Reviewerly/exHarmony huggingface dataset:
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers

Recently, Convolutional Neural Network (CNN) has achieved excellent performance in the classification task. It is widely known that CNN is deemed as a 'blackbox', which is hard for understanding the prediction mechanism and debugging the wrong prediction. Some model debugging and explanation works are developed for solving the above drawbacks. However, those methods focus on explanation and diagnosing possible causes for model prediction, based on which the researchers handle the following optimization of models manually. In this paper, we propose the first completely automatic model diagnosing and treating tool, termed as Model Doctor. Based on two discoveries that 1) each category is only correlated with sparse and specific convolution kernels, and 2) adversarial samples are isolated while normal samples are successive in the feature space, a simple aggregate gradient constraint is devised for effectively diagnosing and optimizing CNN classifiers. The aggregate gradient strategy is a versatile module for mainstream CNN classifiers. Extensive experiments demonstrate that the proposed Model Doctor applies to all existing CNN classifiers, and improves the accuracy of 16 mainstream CNN classifiers by 1%~5%.
Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups is accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.
Participatory design and evaluation of virtual reality physical rehabilitation for people living with dementia

Abstract Emerging research confirms the need for technologically enhanced solutions to support non-pharmacological interventions which can improve the quality of life, the mental and physical health of demented people. Several types of research examined if virtual reality can be an effective solution. This paper aims to present the cyclic process of prototyping, testing, analysing, and refining the VR system in real-world clinical settings. Seven people with moderate to severe dementia were recruited. The experiment required the patients to attend three virtual reality iterations of rapid prototyping with user testing. All three iterations involved training activities with upper body movements similar to their usual physical training. A mixed-methods design measured affect and emotional behaviour using the Observed Emotion Rating Scale and the Visual Analog Scale. Content analysis was conducted following observations and interviews. During each iteration of rapid prototyping with user testing, quantitative measurements of performance, independence and time were recorded. Eye tracking and movement information were captured by the system. Finally, a simplified version of the presence and usability scales evaluated the system. The results of this study provide further evidence that virtual reality can play a significant role in the improvement of people’s with dementia physical training and emotional health when is appropriately designed. The results present the vital factors which should be incorporated in a virtual reality system which are: 1) a simple interactions modality; 2) visible visual targets and continuous feedback; 3) personalized virtual environments; 4) personalized range of movements.
Entailment Graph Learning with Textual Entailment and Soft Transitivity

Typed entailment graphs try to learn the entailment relations between predicates from text and model them as edges between predicate nodes.The construction of entailment graphs usually suffers from severe sparsity and unreliability of distributional similarity.We propose a two-stage method, Entailment Graph with Textual Entailment and Transitivity (EGT2).EGT2 learns local entailment relations by recognizing possible textual entailment between template sentences formed by typed CCG-parsed predicates.Based on the generated local graph, EGT2 then uses three novel soft transitivity constraints to consider the logical transitivity in entailment structures.Experiments on benchmark datasets show that EGT2 can well model the transitivity in entailment graph to alleviate the sparsity issue, and lead to significant improvement over current state-of-the-art methods 1 .
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation

Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available.Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks.Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks.To address this issue, we propose a memory imitation meta-learning (MemIML) method that enhances the model's reliance on support sets for task adaptation.Specifically, we introduce a task-specific memory module to store support set information and construct an imitation module to force query sets to imitate the behaviors of some representative supportset samples stored in the memory.A theoretical analysis is provided to prove the effectiveness of our method, and empirical results also demonstrate that our method outperforms competitive baselines on both text classification and generation tasks.
Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization

Multimodal summarization with multimodal output (MSMO) generates a summary with both textual and visual content. Multimodal news report contains heterogeneous contents, which makes MSMO nontrivial. Moreover, it is observed that different modalities of data in the news report correlate hierarchically. Traditional MSMO methods indistinguishably handle different modalities of data by learning a representation for the whole data, which is not directly adaptable to the heterogeneous contents and hierarchical correlation. In this paper, we propose a hierarchical cross-modality semantic correlation learning model (HCSCL) to learn the intra- and inter-modal correlation existing in the multimodal data. HCSCL adopts a graph network to encode the intra-modal correlation. Then, a hierarchical fusion framework is proposed to learn the hierarchical correlation between text and images. Furthermore, we construct a new dataset with relevant image annotation and image object label information to provide the supervision information for the learning procedure. Extensive experiments on the dataset show that HCSCL significantly outperforms the baseline methods in automatic summarization metrics and fine-grained diversity tests.
Improving collaborative filtering using lexicon-based sentiment analysis

&lt;span&gt;Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.&lt;/span&gt;
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation

Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples.In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge.Given an input text example, our DoCoGen algorithm generates a domain-counterfactual textual example (D-CON) -that is similar to the original in all aspects, including the task label, but its domain is changed to a desired one.Importantly, DoCoGen is trained using only unlabeled examples from multiple domainsno NLP task labels or parallel pairs of textual examples and their domain-counterfactuals are required.We show that DoCoGen can generate coherent counterfactuals consisting of multiple sentences.We use the D-CONs generated by DoCoGen to augment a sentiment classifier and a multi-label intent classifier in 20 and 78 DA setups, respectively, where source-domain labeled data is scarce.Our model outperforms strong baselines and improves the accuracy of a state-of-the-art unsupervised DA algorithm.
The reservoir learning power across quantum many-body localization transition

Harnessing the quantum computation power of the present noisy-intermediate-size-quantum devices has received tremendous interest in the last few years. Here we study the learning power of a one-dimensional long-range randomly-coupled quantum spin chain, within the framework of reservoir computing. In time sequence learning tasks, we find the system in the quantum many-body localized (MBL) phase holds long-term memory, which can be attributed to the emergent local integrals of motion. On the other hand, MBL phase does not provide sufficient nonlinearity in learning highly-nonlinear time sequences, which we show in a parity check task. This is reversed in the quantum ergodic phase, which provides sufficient nonlinearity but compromises memory capacity. In a complex learning task of Mackey—Glass prediction that requires both sufficient memory capacity and nonlinearity, we find optimal learning performance near the MBL-to-ergodic transition. This leads to a guiding principle of quantum reservoir engineering at the edge of quantum ergodicity reaching optimal learning power for generic complex reservoir learning tasks. Our theoretical finding can be tested with near-term NISQ quantum devices.
Convergence Guarantees for Discrete Mode Approximations to Non-Markovian Quantum Baths

Non-Markovian effects are important in modeling the behavior of open quantum systems arising in solid-state physics, quantum optics as well as in study of biological and chemical systems. The non-Markovian environment is often approximated by discrete bosonic modes, thus mapping it to a Lindbladian or Hamiltonian simulation problem. While systematic constructions of such modes have been previously proposed, the resulting approximation lacks rigorous and general convergence guarantees. In this Letter, we show that under some physically motivated assumptions on the system-environment interaction, the finite-time dynamics of the non-Markovian open quantum system computed with a sufficiently large number of modes is guaranteed to converge to the true result. Furthermore, we show that this approximation error typically falls off polynomially with the number of modes. Our results lend rigor to classical and quantum algorithms for approximating non-Markovian dynamics.
Assessing the impact of OCR noise on multilingual event detection over digitised documents

Event detection is a crucial task for natural language processing and it involves the identification of instances of specified types of events in text and their classification into event types. The detection of events from digitised documents could enable historians to gather and combine a large amount of information into an integrated whole, a panoramic interpretation of the past. However, the level of degradation of digitised documents and the quality of the optical character recognition (OCR) tools might hinder the performance of an event detection system. While several studies have been performed in detecting events from historical documents, the transcribed documents needed to be hand-validated which implied a great effort of human expertise and manual labour-intensive work. Thus, in this study, we explore the robustness of two different event detection language-independent models to OCR noise, over two datasets that cover different event types and multiple languages. We aim at analysing their ability to mitigate problems caused by the low quality of the digitised documents and we simulate the existence of transcribed data, synthesised from clean annotated text, by injecting synthetic noise. For creating the noisy synthetic data, we chose to utilise four main types of noise that commonly occur after the digitisation process: Character Degradation, Bleed Through, Blur, and Phantom Character. Finally, we conclude that the imbalance of the datasets, the richness of the different annotation styles, and the language characteristics are the most important factors that can influence event detection in digitised documents.
Deep Learning for 3D Human Motion Prediction: State-of-the-Art and Future Trends

Due to the success of deep learning in wide range of computer vision and computer graphics tasks, there is an increasing number of developed methods leveraging deep neural networks to solve human motion prediction. Recent motion prediction methods focus on solving many issues to predict accurate and natural human motion in temporal domain. In this study, we present a comprehensive survey of deep-learning-based human motion prediction methods. First, we define the human motion prediction problem and the scope of this study. We then provide related background knowledge and a comprehensive list of motion prediction methods based on our proposed classification. Next, we provide a complete survey of the characteristics widely used in the literature and explain the evaluation processes. Finally, we presented a quantitative comparison of recent studies and address the remaining unsolved issues while exploring possible research directions for future research.
Energetic Cost of Measurements Using Quantum, Coherent, and Thermal Light

Quantum measurements are basic operations that play a critical role in the study and application of quantum information. We study how the use of quantum, coherent, and classical thermal states of light in a circuit quantum electrodynamics setup impacts the performance of quantum measurements, by comparing their respective measurement backaction and measurement signal to noise ratio per photon. In the strong dispersive limit, we find that thermal light is capable of performing quantum measurements with comparable efficiency to coherent light, both being outperformed by single-photon light. We then analyze the thermodynamic cost of each measurement scheme. We show that single-photon light shows an advantage in terms of energy cost per information gain, reaching the fundamental thermodynamic cost.
Intensity-based dynamic speckle method using JPEG and JPEG2000 compression

Statistical processing of speckle data enables observation of the speed of processes. In intensity-based pointwise dynamic speckle analysis, a map related to speed's spatial distribution is extracted from a sequence of speckle patterns formed on an object under coherent light. Monitoring of time evolution of a process needs storage, transfer, and processing of a large number of images. We have proposed lossy compression of these images using JPEG and JPEG2000 formats. We have compared the maps computed from noncompressed and decompressed synthetic and experimental images, and we have proven that both compression formats can be applied in the dynamic speckle analysis.
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis

Recently, there has been a trend to investigate the factual knowledge captured by Pre-trained Language Models (PLMs).Many works show the PLMs' ability to fill in the missing factual words in cloze-style prompts such as "Dante was born in [MASK]."However, it is still a mystery how PLMs generate the results correctly: relying on effective clues or shortcut patterns?We try to answer this question by a causal-inspired analysis that quantitatively measures and evaluates the wordlevel patterns that PLMs depend on to generate the missing words.We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.Our analysis shows: (1) PLMs generate the missing factual words more by the positionally close and highly co-occurred words than the knowledge-dependent words; (2) the dependence on the knowledge-dependent words is more effective than the positionally close and highly co-occurred words.Accordingly, we conclude that the PLMs capture the factual knowledge ineffectively because of depending on the inadequate associations.
Optimising Equal Opportunity Fairness in Model Training

Real-world datasets often encode stereotypes and societal biases.Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing societal preconceptions.Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias.However, a disconnect between fairness criteria and training objectives makes it difficult to reason theoretically about the effectiveness of different techniques.In this work, we propose two novel training objectives which directly optimise for the widely-used criterion of equal opportunity, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.
Quantum Non-Markovian Environment-to-System Backflows of Information: Nonoperational vs. Operational Approaches

Quantum memory effects can be qualitatively understood as a consequence of an environment-to-system backflow of information. Here, we analyze and compare how this concept is interpreted and implemented in different approaches to quantum non-Markovianity. We study a nonoperational approach, defined by the distinguishability between two system states characterized by different initial conditions, and an operational approach, which is defined by the correlation between different outcomes associated to successive measurement processes performed over the system of interest. The differences, limitations, and vantages of each approach are characterized in detail by considering diverse system–environment models and dynamics. As a specific example, we study a non-Markovian depolarizing map induced by the interaction of the system of interest with an environment characterized by incoherent and coherent self-dynamics.
Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals

The automatic handling of banknotes can be conducted not only by specialized facilities, such as vending machines, teller machines, and banknote counters, but also by handheld devices, such as smartphones, with the utilization of built-in cameras and detection algorithms. As smartphones are becoming increasingly popular, they can be used to assist visually impaired individuals in daily tasks, including banknote handling. Although previous studies regarding banknote detection by smartphone cameras for visually impaired individuals have been conducted, these studies are limited, even when conducted in a cross-dataset environment. Therefore, we propose a deep learning-based method for detecting fake multinational banknotes using smartphone cameras in a cross-dataset environment. Experimental results of the self-collected genuine and fake multinational datasets for US dollar, Euro, Korean won, and Jordanian dinar banknotes confirm that our method demonstrates a higher detection accuracy than conventional “you only look once, version 3” (YOLOv3) methods and the combined method of YOLOv3 and the state-of-the-art convolutional neural network (CNN).
The first chatbot of a tourism/hospitality journal: Editor’s impressions

The first chatbot of an academic journal in the field of tourism and hospitality was launched in 2017. This editorial presents the structure of the chatbot of the European Journal of Tourism Research, reflects on Editor’s experience in developing and using the chatbot, and provides practical recommendations to editors and publishers who may wish to adopt chatbots for social media communications.