Reviewerly/exHarmony huggingface dataset:
A Fuzzy Cognitive Map method for integrated and participatory water governance and indicators affecting drinking water supplies

Drinking water governance is challenging with different perceptions and priorities among stakeholders in different countries. To make provision for drinking water protection in agricultural areas, governance systems need to be mapped for bottlenecks to be identified and solutions highlighted. To address this a system thinking approach was used in an explanatory network analysis of Fuzzy Cognitive Maps (FCM) that were created during face to face interviews with stakeholder representative groups (individuals, policy developers, researchers, and regulators). Two exercises were designed and facilitated to obtain stakeholder maps on A) the water governance framework from stakeholders' own perspective with a ranking of actors in terms of their perceived importance and B) a list of importance factors and how these were connected for the provision of good drinking water quality supplies in agricultural areas. Causal relationships were subsequently drawn around each subject allowing mapping. A graph theory Hierarchy Index (h) approach examined if stakeholder groups preferred top down hierarchical governance or a more inclusive democratic governance approach. Finally, an auto-associative neural network method was deployed on group maps for examination during steady-state conditions for three scenarios to be explored i.e. changing “Farmers knowledge”, “best management practice (BMP) uptake” and “Farmers behaviour and belief” to the highest level of influence and seeing how the system reacted. Results of Exercise A showed that all stakeholder representative groups had a different perception of the water governance framework. Most stakeholder groups had a democratic point of view regarding water governance structures and the ranking and importance of the actors within the framework. Results of Exercise B demonstrated that most of the groups have similar opinions regarding the highest ranked factors affecting drinking water quality and the possible environmental ecological policy options. In this second exercise, only one representative group showed a democratic outlook whereas all others had a hierarchal outlook. Scenario testing of policy options enabled bottlenecks and possible solutions to be identified. By boosting “Farmers behaviour and belief” to the highest possible level, resulted in a large increase in other factors – a scenario where farmers could benefit from the outcome. This would be achieved by enhancing farmers' willingness and intention to participate and implement BMPs. Better results would be achieved if farmers believed in the method and could benefit from the outcome. Also keeping “Farmers knowledge” at the highest point had a positive influence on the other factors. This can be achieved by enhancing farmers training and knowledge transfer by local and national actors. This method is widely applicable and should be considered for more integrated and participatory approaches to drinking water governance.
Progress in the Application of Machine Learning in Combustion Studies

Combustion is the main source of energy and environmental pollution.The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions.In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field.In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed.Combustion studies combined with ML can be divided into theoretical and industrial aspects.Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel.ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation.ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation.ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields.ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants.In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines.Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters.The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.
A Large-scale Simulation Dataset: Boost the Detection Accuracy for Special Weather Conditions

Object detection is a fundamental task for autonomous driving systems. One bottleneck hindering detection accuracy is a shortage of well-annotated image data. Virtual reality has provided a feasible low-cost way to facilitate computer vision related developments. In autonomous driving area, existing public datasets from real world generally have data biases and cannot represent a wide range of weather conditions, such as rainy or snowy roads. To address this challenge, we introduce a new large-scale simulation dataset which is generated by an automated pipeline from a high realism video game. Our dataset focuses on weather conditions, which can be adopted to train networks to effectively detect objects under such conditions. We use extensive experiments to evaluate our dataset by comparing it with public datasets. The experiment results show that networks trained with our dataset outperform the networks trained by other public datasets. Our work demonstrates the effectiveness of using simulation data to address real-world challenges in the practice of object detection.
Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning

In recommendation systems, the grey-sheep problem refers to users with unique preferences and tastes that make it difficult to develop accurate profiles. That is, the similarity search approach typically followed during the recommendation process fails to yield good results. Most research does not focus on such users and thus fails to cater to more exotic tastes and emerging trends, leading to a subsequent loss in revenue and marketing opportunities. One suggested solution is to use one-class classification to generate a prediction list for these users, where decision boundaries are learned that distinguish between normal and grey-sheep users. In this paper, we present the grey-sheep one-class recommendation (GSOR) framework designed to create accurate prediction models while taking both regular and grey-sheep users into account. In addition, we introduce a novel grey-sheep movie recommendation benchmark to be used by current and future researchers. When evaluating our GSOR framework against this benchmark, our results indicate the value of combining cluster analysis, outlier detection, and one-class learning to generate relevant and timely recommendation lists from data sets that contain grey-sheep users. Specifically, by employing one-class decision tree algorithms, our GSOR framework was able to outperform traditional collaborative filtering-based recommendation systems in both accuracy and model construction time. Furthermore, we report that having grey-sheep users in the system often had a positive impact on the learning and recommendation processes.
TOSCA4QC: Two Modeling Styles for TOSCA to Automate the Deployment and Orchestration of Quantum Applications

Quantum computing introduces a new computing paradigm that promises to solve problems that cannot be solved by classical computers efficiently. Thus, quantum applications will be more and more integrated in classical applications. To bring these composite applications into production, technologies for an automated deployment and orchestration are required to avoid manual error-prone and time-consuming processes. For non-quantum applications, a variety of deployment technologies have been developed in recent years. However, the deployment of quantum applications currently differs significantly from non-quantum applications and thus, leads to a different modeling procedure for the deployment of quantum applications. To overcome these problems, we propose TOSCA4QC that introduces two deployment modeling styles based on the Topology and Orchestration Specification for Cloud Applications (TOSCA) standard for automating the deployment and orchestration of quantum applications: (i) SDK-specific modeling style to cover all technical deployment details and (ii) SDK-agnostic modeling style supporting common modeling principles. We further show how existing model-driven development (MDD) approach can be applied to refine a SDK-agnostic model to an executable SDK-specific model. We demonstrate the practical feasibility by a prototypical implementation as an extension of the TOSCA ecosystem OpenTOSCA and three case studies with IBMQ and a quantum simulator.
Identifying Elements Essential for BERT’s Multilinguality

It has been shown that multilingual BERT (mBERT) yields high quality multilingual representations and enables effective zero-shot transfer. This is surprising given that mBERT does not use any crosslingual signal during training. While recent literature has studied this phenomenon, the reasons for the multilinguality are still somewhat obscure. We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual. To allow for fast experimentation we propose an efficient setup with small BERT models trained on a mix of synthetic and natural data. Overall, we identify four architectural and two linguistic elements that influence multilinguality. Based on our insights, we experiment with a multilingual pretraining setup that modifies the masking strategy using VecMap, i.e., unsupervised embedding alignment. Experiments on XNLI with three languages indicate that our findings transfer from our small setup to larger scale settings.
Traceable and Controllable Encrypted Cloud Image Search in Multi-User Settings

With the advent of cloud computing, explosively increasing images are gradually outsourced to the cloud server for costs saving and feasibility. For security and privacy concerns, images (e.g., medical diagnosis, personal photos) should be encrypted before being outsourced. However, traditional encrypted image retrieval techniques still suffer from costly access control and low search accuracy. To solve these challenging issues, in this article, we first propose a Controllable encrypted cloud image Search scheme in Multi-user settings (namely CSM) by using the polynomial-based access strategy and proxy re-encryption technique. CSM achieves efficient access control and avoids heavy communication overhead caused by key transmission. Then, we improve the basic CSM to achieve malicious search user Tracing (namely TCSM) by utilizing the watermark technique, which can further prevent search users from illegally redistributing retrieved images to unauthorized search users. Our formal security analysis proves that our CSM (or TCSM) can guarantee the privacy of images, indexes, and search queries. Our empirical experiments using real-world datasets demonstrate the efficiency and high accuracy of our CSM (or TCSM) in practice.
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

We present BlockGAN, an image generative model that learns object-aware 3D scene representations directly from unlabelled 2D images. Current work on scene representation learning either ignores scene background or treats the whole scene as one object. Meanwhile, work that considers scene compositionality treats scene objects only as image patches or 2D layers with alpha maps. Inspired by the computer graphics pipeline, we design BlockGAN to learn to first generate 3D features of background and foreground objects, then combine them into 3D features for the wholes cene, and finally render them into realistic images. This allows BlockGAN to reason over occlusion and interaction between objects' appearance, such as shadow and lighting, and provides control over each object's 3D pose and identity, while maintaining image realism. BlockGAN is trained end-to-end, using only unlabelled single images, without the need for 3D geometry, pose labels, object masks, or multiple views of the same scene. Our experiments show that using explicit 3D features to represent objects allows BlockGAN to learn disentangled representations both in terms of objects (foreground and background) and their properties (pose and identity).
Domain Robustness in Neural Machine Translation

Translating text that diverges from the training domain is a key challenge for machine translation. Domain robustnes - the generalization of models to unseen test domains - is low for both statistical (SMT) and neural machine translation (NMT). In this paper, we study the performance of SMT and NMT models on out-of-domain test sets. We find that in unknown domains, SMT and NMT suffer from very different problems: SMT systems are mostly adequate but not fluent, while NMT systems are mostly fluent, but not adequate. For NMT, we identify such hallucinations (translations that are fluent but unrelated to the source) as a key reason for low domain robustness. To mitigate this problem, we empirically compare methods that are reported to improve adequacy or in-domain robustness in terms of their effectiveness at improving domain robustness. In experiments on German→English OPUS data, and German→Romansh (a low-resource setting) we find that several methods improve domain robustness. While thosemethods do lead to higher BLEU scores overall, they only slightly increase the adequacy of translations compared to SMT
Chain-Based Covert Data Embedding Schemes in Blockchain

The quality of covert communications is determined by the choice of communication channels and the design of data embedding schemes. Recently, the Bitcoin system is prevalent as a covert communication channel. The consensus mechanism requires participants to spread their found valid blocks under an adjustable difficulty, which provides a stable periodic broadcast channel. Moreover, senders and receivers are difficult to be traced, because the Bitcoin system is pseudonymous. However, since the historical data in the ledger cannot be removed from the Bitcoin system, the openness and the persistent storage of the ledger in the Bitcoin system post new challenges when designing data embedding schemes. More concreteness, most traditional data embedding schemes either design by heuristic or empirical algorithms or use a fixed field to embed data in the transactions. Therefore, the covert data can be recognized once the algorithm is leaked or the pattern is explored. In this article, we first propose a hash chain-based covert data embedding (HC-CDE) scheme. The embedded transactions are difficult to be discovered. We further propose an elliptic curve Diffie–Hellman chain-based covert data embedding (ECDHC-CDE) scheme to enhance the security of the HC-CDE scheme. Experimental analysis on the Bitcoin Testnet verifies the security and the efficiency of the proposed schemes.
Interactive Video Object Segmentation Using Global and Local Transfer Modules

An interactive video object segmentation algorithm, which takes scribble annotations on query objects as input, is proposed in this paper. We develop a deep neural network, which consists of the annotation network (A-Net) and the transfer network (T-Net). First, given user scribbles on a frame, A-Net yields a segmentation result based on the encoder-decoder architecture. Second, T-Net transfers the segmentation result bidirectionally to the other frames, by employing the global and local transfer modules. The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame. By applying A-Net and T-Net alternately, a user can obtain desired segmentation results with minimal efforts. We train the entire network in two stages, by emulating user scribbles and employing an auxiliary loss. Experimental results demonstrate that the proposed interactive video object segmentation algorithm outperforms the state-of-the-art conventional algorithms. Codes and models are available at https://github.com/yuk6heo/IVOS-ATNet .
Mathematics Teachers’ Perceptions of the Introduction of ICT: The Relationship between Motivation and Use in the Teaching Function

Digital self-efficacy and the amount of perceived support from the school can improve teachers’ motivation to increase the use of information and communication technology (ICT) in the classroom. Likewise, attitude, perception, gender, and experience of mathematics teachers are factors that influence their use of ICT. This study aimed to analyze the profiles of mathematics teachers, determine the existence of differences between them, and identify the sample size necessary to detect significant differences. A total of 73 high school teachers were included in this cross-sectional study. Teaching practice, ICT resources, ICT in the classroom, skills, and uses of ICT were assessed through a validated 19-item questionnaire. Statistical analysis revealed that the required sample to detect significant differences was 53 subjects. Further, 67.21% of the mathematics teachers surveyed in Melilla were younger than 40 years of age, and 62.30% had less than 6 years of teaching experience. In addition, 81.97 and 47.54% of mathematics teachers stated that they consider themselves to have sufficient ICT resources at home for their work and in the classroom, respectively. Through different clusters, mathematics teachers can be identified and classified according to their motivational and competence profiles in pedagogical and digital areas. In addition, young teachers with some teaching experience had positive perceptions of technology, as reflected by high scores in the motivation indicator for ICT.
DaN+: Danish Nested Named Entities and Lexical Normalization

This paper introduces DAN+, a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language.We empirically assess three strategies to model the two-layer Named Entity Recognition (NER) task.We compare transfer capabilities from German versus in-language annotation from scratch.We examine language-specific versus multilingual BERT, and study the effect of lexical normalization on NER.Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexical normalization are the most beneficial on the least canonical data.Our results also show that an out-of-domain setup remains challenging, while performance on news plateaus quickly.This highlights the importance of cross-domain evaluation of cross-lingual transfer.
AdaSpeech: Adaptive Text to Speech for Custom Voice

Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech from her/him. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model needs to handle diverse acoustic conditions which could be very different from source speech data, and 2) to support a large number of customers, the adaptation parameters need to be small enough for each target speaker to reduce memory usage while maintaining high voice quality. In this work, we propose AdaSpeech, an adaptive TTS system for high-quality and efficient customization of new voices. We design several techniques in AdaSpeech to address the two challenges in custom voice: 1) To handle different acoustic conditions, we model the acoustic information in both utterance and phoneme level. Specifically, we use one acoustic encoder to extract an utterance-level vector and another one to extract a sequence of phoneme-level vectors from the target speech during pre-training and fine-tuning; in inference, we extract the utterance-level vector from a reference speech and use an acoustic predictor to predict the phoneme-level vectors. 2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation. We pre-train the source TTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets (with different acoustic conditions from LibriTTS) with few adaptation data, e.g., 20 sentences, about 1 minute speech. Experiment results show that AdaSpeech achieves much better adaptation quality than baseline methods, with only about 5K specific parameters for each speaker, which demonstrates its effectiveness for custom voice. The audio samples are available at https://speechresearch.github.io/adaspeech/.
Lifetime Division Multiplexing by Multilevel Encryption Algorithm

Asymmetric, multilevel, switchable, and reversible encryption is realized by algorithm encryption, which plays an important role in encryption technology. Fluorescence lifetime encryption is currently not executed by an algorithm. It is well-known that the short fluorescence lifetime (τ1), long fluorescence lifetime (τ2), amplitude-weighted average fluorescence lifetime (τm), and intensity-weighted average fluorescence lifetime (τi) can be obtained using a double exponential fitting, and then these four lifetime parameters can be considered as four lifetime algorithms. Therefore, we propose that the acquisition of these four fluorescence lifetimes can be regarded as further dividing the lifetime by different algorithms and optimizing lifetime multiplexing. Moreover, the four lifetime algorithms of τ1, τm, τ2, and τi can be switched between each other and can be used to perform asymmetric, multilevel, and reversible lifetime encryption to effectively increase the difficulties of anticounterfeiting.
MARN: Multi-Scale Attention Retinex Network for Low-Light Image Enhancement

Images captured in low-light conditions often suffer from bad visibility, e.g., low contrast, lost details, and color distortion, and image enhancement methods can be used to improve the image quality.Previous methods have generally obtained a smooth illumination map to enhance the image but have ignored details, leading to inaccurate illumination estimations.To solve this problem, we propose a multi-scale attention retinex network (MARN) for low-light image enhancement, which learns an image-to-illumination mapping to obtain a detailed inverse illumination map inspired by retinex theory.In order to introduce more image priors, we introduce a novel illuminance-attention map to guide the model to characterize varying-lighting areas, which we combine with the low-light image as the model input.MARN consists of a multi-scale attention module and a feature fusion module; the former extracts multi-resolution features with attention-based feature aggregation, while the latter further merges the output features of the previous module with the input.To achieve better visibility, we formulate a novel loss function to synthetically measure the illumination, detail, and colorfulness of the image.Extensive experiments are performed on several benchmark datasets.The results demonstrate that our method outperforms other state-of-the-art methods according to both objective and subjective metrics.