Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batch-wise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. As to the first problem, this method establishes a probabilistic discriminant criterion on the target domain via soft labels. Further, this criterion is extended to a global approximation scheme for the second issue; such approximation is also memory-saving. The manifold metric alignment is exploited to be compatible with the embedding space. A theoretical error bound is derived to facilitate the alignment. Extensive experiments have been conducted to investigate the proposal and results of the comparison study manifest the superiority of consistent manifold learning framework.
Alignment-Enhanced Transformer for Constraining NMT with Pre-Specified Translations

We investigate the task of constraining NMT with pre-specified translations, which has practical significance for a number of research and industrial applications. Existing works impose pre-specified translations as lexical constraints during decoding, which are based on word alignments derived from target-to-source attention weights. However, multiple recent studies have found that word alignment derived from generic attention heads in the Transformer is unreliable. We address this problem by introducing a dedicated head in the multi-head Transformer architecture to capture external supervision signals. Results on five language pairs show that our method is highly effective in constraining NMT with pre-specified translations, consistently outperforming previous methods in translation quality.
Enhancing Pointer Network for Sentence Ordering with Pairwise Ordering Predictions

Dominant sentence ordering models use a pointer network decoder to generate ordering sequences in a left-to-right fashion. However, such a decoder only exploits the noisy left-side encoded context, which is insufficient to ensure correct sentence ordering. To address this deficiency, we propose to enhance the pointer network decoder by using two pairwise ordering prediction modules: The FUTURE module predicts the relative orientations of other unordered sentences with respect to the candidate sentence, and the HISTORY module measures the local coherence between several (e.g., 2) previously ordered sentences and the candidate sentence, without the influence of noisy left-side context. Using the pointer mechanism, we then incorporate this dynamically generated information into the decoder as a supplement to the left-side context for better predictions. On several commonly-used datasets, our model significantly outperforms other baselines, achieving the state-of-the-art performance. Further analyses verify that pairwise ordering predictions indeed provide extra useful context as expected, leading to better sentence ordering. We also evaluate our sentence ordering models on a downstream task, multi-document summarization, and the summaries reordered by our model achieve the best coherence scores. Our code is available at https://github.com/DeepLearnXMU/Pairwise.git.
Towards Zero-Shot Learning for Automatic Phonemic Transcription

Automatic phonemic transcription tools are useful for low-resource language documentation. However, due to the lack of training sets, only a tiny fraction of languages have phonemic transcription tools. Fortunately, multilingual acoustic modeling provides a solution given limited audio training data. A more challenging problem is to build phonemic transcribers for languages with zero training data. The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes. In this work, we address this problem by adopting the idea of zero-shot learning. Our model is able to recognize unseen phonemes in the target language without any training data. In our model, we decompose phonemes into corresponding articulatory attributes such as vowel and consonant. Instead of predicting phonemes directly, we first predict distributions over articulatory attributes, and then compute phoneme distributions with a customized acoustic model. We evaluate our model by training it using 13 languages and testing it using 7 unseen languages. We find that it achieves 7.7% better phoneme error rate on average over a standard multilingual model.
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification

An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.
Robust Named Entity Recognition with Truecasing Pretraining

Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data. In particular, capitalization is a strong signal for entities in many languages, and even state of the art models overfit to this feature, with drastically lower performance on uncapitalized text. In this work, we address the problem of robustness of NER systems in data with noisy or uncertain casing, using a pretraining objective that predicts casing in text, or a truecaser, leveraging unlabeled data. The pretrained truecaser is combined with a standard BiLSTM-CRF model for NER by appending output distributions to character embeddings. In experiments over several datasets of varying domain and casing quality, we show that our new model improves performance in uncased text, even adding value to uncased BERT embeddings. Our method achieves a new state of the art on the WNUT17 shared task dataset.
Deep Reinforcement Learning for General Game Playing

General Game Playing agents are required to play games they have never seen before simply by looking at a formal description of the rules of the game at runtime. Previous successful agents have been based on search with generic heuristics, with almost no work done into using machine learning. Recent advances in deep reinforcement learning have shown it to be successful in some two-player zero-sum board games such as Chess and Go. This work applies deep reinforcement learning to General Game Playing, extending the AlphaZero algorithm and finds that it can provide competitive results.
Regional Multi-Scale Approach for Visually Pleasing Explanations of Deep Neural Networks

Recently, many methods to interpret and visualize deep neural network predictions have been proposed, and significant progress has been made. However, a more class-discriminative and visually pleasing explanation is required. Thus, this paper proposes a region-based approach that estimates feature importance in terms of appropriately segmented regions. By fusing the saliency maps generated from multi-scale segmentations, a more class-discriminative and visually pleasing map is obtained. This paper incorporates this regional multi-scale concept into a prediction difference method that is model-agnostic. An input image is segmented in several scales using the superpixel method, and exclusion of a region is simulated by sampling a normal distribution constructed via the boundary prior. The experimental results demonstrate that the regional multi-scale method produces much more class-discriminative and visually pleasing saliency maps.
Quantumness and memory of one qubit in a dissipative cavity under classical control

Hybrid quantum–classical systems constitute a promising architecture for useful control strategies of quantum systems by means of a classical device. Here we provide a comprehensive study of the dynamics of various manifestations of quantumness with memory effects, identified by non-Markovianity, for a qubit controlled by a classical field and embedded in a leaky cavity. We consider both Leggett–Garg inequality and quantum witness as experimentally-friendly indicators of quantumness, also studying the geometric phase of the evolved (noisy) quantum state. We show that, under resonant qubit-classical field interaction, a stronger coupling to the classical control leads to enhancement of quantumness despite a disappearance of non-Markovianity. Differently, increasing the qubit-field detuning (out-of-resonance) reduces the nonclassical behavior of the qubit while recovering non-Markovian features. We then find that the qubit geometric phase can be remarkably preserved irrespective of the cavity spectral width via strong coupling to the classical field. The controllable interaction with the classical field inhibits the effective time-dependent decay rate of the open qubit. These results supply practical insights towards a classical harnessing of quantum properties in a quantum information scenario.
Facilitating K-12 Teachers in Creating Apps by Visual Programming and Project-based Learning

The use of apps in education has become popular in recent years with the development of technology. How to prepare K-12 teachers in creating apps for students learning is worth our attention. This paper presents how to ap-ply visual programming and project-based learning to facilitate K-12 teachers in creating apps. Seven K-12 teachers with less computer background partic-ipated in this study. Data source including (1) K-12 teachers’ project codes, (2) K-12 teachers’ questionnaire, (3) K-12 teachers’ interviews, and (4) ques-tionnaire giving to K-12 students were collected and analyzed. The results reveal that K-12 teachers applied many programming concepts in the coding implementation in these projects. Most students gave positive feedback to these K-12 teachers’ app projects. Furthermore, K-12 teachers were also very positive toward the creation of apps by visual programming and project implementation, which was reflected through K-12 teachers’ questionnaire surveys and interview results. The above findings provide a practicable ap-proach for future teaching to K-12 teachers or novice programmers on app programming.
Digital-analog quantum algorithm for the quantum Fourier transform

The authors introduce an efficient digital-analog quantum algorithm to compute the quantum Fourier transform, a subroutine widely employed in several relevant quantum algorithms. The paper shows that, under sensible assumptions about noise sources, the fidelity of the quantum Fourier transformation improves considerably with this approach when compared against digital quantum computing. This suggests that hybrid protocols combining digital and analog quantum computing could be a sensible approach to reach quantum advantage in the Noisy Intermediate-Scale Quantum era.
Controllable Josephson junction for photon Bose-Einstein condensates

Josephson junctions are the basis for the most sensitive magnetic flux detectors, the definition of the unit volt by the Josephson voltage standard, and superconducting digital and quantum computing. They result from the coupling of two coherent quantum states, as they occur in superconductors, superfluids, atomic Bose-Einstein condensates, and exciton-polariton condensates. In their ground state, Josephson junctions are characterised by an intrinsic phase jump. Controlling this phase jump is fundamental for applications in computing. Here, we experimentally demonstrate controllable phase relations between photon Bose-Einstein condensates resulting from particle exchange in a thermo-optically tunable potential landscape. Our experiment realises an optical analogue of a controllable 0,$\pi$-Josephson junction. By connecting several junctions, we can study a reconfigurable 4-condensate system demonstrating the potential of our approach for analog spin glass simulation. More generally, the combination of static and dynamic nanostructuring techniques introduced in our work offers a powerful platform for the implementation of adaptive optical systems for paraxial light in and outside of thermal equilibrium.
Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks

Movement screens are used to assess the overall movement quality of an athlete. However, these rely on visual observation of a series of movements and subjective scoring. Data-driven methods to provide objective scoring of these movements are being developed. These currently use optical motion capture and require manual pre-processing of data to identify the start and end points of each movement. Therefore, we aimed to use deep learning techniques to automatically identify movements typically found in movement screens and assess the feasibility of performing the classification based on wearable sensor data. Optical motion capture data were collected on 417 athletes performing thirteen athletic movements. We trained an existing deep neural network architecture that combines convolutional and recurrent layers on a subset of 278 athletes. A validation subset of 69 athletes was used to tune the hyperparameters and the final network was tested on the remaining 70 athletes. Simulated inertial measurement data were generated based on the optical motion capture data and the network was trained on this data for different combinations of body segments. Classification accuracy was similar for networks trained using the optical and full-body simulated inertial measurement unit data at 90.2% and 90.9%, respectively. A good classification accuracy of 86.2% was obtained using as few as three simulated sensors placed on the torso and shanks. However, using three simulated sensors on the torso and upper arms or fewer than three sensors resulted in poor accuracy. These results for simulated sensor data indicate the feasibility of classifying athletic movements using a small number of wearable sensors. This could facilitate objective data-driven methods that automatically score overall movement quality using wearable sensors to be easily implemented in the field.
Deterministic learning of hybrid Fuzzy Cognitive Maps and network reduction approaches

Hybrid artificial intelligence deals with the construction of intelligent systems by relying on both human knowledge and historical data records. In this paper, we approach this problem from a neural perspective, particularly when modeling and simulating dynamic systems. Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are requested to define the interaction among the input neurons. As a second contribution, we introduce a fast and deterministic learning rule to compute the weights among input and output neurons. This parameterless learning method is based on the Moore–Penrose inverse and it can be performed in a single step. In addition, we discuss a model to determine the relevance of weights, which allows us to better understand the system. Last but not least, we introduce two calibration methods to adjust the model after the removal of potentially superfluous weights.
A new image-based visual servoing method with velocity direction control

In image-based visual servoing (IBVS), for more satisfying trajectories, the target is always expected to move in straight lines to the desired positions. In this paper, a new IBVS controller for trajectory adjustment is obtained by optimizing a multi-objective function about adjusting a direction in the image plane. By applying the control law based on the optimized parameters, less redundant motion can be achieved in contrast to the traditional methods. Meanwhile, it will be beneficial for reducing the risk of the target leaving the field of view (FOV) and the robustness to the uncertainties of depth and camera internal parameters remains. The experimental results of the 6 degrees of freedom (DOF) robot with eye-in-hand configuration demonstrate the effectiveness and practicability of the proposed method.
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification

Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.
Bidirectional Quantum Teleportation by Using a Four-Qubit GHZ State and Two Bell States

In this paper, a theoretical scheme for bidirectional quantum teleportation by using a four-qubit GHZ state and two Bell states as a quantum channel is proposed. In this scheme, two arbitrary single-qubit quantum states and an unknown three-qubit state can be mutually transmitted between the two communication participants Alice and Bob. The quantum state information can be transmitted if Alice performs four single-qubit measurement operations on her qubits and Bob carries out a four-qubit joint measurement on his qubits, respectively. Then both Alice and Bob can reconstruct the target quantum state by means of appropriate unitary operations. Then we take comparisons with the other bidirectional quantum teleportation protocols in five aspects of quantum information bits transmitted, necessary operations, consumption of quantum resource, consumption of classical resource and intrinsic efficiency. Furthermore, we use channel authentication method to ensure the channel security and prove that our protocol can withstand four attack scenarios. It is concluded that our scheme has significant advantages of more qubits number transmitted, higher intrinsic efficiency and high security.
Blockchain in Academia: Where do we stand and where do we go?

Blockchain is an emerging exponential technology that disrupts the existing way of doing business.During the last 10 years its importance has been highlighted and many organizations worldwide have embraced it and developed innovative applications.Even though Blockchain has been adopted by many sectors, Universities are reluctant to propose new academic programs on this field at bachelor and postgraduate level and fail to efficiently educate students on Blockchain technology and cryptocurrencies.Consequently, universities have failed to investigate the business, technical, legal and other aspects of this technology.As a result, we have the paradox where industry and economy would like to experiment and adopt Blockchain solutions but there is a lack of people with appropriate and adequate skills to work on these solutions.Obviously, this holds back the adoption and the widespread of this technology and currently there are problems in scaling up Blockchain technology.The goal of this paper is to explore the area of Blockchain education and training and propose the structure of a master program that can be used as a model.In doing so, we expand the body of knowledge and we shed light to an important area with limited available information and use cases.
Missed by Filter Lists: Detecting Unknown Third-Party Trackers with Invisible Pixels

Web tracking has been extensively studied over the last decade. To detect tracking, previous studies and user tools rely on filter lists. However, it has been shown that filter lists miss trackers. In this paper, we propose an alternative method to detect trackers inspired by analyzing behavior of invisible pixels. By crawling 84,658 webpages from 8,744 domains, we detect that third-party invisible pixels are widely deployed: they are present on more than 94.51% of domains and constitute 35.66% of all third-party images. We propose a fine-grained behavioral classification of tracking based on the analysis of invisible pixels. We use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 4,216,454 third-party requests. We demonstrate that two popular methods to detect tracking, based on EasyList&EasyPrivacy and on Disconnect lists respectively miss 25.22% and 30.34% of the trackers that we detect. Moreover, we find that if we combine all three lists 379,245 requests originated from 8,744 domains still track users on 68.70% of websites.
Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations

Increasingly, popular online museums have significantly changed the way people acquire cultural knowledge. These online museums have been generating abundant amounts of cultural relics data. In recent years, researchers have used deep learning models that can automatically extract complex features and have rich representation capabilities to implement named-entity recognition (NER). However, the lack of labeled data in the field of cultural relics makes it difficult for deep learning models that rely on labeled data to achieve excellent performance. To address this problem, this paper proposes a semi-supervised deep learning model named SCRNER (Semi-supervised model for Cultural Relics' Named Entity Recognition) that utilizes the bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) model trained by seldom labeled data and abundant unlabeled data to attain an effective performance. To satisfy the semi-supervised sample selection, we propose a repeat-labeled (relabeled) strategy to select samples of high confidence to enlarge the training set iteratively. In addition, we use embeddings from language model (ELMo) representations to dynamically acquire word representations as the input of the model to solve the problem of the blurred boundaries of cultural objects and Chinese characteristics of texts in the field of cultural relics. Experimental results demonstrate that our proposed model, trained on limited labeled data, achieves an effective performance in the task of named entity recognition of cultural relics.