to our experiments, the WRS algorithm outperforms the other methods.