Reviewerly/exHarmony huggingface dataset:
Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE programme

The Defense Advanced Research Projects Agency (DARPA) programme 'Systematizing Confidence in Open Research and Evidence' (SCORE) aims to generate confidence scores for a large number of research claims from empirical studies in the social and behavioural sciences. The confidence scores will provide a quantitative assessment of how likely a claim will hold up in an independent replication. To create the scores, we follow earlier approaches and use prediction markets and surveys to forecast replication outcomes. Based on an initial set of forecasts for the overall replication rate in SCORE and its dependence on the academic discipline and the time of publication, we show that participants expect replication rates to increase over time. Moreover, they expect replication rates to differ between fields, with the highest replication rate in economics (average survey response 58%), and the lowest in psychology and in education (average survey response of 42% for both fields). These results reveal insights into the academic community's views of the replication crisis, including for research fields for which no large-scale replication studies have been undertaken yet.
Parallel discrete lion swarm optimization algorithm for solving traveling salesman problem

As a typical representative of the NP-complete problem, the traveling salesman problem (TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization (DLSO) algorithm is proposed to solve the TSP Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt (C2-opt) algorithm to enhance the local search ability. Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization (PDLSO) algorithm is proposed. The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.
Attention Based Multi-Layer Fusion of Multispectral Images for Pedestrian Detection

Multispectral images are increasingly used for pedestrian detection. Preliminary fusion strategies would fail to exploit informative features from cross-spectral images, or worse, may introduce additional interference. In this paper, we propose an attention based multi-layer fusion network in the triple-stream deep convolutional neural network architecture for multispectral pedestrian detection. The effectiveness of multi-layer fusion is examined and verified in this work. Furthermore, a channel-wise attention module (CAM) and a spatial-wise attention module (SAM) are developed and incorporated into the network aiming at more subtle adjustment to weights of multispectral features along both the channel and spatial dimensions respectively. Channel-wise attention is trained with self-supervision while spatial-wise attention is trained with external supervision as we remodel its learning process as saliency detection. Both attention-based weighting mechanisms are evaluated separately and then sequentially. Experimental results on the KAIST dataset show that the proposed multi-layer cross-spectral fusion R-CNN (CS-RCNN), with spatial-wise weighting applied alone, achieves state-of-the-art performance on all-day detection while outperforming compared methods at nighttime.
Promoting Intercultural Competence in a Learning Activity Supported by Virtual Reality Technology

Virtual reality (VR) technology makes it possible to create an authentic virtual environment that benefits immersive learning. We designed an intercultural learning activity and applied VR technology to support it. Then, we investigated students’ perceptions of the learning activity, VR technology, and intercultural competence (IC) development during learning. Students from China and Uzbekistan participated in the activity, in which a pragmatic mixed-methods approach was used. The data were collected through student reports, three questionnaires, and interviews, and then analyzed. Three main findings were obtained. First, 13 items related to perception of the learning activity were revealed. When compared with earlier studies, new items were found, including presence, immersion, and authentic cultural experience. Second, the results showed that the participants intended to continue using VR technology, were satisfied with intercultural learning supported by VR technology, and felt that the technology confirmed their expectations. Third, the results showed that intercultural learning supported by VR technology helped facilitate IC development. Based on these results, we discuss implications and offer suggestions for educators and researchers.
How Blockchain Enhances Supply Chain Management: A Survey

Providing transparency and trust among participants and stakeholders and ensuring an efficient operation are current supply chain challenges. These challenges are difficult to resolve because the records of supply chains may be exposed to alterations by participants. Blockchain technology has been identified as a promising solution to resolve these challenges. In this paper, we introduce blockchain and survey recent blockchain frameworks that address some of the supply chain challenges. We describe the components and operation of these blockchain frameworks. We identify the objectives and motivation in each of the surveyed use cases and highlight the advantages and disadvantages of each adopted framework. We analyze how the reported blockchain frameworks address different supply chain challenges. We present a comparative summary of existing literature on blockchain for supply chain. We also summarize the properties of a blockchain framework for its successful adoption in future supply chains and discuss several remaining challenges and opportunities.
A Dynamic Frame Selection Framework for Fast Video Recognition

We introduce AdaFrame, a conditional computation framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame, which contains a Long Short-Term Memory augmented with a global memory to provide context information, operates as an agent to interact with video sequences aiming to search over time which frames to use. Trained with policy search methods, at each time step, AdaFrame computes a prediction, decides where to observe next, and estimates a utility, i.e., expected future rewards, of viewing more frames in the future. Exploring predicted utilities at testing time, AdaFrame is able to achieve adaptive lookahead inference so as to minimize the overall computational cost without incurring a degradation in accuracy. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet. With a vanilla ResNet-101 model, AdaFrame achieves similar performance of using all frames while only requiring, on average, 8.21 and 8.65 frames on FCVID and ActivityNet, respectively. We also demonstrate AdaFrame is compatible with modern 2D and 3D networks for video recognition. Furthermore, we show, among other things, learned frame usage can reflect the difficulty of making prediction decisions both at instance-level within the same class and at class-level among different categories.
MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

We contribute MobileVisFixer, a new method to make visualizations more mobile-friendly. Although mobile devices have become the primary means of accessing information on the web, many existing visualizations are not optimized for small screens and can lead to a frustrating user experience. Currently, practitioners and researchers have to engage in a tedious and time-consuming process to ensure that their designs scale to screens of different sizes, and existing toolkits and libraries provide little support in diagnosing and repairing issues. To address this challenge, MobileVisFixer automates a mobile-friendly visualization re-design process with a novel reinforcement learning framework. To inform the design of MobileVisFixer, we first collected and analyzed SVG-based visualizations on the web, and identified five common mobile-friendly issues. MobileVisFixer addresses four of these issues on single-view Cartesian visualizations with linear or discrete scales by a Markov Decision Process model that is both generalizable across various visualizations and fully explainable. MobileVisFixer deconstructs charts into declarative formats, and uses a greedy heuristic based on Policy Gradient methods to find solutions to this difficult, multi-criteria optimization problem in reasonable time. In addition, MobileVisFixer can be easily extended with the incorporation of optimization algorithms for data visualizations. Quantitative evaluation on two real-world datasets demonstrates the effectiveness and generalizability of our method.
Real-Time Ultrasound Image Despeckling Using Mixed-Attention Mechanism Based Residual UNet

Ultrasound imaging has been widely used for clinical diagnosis. However, the inherent speckle noise will degrade the quality of ultrasound images. Existing despeckling methods cannot deliver sufficient speckle reduction and preserve image details well at high noise corruption and they cannot realize real-time ultrasound image denoising. With the popularity of deep learning, supervised learning for image denoising has recently attracted considerable attention. In this paper, we have proposed a novel residual UNet using mixed-attention mechanism (MARU) for real-time ultrasound image despeckling. In view of the signal-dependent characteristics of speckle noise, we have designed an encoder-decoder network to reconstruct the despeckled image by extracting features from the noisy image. Furthermore, a lightweight mixed-attention block is proposed to effectively enhance the image features and suppress some speckle noise during the encoding phase by using separation and re-fusion strategy for channel and spatial attention. Besides, we have graded the speckle noise levels with a certain interval and designed an algorithm to estimate the noise levels for despeckling real ultrasound images. Experiments have been done on the natural images, the synthetic image, the image simulated using Field II and the real ultrasound images. Compared with existing despeckling methods, the proposed network has achieved the state-of-the-art despeckling performance in terms of subjective human vision and such quantitative indexes as peak signal to noise ratio (PSNR), structural similarity (SSIM), equivalent number of looks (ENL) and contrast-to-noise ratio (CNR).
Finding spin glass ground states using quantum walks

Abstract Quantum computation using continuous-time evolution under a natural hardware Hamiltonian is a promising near- and mid-term direction toward powerful quantum computing hardware. We investigate the performance of continuous-time quantum walks as a tool for finding spin glass ground states, a problem that serves as a useful model for realistic optimization problems. By performing detailed numerics, we uncover significant ways in which solving spin glass problems differs from applying quantum walks to the search problem. Importantly, unlike for the search problem, parameters such as the hopping rate of the quantum walk do not need to be set precisely for the spin glass ground state problem. Heuristic values of the hopping rate determined from the energy scales in the problem Hamiltonian are sufficient for obtaining a better quantum advantage than for search. We uncover two general mechanisms that provide the quantum advantage: matching the driver Hamiltonian to the encoding in the problem Hamiltonian, and an energy redistribution principle that ensures a quantum walk will find a lower energy state in a short timescale. This makes it practical to use quantum walks for solving hard problems, and opens the door for a range of applications on suitable quantum hardware.
Thinking Like a Skeptic: Defeasible Inference in Natural Language

Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin).Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning.We introduce Defeasible NLI (abbreviated δ-NLI), a dataset for defeasible inference in natural language.δ-NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms.From δ-NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging.Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68% of the time.
Fortifying Toxic Speech Detectors Against Veiled Toxicity

Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons, or manifestations of implicit bias.Building a large annotated dataset for such veiled toxicity can be very expensive.In this work, we propose a framework aimed at fortifying existing toxic speech detectors without a large labeled corpus of veiled toxicity.Just a handful of probing examples are used to surface orders of magnitude more disguised offenses.We augment the toxic speech detector's training data with these discovered offensive examples, thereby making it more robust to veiled toxicity while preserving its utility in detecting overt toxicity. 1Warning: this paper contains examples that may be offensive or upsetting.
Dynamic Data Selection and Weighting for Iterative Back-Translation

Back-translation has proven to be an effective method to utilize monolingual data in neural machine translation (NMT), and iteratively conducting back-translation can further improve the model performance.Selecting which monolingual data to back-translate is crucial, as we require that the resulting synthetic data are of high quality and reflect the target domain.To achieve these two goals, data selection and weighting strategies have been proposed, with a common practice being to select samples close to the target domain but also dissimilar to the average general-domain text.In this paper, we provide insights into this commonly used approach and generalize it to a dynamic curriculum learning strategy, which is applied to iterative back-translation models.In addition, we propose weighting strategies based on both the current quality of the sentence and its improvement over the previous iteration.We evaluate our models on domain adaptation, low-resource, and high-resource MT settings and on two language pairs.Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines. 1
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction

Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer.However, their performance on Arabic information extraction (IE) tasks is not very well studied.In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning.We study Giga-BERT's effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction.Our best model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT (Antoun et al., 2020) in both the supervised and zero-shot transfer settings.
Image Anomaly Detection Using Normal Data Only by Latent Space Resampling

Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. Then, the deviation code will be resampled from the normal distribution and decoded to yield a restored image, which is closest to the anomaly input. The anomaly is then detected by comparing the difference between the restored image and the anomaly image. Our proposed method is evaluated on the high-resolution industrial inspection image datasets MVTec AD which consist of 15 categories. The results show that the AUROC of the model improves by 15% over autoencoder and also yields competitive performance compared with state-of-the-art methods.
Intelligent Smart Contracts for Innovative Supply Chain Management

We propose blockchains and smart contracts as enabling technologies for an innovative type of supply chain management, with the goal of achieving higher levels of collaboration between the companies participating in the chain, which in turn pays in the form of higher levels of profitability and economic health for the participating enterprises. Our proposal goes far beyond simply using blockchains as decentralized systems to track the origin and delivery of goods, which is what most of the current blockchain projects on supply chains are focused on. In fact, we introduce a type of smart contract aimed to solve two of the main problems that hinder the efficiency and effectiveness of supply chains, namely trust and coordination . Solving the problem of trust amounts to the capability of establishing quickly and cheaply contractual relationships based on convergent business needs among parties that may not know each other, and therefore need to protect themselves from opportunistic or incorrect behavior. Solving the problem of coordination consists in creating, at convenient management costs, a control system capable of directing the objectives of the supply chain as a whole, so as to achieve a greater common good in the medium term, as an alternative to the state of affairs in which each participant pursues, on its own behalf, lower but immediate returns. Our smart contracts for innovative supply chain management replace human coordinators in tackling the problems above, thus eliminating one major obstacle to their effective solution, namely the need to trust the coordinator itself. Furthermore, in this way, by automating the process of coordination, they unburden the supply chain of a considerable management cost. Contracts of this kind not only automate contract execution as in standard smart contracts, but also adjust costs and compensations of the members of a supply chain, effectively taking up the role that was of human coordinators. Thus, we refer to them as “intelligent smart contracts.” In the course of the paper, we will illustrate an innovative supply chain architecture based on intelligent smart contracts running on blockchain, we will detail the algorithmic methodologies underlying the decision-making process of these contracts and we will outline the wider socio-economic perspectives opened by our approach.
SpaceGroupIrep: A package for irreducible representations of space group

We have developed a Mathematica program package SpaceGroupIrep which is a database and tool set for irreducible representations (IRs) of space group in BC convention, i.e. the convention used in the famous book "The mathematical theory of symmetry in solids" by C.J. Bradley & A.P. Cracknell. Using this package, elements of any space group, little group, Herring little group, or central extension of little co-group can be easily obtained. This package can give not only little-group (LG) IRs for any k-point but also space-group (SG) IRs for any k-stars in intuitive table form, and both single-valued and double-valued IRs are supported. This package can calculate the decomposition of the direct product of SG IRs for any two k-stars. This package can determine the LG IRs of Bloch states in energy bands in BC convention and this works for any input primitive cell thanks to its ability to convert any input cell to a cell in BC convention. This package can also provide the correspondence of k-points and LG IR labels between BCS (Bilbao Crystallographic Server) and BC conventions. In a word, the package SpaceGroupIrep is very useful for both study and research, e.g. for analyzing band topology or determining selection rules. Program title: SpaceGroupIrep CPC Library link to program files: https://doi.org/10.17632/3vm4g32t4d.1 Developer's repository link: https://github.com/goodluck1982/SpaceGroupIrep Licensing provisions: GNU General Public Licence 3.0 Programming language: Mathematica External routines/libraries used: spglib (http://spglib.github.io/spglib) Nature of problem: Space groups and their representations are important mathematical language to describe symmetry in crystals. The book—"The mathematical theory of symmetry in solids" by C.J. Bradley & A.P. Cracknell (called the BC book)—is highly influential because it contains not only systematic theory but also detailed complete data of space groups and their representations. The package SpaceGroupIrep digitizes these data in the BC book and provides tens of functions to manipulate them, such as obtaining group elements and calculating their multiplications, identifying k-points, showing the character table of any little group, determining the little-group (LG) irreducible representations (IRs) of energy bands, and calculating the direct product of space-group (SG) IRs. This package is a useful database and tool set for space groups and their representations in BC convention. Solution method: The direct data in the BC book is used to calculate the LG IRs for standard k-points defined in the book. For a non-standard k-point, we first relate it to a standard k-point by an element which makes the space group self-conjugate and then calculate the LG IRs through the element. SG IRs are obtained by calculating the induced representations of the corresponding LG IRs. The full-group method based on double coset is used to calculate the direct products of SG IRs. In addition, an external package spglib is utilized to help convert any input cell to a cell in BC convention.