Reviewerly/exHarmony huggingface dataset:
Document-level Relation Extraction as Semantic Segmentation

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information, parallel to the semantic segmentation task in computer vision. Herein, we propose a Document U-shaped Network for document-level relation extraction. Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples. Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets DocRED, CDR, and GDA.
Tunable Coupling Architecture for Fixed-Frequency Transmon Superconducting Qubits

Implementation of high-fidelity two-qubit operations is a key ingredient for scalable quantum error correction. In superconducting qubit architectures tunable buses have been explored as a means to higher fidelity gates. However, these buses introduce new pathways for leakage. Here we present a modified tunable bus architecture appropriate for fixed-frequency qubits in which the adiabaticity restrictions on gate speed are reduced. We characterize this coupler on a range of two-qubit devices achieving a maximum gate fidelity of $99.85\%$. We further show the calibration is stable over one day.
Efficient Long-Range Attention Network for Image Super-Resolution

Recently, transformer-based methods have demonstrated impressive results in various vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for feature extraction. However, the computation of SA in most existing transformer based models is very expensive, while some employed operations may be redundant for the SR task. This limits the range of SA computation and consequently limits the SR performance. In this work, we propose an efficient long-range attention network (ELAN) for image SR. Specifically, we first employ shift convolution (shift-conv) to effectively extract the image local structural information while maintaining the same level of complexity as 1 $$\,\times \,$$ 1 convolution, then propose a group-wise multi-scale self-attention (GMSA) module, which calculates SA on non-overlapped groups of features using different window sizes to exploit the long-range image dependency. A highly efficient long-range attention block (ELAB) is then built by simply cascading two shift-conv with a GMSA module, which is further accelerated by using a shared attention mechanism. Without bells and whistles, our ELAN follows a fairly simple design by sequentially cascading the ELABs. Extensive experiments demonstrate that ELAN obtains even better results against the transformer-based SR models but with significantly less complexity. The source codes of ELAN can be found at https://github.com/xindongzhang/ELAN.
A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell

At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfactory data collection is relatively cumbersome, and it is more difficult to collect labeled data. However, in many scenarios, to use a small amount of labeled data to train a good-performing classifier, it is not feasible to rely only on supervised learning algorithms, but semi-supervised learning algorithms can better cope with only a small amount of labeled data and a large amount of unlabeled data. This study combines the new weighted kernel with SKELM and proposes a semi-supervised extreme learning machine algorithm based on the weighted kernel, SELMWK. The experimental results show that the proposed SELMWK algorithm has good classification performance and can solve the semi-supervised gas classification task of the same domain data well on the used dataset.
Interval type-2 fuzzy tracking control for nonlinear systems via sampled-data controller

This paper addresses an interval type-2 (IT2) fuzzy sampled-data tracking control problem of nonlinear systems subject to uncertain parameters. The controlled plant is modeled as Takagi–Sugeno (T–S) fuzzy structure, where the parameter uncertainties are captured effectively by the lower and upper membership functions. By means of the linear matrix inequalities (LMIs) and free-weighting matrices, an IT2 fuzzy sampled-data control scheme is developed such that the system states are driven to track those of the reference model. Finally, an inverted pendulum system and a mass–spring–damper mechanical system are used to show the efficiency of the suggested tracking control design.
Geostatistical analyses and co-occurrence correlations of heavy metals distribution with various types of land use within a watershed in eastern QingHai-Tibet Plateau, China

Due to increasing anthropogenic disturbance, various pollutants have been frequently detected in QingHai-Tibet Plateau, where the environment is extremely fragile and sensitive to human activities. In this study, 51 sampling sites with different land covers were selected in a sub-basin in the Huangshui watershed in eastern QingHai-Tibet Plateau. A total of 102 soil samples were collected at surface (0–20 cm) and subsurface (20–40 cm) depths for studying on heavy metal pollution in this region. The relationships between land cover types and spatial distribution of five heavy metals Cr, Cu, Cd, Pb, and Zn were systematically analyzed combing field survey, remote-sensing database, and geostatistical methods. The relationships between soil physiochemical properties and heavy metals were assessed by geostatistics, co-occurrence network analysis and geographically weighted regression (GWR) model. The results indicated that the average concentrations of all five heavy metals in soil were higher than the local background values, indicating the influence of anthropogenic inputs of these elements into environment. Strong positive correlations were observed between some heavy metals and soil physic-chemical properties in different land covers, combined with the geoaccumulation index, human activities contribution can be interpolated. The Cr and Zn in surface soils are explainers for Cr and Zn in subsurface soils in the GWR models respectively, as well as the area of different land cover. Cd contamination was specifically analyzed due to its degree of contamination and then the sources of Cd were identified such as energy production as well as chemical fertilizers. The results of this study can provide basic data for long-term accumulation of heavy metals in soils under different land uses in QingHai-Tibet Plateau.
One-for-All: Grouped Variation Network-Based Fractional Interpolation in Video Coding

Fractional interpolation is used to provide sub-pixel level references for motion compensation in the interprediction of video coding, which attempts to remove temporal redundancy in video sequences. Traditional handcrafted fractional interpolation filters face the challenge of modeling discontinuous regions in videos, while existing deep learning-based methods are either designed for a single quantization parameter (QP), only generating half-pixel samples, or need to train a model for each sub-pixel position. In this paper, we present a one-for-all fractional interpolation method based on a grouped variation convolutional neural network (GVCNN). Our method can deal with video frames coded using different QPs and is capable of generating all sub-pixel positions at one sub-pixel level. Also, by predicting variations between integer-position pixels and sub-pixels, our network offers more expressive power. Moreover, we perform specific measurements in training data generation to simulate practical situations in video coding, including blurring the down-sampled sub-pixel samples to avoid aliasing effects and coding integer pixels to simulate reconstruction errors. In addition, we analyze the impact of the size of blur kernels theoretically. Experimental results verify the efficiency of GVCNN. Compared with HEVC, our method achieves 2.2% in bit saving on average and up to 5.2% under low-delay P configuration.
Krotov: A Python implementation of Krotov's method for quantum optimal control

We present a new open-source Python package, krotov, implementing the quantum optimal control method of that name. It allows to determine time-dependent external fields for a wide range of quantum control problems, including state-to-state transfer, quantum gate implementation and optimization towards an arbitrary perfect entangler. Krotov's method compares to other gradient-based optimization methods such as gradient-ascent and guarantees monotonic convergence for approximately time-continuous control fields. The user-friendly interface allows for combination with other Python packages, and thus high-level customization. The package is being developed at https://github.com/qucontrol/krotov
A Novel Cluster Validity Index Based on Local Cores

It is critical to evaluate the quality of clusters for most cluster analysis. A number of cluster validity indexes have been proposed, such as the Silhouette and Davies-Bouldin indexes. However, these validity indexes cannot be used to process clusters with arbitrary shapes. Some researchers employ graph-based distance to cluster nonspherical data sets, but the computation of graph-based distances between all pairs of points in a data set is time-consuming. A potential solution is to select some representative points. Inspired by this idea, we propose a novel Local Cores-based Cluster Validity (LCCV) index to improve the performance of Silhouette index. Local cores, with local maximum density, are selected as representative points. Since graph-based distance is used to evaluate the dissimilarity between local cores, the LCCV index is effective for obtaining the optimal cluster number for data sets containing clusters with arbitrary shapes. Moreover, a hierarchical clustering algorithm based on the LCCV index is proposed. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.
Unconditional Security of Sending or Not Sending Twin-Field Quantum Key Distribution with Finite Pulses

For encrypted communication, the ``sending or not sending'' protocol of twin-field quantum key distribution has the advantages of proven unconditional security against any coherent attack, and fault tolerance to large misalignment error. The existing security proof is based on infinite key length, however. Here the authors consider the complete finite-key effects for this protocol under the universally composable framework. Numerical simulation shows that in practice this scheme can exceed a secure distance of 500 km for a typical, finite number of pulses, even with large misalignment error. The results of this work could be directly applied in experiment.
FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter

This paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter.We participated in the subtask A for English and ranked first in the evaluation on the test set.We evaluate the quality of multiple sentence embeddings and explore multiple training models to evaluate the performance of simple yet effective embedding-ML combination algorithms.Our team -Fermi's model achieved an accuracy of 65.00% for English language in task A. Our models, which use pretrained Universal Encoder sentence embeddings for transforming the input and SVM (with RBF kernel) for classification, scored first position (among 68) in the leaderboard on the test set for Subtask A in English language.In this paper we provide a detailed description of the approach, as well as the results obtained in the task.
Unblocking the chain – findings from an executive workshop on blockchain in the supply chain

Purpose There is a lot of interest in blockchain in the supply chain and several papers call it a disruptive technology. Existing research, however, is mostly conceptual and focused on use-case development and early pilots. This paper aims to report the findings from a workshop with managers aimed at empirically exploring what adoption rates and focus areas are for blockchain in the supply chain, what drives blockchain in the supply chain applications and what barriers are to the implementation of blockchain in the supply chain. Design/methodology/approach A workshop with managers was organized to empirically explore blockchain adoption levels and focus areas in the supply chain, as well as drivers and barriers of implementation. Findings Workshop participants reported that adoption of blockchain in the supply chain today is very limited but actively considered by many. Drivers for this consideration include achieving greater transparency and visibility, as well as, improving processes and reducing costs. Participants identify many barriers, including a lack of understanding of costs and benefits of blockchain in the supply chain. Interestingly, participants report less concern about the feasibility of the technology implying managerial consideration if progressing beyond the technology and into the potential adoption of it. As a result, participants may be moving beyond the hype surrounding blockchain and giving consideration to the many remaining questions. A working technology does not yet mean that there is a feasible supply chain adoption. As a result, it may be too early to tell whether blockchain will be a disruptive technology. This paper identifies several fruitful areas for further consideration by management and in research. Originality/value As there is little empirical research on blockchain in the supply chain, this paper moves beyond use-case development and the exploration of pilot cases and studies how companies may consider supply chain adoption beyond the pilot and the early development of blockchain. Although only offering an initial exploration, this paper uncovers progress being reported in industry and many areas where further consideration and research can help advance thinking and practice.
Learning Reward Functions by Integrating Human Demonstrations and Preferences

Our goal is to accurately and efficiently learn reward functions for autonomous robots.Current approaches to this problem include inverse reinforcement learning (IRL), which uses expert demonstrations, and preference-based learning, which iteratively queries the user for her preferences between trajectories.In robotics however, IRL often struggles because it is difficult to get high-quality demonstrations; conversely, preference-based learning is very inefficient since it attempts to learn a continuous, high-dimensional function from binary feedback.We propose a new framework for reward learning, DemPref, that uses both demonstrations and preference queries to learn a reward function.Specifically, we (1) use the demonstrations to learn a coarse prior over the space of reward functions, to reduce the effective size of the space from which queries are generated; and (2) use the demonstrations to ground the (active) query generation process, to improve the quality of the generated queries.Our method alleviates the efficiency issues faced by standard preference-based learning methods and does not exclusively depend on (possibly low-quality) demonstrations.In numerical experiments, we find that DemPref is significantly more efficient than a standard active preference-based learning method.In a user study, we compare our method to a standard IRL method; we find that users rated the robot trained with DemPref as being more successful at learning their desired behavior, and preferred to use the DemPref system (over IRL) to train the robot.
Tree-Structured Policy Based Progressive Reinforcement Learning for Temporally Language Grounding in Video

Temporally language grounding in untrimmed videos is a newly-raised task in video understanding. Most of the existing methods suffer from inferior efficiency, lacking interpretability, and deviating from the human perception mechanism. Inspired by human's coarse-to-fine decision-making paradigm, we formulate a novel Tree-Structured Policy based Progressive Reinforcement Learning (TSP-PRL) framework to sequentially regulate the temporal boundary by an iterative refinement process. The semantic concepts are explicitly represented as the branches in the policy, which contributes to efficiently decomposing complex policies into an interpretable primitive action. Progressive reinforcement learning provides correct credit assignment via two task-oriented rewards that encourage mutual promotion within the tree-structured policy. We extensively evaluate TSP-PRL on the Charades-STA and ActivityNet datasets, and experimental results show that TSP-PRL achieves competitive performance over existing state-of-the-art methods.
High-speed Gaussian-modulated continuous-variable quantum key distribution with a local local oscillator based on pilot-tone-assisted phase compensation

A high-speed Gaussian-modulated continuous-variable quantum key distribution (CVQKD) with a local local oscillator (LLO) is experimentally demonstrated based on pilot-tone-assisted phase compensation. In the proposed scheme, the frequency-multiplexing and polarization-multiplexing techniques are used for the separate transmission and heterodyne detection between quantum signal and pilot tone, guaranteeing no crosstalk from strong pilot tone to weak quantum signal and different detection requirements of low-noise for quantum signal and high-saturation limitation for pilot tone. Moreover, compared with the conventional CVQKD based on homodyne detection, the proposed LLO-CVQKD scheme can measure X and P quadrature simultaneously using heterodyne detection without need of extra random basis selection. Besides, the phase noise, which contains the fast-drift phase noise due to the relative phase of two independent lasers and the slow-drift phase noise introduced by quantum channel disturbance, has been compensated experimentally in real time, so that a low level of excess noise with a 25 km optical fiber channel (with 5 dB loss) is obtained for the achievable secure key rate of 7.04 Mbps in the asymptotic regime and 1.85 Mbps under the finite-size block of 107.
Understanding coordination in global software engineering: A mixed-methods study on the use of meetings and Slack

Given the relevance of coordination in the field of global software engineering, this work was carried out to further understand coordination mechanisms. Specifically, we investigated meetings and the collaboration tool Slack. We conducted a longitudinal case study using a mixed-methods approach with surveys, observations, interviews, and chat logs. Our quantitative results show that employees in global projects spend 7 h 45 min per week on average in scheduled meetings and 8 h 54 min in unscheduled meetings. Furthermore, distributed teams were significantly larger than co-located teams, and people working in distributed teams spent somewhat more time in meetings per day. We found that low availability of key people, absence of organizational support for unscheduled meetings and unbalanced activity from team members in meetings and on Slack were barriers for effective coordination across sites. The positive aspects of using collaboration tools in distributed teams were increased team awareness and informal communication and reduced the need for e-mail. Our study emphasizes the importance of reflecting on how global software engineering teams use meetings and collaboration tools to coordinate. We provide practical advice for conducting better meetings and give suggestions for more efficient use of collaboration tools in global projects.
A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.
‘It is no longer scary’: digital learning before and during the Covid-19 pandemic in Irish secondary schools

The use of digital technology to support teaching and learning in schools has been rising for years, but in March 2020, it became the only option when the Covid-19 pandemic resulted in the closure of almost all educational institutions worldwide. This article reports on a survey of secondary school leaders (n = 72) in Ireland, conducted three months after the closures. Leaders' beliefs about technology, digital practices before the pandemic and responses to the emergency are considered. The findings suggest that leaders are positively disposed towards technology, and that, prior to the crisis, approaches to digital learning were aligned with some best practice recommendations. Although schools endeavoured to continue provision during the closures, challenges were reported, particularly in rural schools and those serving disadvantaged cohorts. Leaders perceived teachers' 'digital competence' as an area in need of development, and noted that the pandemic may have provided an impetus for this.
Enhancing Factual Consistency of Abstractive Summarization

Automatic abstractive summaries are found to often distort or fabricate facts in the article.This inconsistency between summary and original text has seriously impacted its applicability.We propose a fact-aware summarization model FASUM to extract and integrate factual relations into the summary generation process via graph attention.We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems.Empirical results 1 show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.