Reviewerly/exHarmony huggingface dataset:
Benefits and Obstacles of Blockchain Applications in e-Government

Nowadays, Blockchain Technologies (BCT) could be characterized as one of the most promising trends.We are currently witnessing a plethora of implementations basically in the economic sector with the creation of cryptocurrencies.The majority of researchers and practitioners argues that many benefits could be derived from the use of this innovative technology with the most significant one being the improved sense of trust to BCT applications.At the same time governments pursue amplified trust from their citizens and BCT is gaining momentum since it addresses this of utmost importance problem based on its unique characteristics.More and more governments realize the advances of this technology and participate in pilot applications in different vertical governmental sectors.Even though there are several implementations in the Government sector, there is no comprehensive study towards the analysis of the major characteristics of these developments.This paper moves towards the fulfilment of this gap conducting a thorough analysis of e-Government pilot applications of BCT in a European level.Furthermore, this study discusses the key benefits and main barriers coming from the application of this technology in different domains with BCT experts.
Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search

Artificial Neural networks (ANNs) are often applied to data classification problems. However, training ANNs remains a challenging task due to the large and high dimensional nature of search space particularly in the process of fine-tuning the best set of control parameters in terms of weight and bias. Evolutionary algorithms are proved to be a reliable optimization method for training the parameters. While a number of conventional training algorithms have been proposed and applied to various applications, most of them share the common disadvantages of local optima stagnation and slow convergence. In this paper, we propose a new evolutionary training algorithm referred to as LPSONS, which combines the velocity operators in Particle Swarm Optimization (PSO) with Mantegna Lévy distribution to produce more diverse solutions by dividing the population and generation between different sections of the algorithm. It further combines Neighborhood Search with Mantegna Lévy distribution to mitigate premature convergence and avoid local minima. The proposed algorithm can find optimal results and at the same time avoid stagnation in local optimum solutions as well as prevent premature convergence in training Feedforward Multi-Layer Perceptron (MLP) ANNs. Experiments with fourteen standard datasets from UCI machine learning repository confirm that the LPSONS algorithm significantly outperforms a gradient-based approach as well as some well-known evolutionary algorithms that are also based on enhancing PSO.
Towards Code-switching ASR for End-to-end CTC Models

Although great progress has been made on end-to-end (E2E) models for monolingual and multilingual automatic speech recognition (ASR), there is no successful study for E2E models on the challenging intra-sentential code-switching (CS) ASR task to our best knowledge. In this paper, we propose an approach for CS ASR using E2E connectionist temporal classification (CTC) models. We use a frame-level language identification model to linearly adjust the posteriors of an E2E CTC model. We evaluate the proposed method on Microsoft live Chinese Cortana data with 7000 hours Chinese and English monolingual data and 300 hours CS data as the training data. Trained with only monolingual data without observing any CS data, the proposed method can obtain up to 6.3% relative word error rate (WER) reduction. In the scenario of training with both monolingual and CS data, the proposed method can get up to 4.2% relative WER improvement. This approach can also maintain comparable performance on a Chinese test set compared with baseline models.
BayesNAS: A Bayesian Approach for Neural Architecture Search

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression problem on the architecture parameters from an over-parameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies between a node and its predecessors and successors are often disregarded which result in improper treatment over zero operations. Second, architecture parameters pruning based on their magnitude is questionable. In this paper, we employ the classic Bayesian learning approach to alleviate these two issues by modeling architecture parameters using hierarchical automatic relevance determination (HARD) priors. Unlike other NAS methods, we train the over-parameterized network for only one epoch then update the architecture. Impressively, this enabled us to find the architecture on CIFAR-10 within only 0.2 GPU days using a single GPU. Competitive performance can be also achieved by transferring to ImageNet. As a byproduct, our approach can be applied directly to compress convolutional neural networks by enforcing structural sparsity which achieves extremely sparse networks without accuracy deterioration.
Bounds for multi-end communication over quantum networks

Quantum and private communications are affected by a fundamental limitation which severely restricts the optimal rates that are achievable by two distant parties. To overcome this problem, one needs to introduce quantum repeaters and, more generally, quantum communication networks. Within a quantum network, other problems and features may appear when we move from the basic unicast setting of single-sender/single-receiver to more complex multiend scenarios, where multiple senders and multiple receivers simultaneously use the network to communicate. Assuming various configurations, including multiple-unicast, multicast, and multiple-multicast communication, we bound the optimal rates for transmitting quantum information, distributing entanglement, or generating secret keys in quantum networks connected by arbitrary quantum channels. These bounds cannot be surpassed by the most general adaptive protocols of quantum network communication.
Careful Selection of Knowledge to Solve Open Book Question Answering

Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

This work considers the trade-off between accuracy and testtime computational cost of deep neural networks (DNNs) via anytime predictions from auxiliary predictions. Specifically, we optimize auxiliary losses jointly in an adaptive weighted sum, where the weights are inversely proportional to average of each loss. Intuitively, this balances the losses to have the same scale. We demonstrate theoretical considerations that motivate this approach from multiple viewpoints, including connecting it to optimizing the geometric mean of the expectation of each loss, an objective that ignores the scale of losses. Experimentally, the adaptive weights induce more competitive anytime predictions on multiple recognition data-sets and models than non-adaptive approaches including weighing all losses equally. In particular, anytime neural networks (ANNs) can achieve the same accuracy faster using adaptive weights on a small network than using static constant weights on a large one. For problems with high performance saturation, we also show a sequence of exponentially deepening ANNs can achieve near-optimal anytime results at any budget, at the cost of a const fraction of extra computation.
A Unified Framework for Planning in Adversarial and Cooperative Environments

Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for producing legible plans in cooperative settings such that the observation sequence projected by the plan is consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or convey the actions in a plan when the goal is known to the observer. We present theoretical results on the complexity analysis of our approach. We also present an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains.
Unsupervised Neural Machine Translation with SMT as Posterior Regularization

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables; meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.
Multi-View Multi-Label Learning with View-Specific Information Extraction

Multi-view multi-label learning serves an important framework to learn from objects with diverse representations and rich semantics. Existing multi-view multi-label learning techniques focus on exploiting shared subspace for fusing multi-view representations, where helpful view-specific information for discriminative modeling is usually ignored. In this paper, a novel multi-view multi-label learning approach named SIMM is proposed which leverages shared subspace exploitation and view-specific information extraction. For shared subspace exploitation, SIMM jointly minimizes confusion adversarial loss and multi-label loss to utilize shared information from all views. For view-specific information extraction, SIMM enforces an orthogonal constraint w.r.t. the shared subspace to utilize view-specific discriminative information. Extensive experiments on real-world data sets clearly show the favorable performance of SIMM against other state-of-the-art multi-view multi-label learning approaches.
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages

We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to a significant improvement in source→target translation.We propose three methods to increase the relation among source, pivot, and target languages in the pre-training: 1) step-wise training of a single model for different language pairs, 2) additional adapter component to smoothly connect pre-trained encoder and decoder, and 3) cross-lingual encoder training via autoencoding of the pivot language.Our methods greatly outperform multilingual models up to +2.6% BLEU in WMT 2019 French→German and German→Czech tasks.We show that our improvements are valid also in zero-shot/zeroresource scenarios.
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding

Distantly-supervised relation extraction has proven to be effective to find relational facts from texts.However, the existing approaches treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances.In this paper, we propose a novel multi-layer attention-based model to improve relation extraction with joint label embedding.The model makes full use of both structural information from Knowledge Graphs and textual information from entity descriptions to learn label embeddings through gating integration, while avoiding the imposed noise with an attention mechanism.Then the learned label embeddings are used as another attention over the instances (whose embeddings are also enhanced with the entity descriptions) for improving relation extraction.Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods.
Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization

Chest X-ray (CXR) imaging is one of the most widely-used and cost-effective technology for chest screening and diagnosis of Pulmonary diseases. An always concerned improvement about CXR is to reduce X-ray radiation while achieving ultra-high quality imaging with fine structural details since CXR involves ionizing radiation and tolerance of different populations. In this paper, we present a supervised generative adversarial nets approach to accurately recover high-resolution (HR) CXR images from low-resolution (LR) counterparts while keep pathological invariance. Specifically, the auxiliary label information is introduced to constrain the feature generation to attack the potential risk of pathological variance. Then, spectral normalization is designed to control the performance of discriminative network with the guarantee of theoretical demonstration in controlling Lipschitz bound of discriminator. Results from quantitative and qualitative evaluations demonstrate that our method delivers more authentic improvement for CXR super-resolution (SR) compared to recent state-of-the-art methods. The proposed method has outperformed average 13.0%, 12.2% in FSIM and 13.7%, 12.5% in MSIM on two datasets, respectively. Besides, the index of generative performance GAN-train and GAN-test have achieved average increment 9.3% and 10.5% on CXR2 dataset. Subjective evaluation on SR CXR has outperformed average score 0.425 and 0.525 in terms of pathological invariance and acceptability, respectively.
Domain Adaptive Text Style Transfer

Text style transfer without parallel data has achieved some practical success.However, in the scenario where less data is available, these methods may yield poor performance.In this paper, we examine domain adaptation for text style transfer to leverage massively available data from other domains.These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.To address this challenge, we propose simple yet effective domain adaptive text style transfer models, enabling domain-adaptive information exchange.The proposed models presumably learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information; and (iii) adaptively transfer the styles in a domain-aware manner.We evaluate the proposed models on two style transfer tasks (sentiment and formality) over multiple target domains where only limited non-parallel data is available.Extensive experiments demonstrate the effectiveness of the proposed model compared to the baselines.
RUDDER: Return Decomposition for Delayed Rewards

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards. The latter are related to bias problems in temporal difference (TD) learning and to high variance problems in Monte Carlo (MC) learning. Both problems are even more severe when rewards are delayed. RUDDER aims at making the expected future rewards zero, which simplifies Q-value estimation to computing the mean of the immediate reward. We propose the following two new concepts to push the expected future rewards toward zero. (i) Reward redistribution that leads to return-equivalent decision processes with the same optimal policies and, when optimal, zero expected future rewards. (ii) Return decomposition via contribution analysis which transforms the reinforcement learning task into a regression task at which deep learning excels. On artificial tasks with delayed rewards, RUDDER is significantly faster than MC and exponentially faster than Monte Carlo Tree Search (MCTS), TD(λ), and reward shaping approaches. At Atari games, RUDDER on top of a Proximal Policy Optimization (PPO) baseline improves the scores, which is most prominent at games with delayed rewards.
PHYRE: A New Benchmark for Physical Reasoning

Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit https://player.phyre.ai.
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations

Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task.Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models.In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations.In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations.We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms the current state-of-the-art methods in few-shot scenarios.Our code and datasets can be obtained from https://github.com/THU-KEG/MetaKGR.
Augmented Reality experiment in higher education, for complex system appropriation in mechanical design

Virtual representations are quite widely used in higher education to visualize a design model or simulation. Nevertheless, many students have difficulty grasping mechanical systems, from a 2D design plan or a 3D CAD definition. This is why we implement real system manipulations, associated with various representations, especially for students with no technological skills. Augmented reality can provide an answer to the difficulty of making the link between a representation and the real system. Since augmented reality has not yet been used in mechanical design pedagogy, the challenge was to evaluate the relevance of this technology to facilitate understanding of mechanisms. An augmented reality scenario has been implemented on an electromechanical mechanism. It makes it possible to identify components and their locations, to explore the mechanism and thus more easily identify the kinematic chain or transmission power flow for example. Two different interfaces were used by the learners (tablet and HoloLens glasses), each of which has its advantages. The first experiment was conducted with students in engineering training, as well as bachelor of technology students. During analysis of mechanism practical session, half of the workforce used augmented reality, while the others had only paper documentation and cad. At the end of the session, an assessment of the system's understanding was conducted 'hot' and shows improvements for augmented reality users. Here, augmented reality is used in pedagogy, as a new medium support to pedagogy. But this experience is also relevant for introducing engineering students to a relevant technology for industry of the future, enabling them to measure the potential of augmented reality.
Adaptive Type-2 Fuzzy Neural-Network Control for Teleoperation Systems With Delay and Uncertainties

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov-Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.