Reviewerly/exHarmony huggingface dataset:
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling

We investigate the impact of search strategies in neural dialogue modeling.We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate responses.We evaluate these strategies in realistic full conversations with humans and propose a modelbased Bayesian calibration to address annotator bias.These conversations are analyzed using two automatic metrics: log-probabilities assigned by the model and utterance diversity.Our experiments reveal that better search algorithms lead to higher rated conversations.However, finding the optimal selection mechanism to choose from a more diverse set of candidates is still an open question.
Discriminative Transfer Feature and Label Consistency for Cross-Domain Image Classification

Visual domain adaptation aims to seek an effective transferable model for unlabeled target images by benefiting from the well-labeled source images following different distributions. Many recent efforts focus on extracting domain-invariant image representations via exploring target pseudo labels, predicted by the source classifier, to further mitigate the conditional distribution shift across domains. However, two essential factors are overlooked by most existing methods: 1) the learned transferable features should be not only domain invariant but also category discriminative; and 2) the target pseudo label is a two-edged sword to cross-domain alignment. In other words, the wrongly predicted target labels may hinder the class-wise domain matching. In this article, to address these two issues simultaneously, we propose a discriminative transfer feature and label consistency (DTLC) approach for visual domain adaptation problems, which can naturally unify cross-domain alignment with discriminative information preserved and label consistency of source and target data into one framework. To be specific, DTLC first incorporates class discriminative information by penalizing the maximum distance of data pair in the same class and the minimum distance of data pair sharing the different labels for each data into the distribution alignment of both domains. The target pseudo labels are then refined based on the label consistency within the domains. Thus, the transfer feature learning and coarse-to-fine target labels would be coupled to benefit each other in an iterative way. Comprehensive experiments on several visual cross-domain benchmarks verify that DTLC can gain remarkable margins over state-of-the-art (SOTA) nondeep visual domain adaptation methods and even be comparable to competitive deep domain adaptation ones.
Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition

The goal of few-shot image recognition is to distinguish different categories with only one or a few training samples. Previous works of few-shot learning mainly work on general object images. And current solutions usually learn a global image representation from training tasks to adapt novel tasks. However, fine-gained categories are distinguished by subtle and local parts, which could not be captured by global representations effectively. This may hinder existing few-shot learning approaches from dealing with fine-gained categories well. In this work, we propose a multi-attention meta-learning (MattML) method for few-shot fine-grained image recognition (FSFGIR). Instead of using only base learner for general feature learning, the proposed meta-learning method uses attention mechanisms of the base learner and task learner to capture discriminative parts of images. The base learner is equipped with two convolutional block attention modules (CBAM) and a classifier. The two CBAM can learn diverse and informative parts. And the initial weights of classifier are attended by the task learner, which gives the classifier a task-related sensitive initialization. For adaptation, the gradient-based meta-learning approach is employed by updating the parameters of two CBAM and the attended classifier, which facilitates the updated base learner to adaptively focus on discriminative parts. We experimentally analyze the different components of our method, and experimental results on four benchmark datasets demonstrate the effectiveness and superiority of our method.
Escape of a Driven Quantum Josephson Circuit into Unconfined States

Josephson circuits have been ideal systems to study complex non-linear dynamics which can lead to chaotic behavior and instabilities. More recently, Josephson circuits in the quantum regime, particularly in the presence of microwave drives, have demonstrated their ability to emulate a variety of Hamiltonians that are useful for the processing of quantum information. In this paper we show that these drives lead to an instability which results in the escape of the circuit mode into states that are not confined by the Josephson cosine potential. We observe this escape in a ubiquitous circuit: a transmon embedded in a 3D cavity. When the transmon occupies these free-particle-like states, the circuit behaves as though the junction had been removed, and all non-linearities are lost. This work deepens our understanding of strongly driven Josephson circuits, which is important for fundamental and application perspectives, such as the engineering of Hamiltonians by parametric pumping.
Quantum dots as potential sources of strongly entangled photons: Perspectives and challenges for applications in quantum networks

The generation and long-haul transmission of highly entangled photon pairs is a cornerstone of emerging photonic quantum technologies with key applications such as quantum key distribution and distributed quantum computing. However, a natural limit for the maximum transmission distance is inevitably set by attenuation in the medium. A network of quantum repeaters containing multiple sources of entangled photons would allow overcoming this limit. For this purpose, the requirements on the source's brightness and the photon pairs' degree of entanglement and indistinguishability are stringent. Despite the impressive progress made so far, a definitive scalable photon source fulfilling such requirements is still being sought after. Semiconductor quantum dots excel in this context as sub-Poissonian sources of polarization entangled photon pairs. In this work, we present the state-of-the-art set by GaAs based quantum dots and use them as a benchmark to discuss the challenges toward the realization of practical quantum networks.
Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images

Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate. To precisely segment the prostate in the second stage, we formulate prostate segmentation into a multi-task learning framework, which includes a main task to segment the prostate, and an auxiliary task to delineate the prostate boundary. Here, the second task is applied to provide additional guidance of unclear prostate boundary in CT images. Besides, the conventional multi-task deep networks typically share most of the parameters (i.e., feature representations) across all tasks, which may limit their data fitting ability, as the specificity of different tasks are inevitably ignored. By contrast, we solve them by a hierarchically-fused U-Net structure, namely HF-UNet. The HF-UNet has two complementary branches for two tasks, with the novel proposed attention-based task consistency learning block to communicate at each level between the two decoding branches. Therefore, HF-UNet endows the ability to learn hierarchically the shared representations for different tasks, and preserve the specificity of learned representations for different tasks simultaneously. We did extensive evaluations of the proposed method on a large planning CT image dataset and a benchmark prostate zonal dataset. The experimental results show HF-UNet outperforms the conventional multi-task network architectures and the state-of-the-art methods.
An ANN Model for Predicting the Compressive Strength of Concrete

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed in earlier research by another author is used for training and testing the ANN. The proper number of neurons in the hidden layer is determined by checking the features of over-fitting while the synaptic weights and the thresholds are finalized by checking the features of over-training. After that, we use experimental data from other papers to verify and validate our ANN model. The final result of the synaptic weights and the thresholds in the ANN are all listed. Therefore, with them, and using the formulae expressed in this article, anyone can predict the compressive strength of concrete according to the mix proportioning on his/her own.
Group Collaborative Learning for Co-Salient Object Detection

We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus. To learn a better embedding space without extra computational overhead, we explicitly employ auxiliary classification supervision. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our simple GCoNet outperforms 10 cutting-edge models and achieves the new state-of-the-art. We demonstrate this paper’s new technical contributions on a number of important downstream computer vision applications including content aware co-segmentation, co-localization based automatic thumbnails, etc. Code has been made publicly available: https://github.com/fanq15/GCoNet.
Blockchain based decentralized local energy flexibility market

Large-scale deployment of renewable energy sources brings new challenges for smart grid management requiring the development of decentralized solutions and active participation of prosumer and non-grid-owned assets. Local energy flexibility markets can help in monitoring energy flows, motivate changes in prosumers’ energy supply and demand, achieving local energy balance, and optimization of electricity flows. In this paper, we propose a blockchain-based decentralized energy flexibility market enabling small-scale prosumers to trade in a peer-to-peer fashion their flexibility in terms of load modulation concerning the baseline energy profiles. We have defined an energy flexibility token for digitizing the flexibility of prosumers allowing to be traded on the market as an asset and self-enforcing smart contracts for decentralized market operation including functions such as the placement of flexibility bids/offers, trading session management, or energy and financial settlement of energy flexibility transactions. For matching the flexibility bids and offers, a solution based on a greedy heuristic and a bipartite graph is proposed for minimizing the number of flexibility transactions and reducing the blockchain-associated costs, while Oracles are used to assure its secure integration with the blockchain. The blockchain-based flexibility market was validated with the help of the Terni city Distribution System Operator, showing promising results in enabling the self-consumption of renewable energy generated in a small scale urban micro-grid considering live energy monitoring data, and in assuring the local balancing of the demand side in a simulated environment considering many market participants and historical energy data.
FaceX-Zoo

Due to the remarkable progress in recent years, deep face recognition is in great need of public support for practical model production and further exploration. The demands are in three folds, including 1) modular training scheme, 2) standard and automatic evaluation, and 3) groundwork of deployment. To meet these demands, we present a novel open-source project, named FaceX-Zoo, which is constructed with modular and scalable design, and oriented to the academic and industrial community of face-related analysis. FaceX-Zoo provides 1) the training module with various choices of backbone and supervisory head; 2) the evaluation module that enables standard and automatic test on most popular benchmarks; 3) the module of simple yet fully functional face SDK for the validation and primary application of end-to-end face recognition; 4) the additional module that integrates a group of useful tools. Based on these easy-to-use modules, FaceX-Zoo can help the community to easily build stateof-the-art solutions for deep face recognition and, such like the newly-emerged challenge of masked face recognition caused by the worldwide COVID-19 pandemic. Besides, FaceX-Zoo can be easily upgraded and scaled up along with further exploration in face related fields. The source codes and models have been released and received over 900 stars at https://github.com/JDAI-CV/FaceX-Zoo.
How-to conduct a systematic literature review: A quick guide for computer science research

Performing a literature review is a critical first step in research to understanding the state-of-the-art and identifying gaps and challenges in the field. A systematic literature review is a method which sets out a series of steps to methodically organize the review. In this paper, we present a guide designed for researchers and in particular early-stage researchers in the computer-science field. The contribution of the article is the following:•Clearly defined strategies to follow for a systematic literature review in computer science research, and•Algorithmic method to tackle a systematic literature review.
Regularizing Neural Machine Translation by Target-Bidirectional Agreement

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.
Towards Analyzing the Complexity Landscape of Solidity Based Ethereum Smart Contracts

Blockchain-based decentralized cryptocurrency platforms are currently one of the hottest topics in technology. Although most of the interest is generated by cryptocurrency related activities, it is becoming apparent that a much wider spectrum of applications can leverage the blockchain technology. The primary concepts enabling such general use of the blockchain are the so-called smart contracts, which are special programs that run on the blockchain. One of the most popular blockchain platforms that supports smart contracts is Ethereum. As smart contracts typically handle money, ensuring their low number of faults and vulnerabilities are essential. To aid smart contract developers and help to mature the technology, we need analysis tools and studies for smart contracts. As an initiative for this, we propose the adoption of some well-known OO metrics for Solidity smart contracts. Furthermore, we analyze more than 40 thousand Solidity source files with our prototype tool. The results suggest that smart contract programs are short, neither overly complex nor coupled too much, do not rely heavily on inheritance, and either quite well-commented or not commented at all. Moreover, smart contracts could benefit from an external library and dependency management mechanism, as more than 85% of the defined libraries in Solidity files code the same functionalities.
Virtual Reality in Education: Focus on the Role of Emotions and Physiological Reactivity

Cognitive and emotional dimensions are often linked to each other in learning experiences. Moreover, emotions and engagement can lead to better outcomes at the cognitive level. Previous research has indicated that virtual reality (VR) provides a feeling of presence and immersion, which can trigger emotionally engaging learning situations. In this study, we explore the opportunities and challenges related to the use of VR in an educational context. The focus of this article is threefold: First, we explore interdisciplinary research literature related to the use of VR for educational purposes. Second, we introduce our VR pilot study in teacher education, applying three different kinds of VR applications. During the pilot study, we utilized physiological measurements, the self-assessed experience of emotional involvement (PANAS; Watson, Clark, &amp; Tellegen, 1988) and students’ qualitative reporting on VR experiences. Third, we discuss the potential of brain imaging methods such as EEG measures for capturing learning, performance, and emotions in VR and offer pedagogical guidelines for the future design of VR environments.
Robust and High Fidelity Mesh Denoising

This paper presents a simple and effective two-stage mesh denoising algorithm, where in the first stage, face normal filtering is done by using bilateral normal filtering in a robust statistics framework. Tukey's bi-weight function is used as similarity function in the bilateral weighting, which is a robust estimator and stops the diffusion at sharp edges to retain features and removes noise from flat regions effectively. In the second stage, an edge-weighted Laplace operator is introduced to compute a differential coordinate. This differential coordinate helps the algorithm to produce a high-quality mesh without any face normal flips and makes the method robust against high-intensity noise.
Classification with Costly Features Using Deep Reinforcement Learning

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision. On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.
Entity Projection via Machine Translation for Cross-Lingual NER

Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition.Motivated by this fact, we leverage machine translation to improve annotationprojection approaches to cross-lingual named entity recognition.We propose a system that improves over prior entity-projection methods by: (a) leveraging machine translation systems twice: first for translating sentences and subsequently for translating entities; (b) matching entities based on orthographic and phonetic similarity; and (c) identifying matches based on distributional statistics derived from the dataset.Our approach improves upon current state-of-the-art methods for cross-lingual named entity recognition on 5 diverse languages by an average of 4.1 points.Further, our method achieves state-of-the-art F 1 scores for Armenian, outperforming even a monolingual model trained on Armenian source data. 1
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters

Reducing the model redundancy is an important task to deploy complex deep learning models to resource-limited or time-sensitive devices. Directly regularizing or modifying weight values makes pruning procedure less robust and sensitive to the choice of hyperparameters, and it also requires prior knowledge to tune different hyperparameters for different models. To build a better generalized and easy-to-use pruning method, we propose AutoPrune, which prunes the network through optimizing a set of trainable auxiliary parameters instead of original weights. The instability and noise during training on auxiliary parameters will not directly affect weight values, which makes pruning process more robust to noise and less sensitive to hyperparameters. Moreover, we design gradient update rules for auxiliary parameters to keep them consistent with pruning tasks. Our method can automatically eliminate network redundancy with recoverability, relieving the complicated prior knowledge required to design thresholding functions, and reducing the time for trial and error. We evaluate our method with LeNet and VGG-like on MNIST and CIFAR-10 datasets, and with AlexNet, ResNet and MobileNet on ImageNet to establish the scalability of our work. Results show that our model achieves state-of-the-art sparsity, e.g. 7%, 23% FLOPs and 310x, 75x compression ratio for LeNet5 and VGG-like structure without accuracy drop, and 200M and 100M FLOPs for MobileNet V2 with accuracy 73.32% and 66.83% respectively.