Reviewerly/exHarmony huggingface dataset:
Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction

In recent years, there have been many studies on the application and implementation of machine learning techniques in the financial domain. Implementation of such state-of-the-art models inevitably requires interpretability for users to understand the result and trust. However, as most of the machine learning methods are “black-box,” explainable AI, which aims to provide explanations to users, has become an important research issue. This paper focuses on explanation by counterfactual example for a bankruptcy-prediction model. Counterfactual-based explanation offers an alternative case for users in order for them to have a desired output from the model. This paper proposes a genetic algorithm (GA)-based counterfactual generation algorithm using feature importance whilst taking other key factors into account. Feature importance was derived from a prediction model, and key factors for counterfactuals include closeness to the original dataset and sparsity. The proposed method presented advantages over the nearest contrastive sample and a simple counterfactual generation algorithm in the experiment. Also, it provides relevant and compact explanations to enhance the interpretability of the bankruptcy prediction model.
Development of research network on Quantum Annealing Computation and Information using Google Scholar data

We build and analyse the network of 100 top-cited nodes (research papers and books from Google Scholar; the strength or citation of the nodes range from about 44 000 up to 100) starting in early 1980 until last year. These searched publications (papers and books) are based on Quantum Annealing Computation and Information categorized into four different sets: (A) Quantum/Transverse Field Spin Glass Model, (B) Quantum Annealing, (C) Quantum Adiabatic Computation and (D) Quantum Computation Information in the title or abstract of the searched publications. We fitted the growth in the annual number of publication ([Formula: see text]) in each of these four categories, A-D, to the form [Formula: see text] where [Formula: see text] denotes the time in years. We found the scaling time [Formula: see text] to be of the order of about 10 years for categories A and C, whereas [Formula: see text] is of the order of about 5 years for categories B and D. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.
Change in students’ explanation of the shape of snowflakes after collaborative immersive virtual reality

In recent years, chemistry educators are increasingly adopting immersive virtual reality (IVR) technology to help learners visualise molecular interactions. However, educational studies on IVR mostly investigated its usability and user perceptions leaving out its impact on improving conceptual understanding. If they evaluated students’ knowledge gains, they tended to use information recall tests to assess knowledge gains. Employing interviews and diagram-drawing tasks, this study explored how students’ conceptual understanding of the nature of hydrogen bonds and the shape of snowflakes changed through a collaborative IVR experience on snowflakes. Participants were 68 undergraduate chemistry students. Videos of pre-/post-interviews and student-generated diagrams were analysed. The results indicated a marked improvement in students’ conceptual understanding of the nature of hydrogen bonds among water molecules in snowflakes. After IVR, 57 students provided scientifically acceptable explanations of the nature of hydrogen bonds. Improvements in students’ understanding were related to the intermolecular nature of hydrogen bonds, the role of lone pairs of electrons in forming hydrogen bonds, and molecular interactions in 3D space. This study suggests that collaborative IVR could be a powerful way for students to visualise molecular interactions, examine their alternative conceptions, and build more coherent understanding. Implications for the design and implementation of IVR activities for science learning are discussed.
Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

Social media is an online platform with millions of users and is utilized to spread news, information, world events, discuss ideas, etc. During the COVID-19 pandemic, information and ideas are shared by users both officially and by citizens. Here, the detection of useful content from social media is a challenging task. Hence, natural language processing (NLP) and deep learning are widely utilized for the analysis of the emotions of people during the COVID-19 pandemic. Hence, this research introduces a deep learning mechanism for identifying the sentiment of the people by considering the online Twitter data regarding COVID-19. The intelligent lead-based BiLSTM is utilized to analyze people's sentiments. Here, the loss of the classifier while learning the data is eliminated through the incorporation of the intelligent lead optimization. Hence, the loss is reduced, and a more accurate analysis is obtained. The intelligent lead optimization is devised by considering the role of the informer in identifying the enemy base to safeguard the territory from attack along with the Monarch's knowledge. The performance of the intelligent lead-based BiLSTM for the sentiment analysis is assessed using the metrics like accuracy, sensitivity, and specificity and obtained the values of 96.11, 99.22, and 95.35%, respectively, which are 14.24, 10.45, and 26.57% enhanced performance compared to the baseline KNN technique.
Sign Detect: An app to detect sign language

The fast-growing world of today is advancing in every single domain. But, the deaf and dumb community–is quite far behind due to the vast communication gap in comparison to the rest of the population. To reduce this communication gap, Sign Language is being widely used and research is in progress to narrow down this gap. Even though there exist many state-of-art techniques, during the literature survey it was clear that these techniques failed to address the portability issue which reduced their acceptance by the community. Also, there doesn’t exist a full-proof comprehensive system for recognizing Alphabets, Numbers and Words. The objective of this paper is to create one such system named, SignDetect which is a Mobile Application which efficiently (1) recognizes the Sign Language; (2) quickly translates it to English and (3) then generates text and audio formats of the same. The system was tested for its accuracy with end users. Also, their User Experience (UX) was remarkable compared to the state-of-art systems used by them. As the system developed is a mobile application, it easily addressed the portability issue. And for wide acceptance, the application was developed in Flutter so that it can be easily installed on Android and iOS phones. Through this work, the authors hope to bridge the vast communication gap that is prevalent in today’s world and take a step forward in this direction.
Lifelong language learning with adaptive uncertainty regularization

It has been a long-standing goal in natural language processing (NLP) to learn a general linguistic intelligence model that can perform well on many different NLP tasks continually evolving over time while avoiding revisiting all previous data at each stage. Most existing deep neural networks suffer from catastrophic forgetting when dealing with sequential tasks in an incremental way, leading to dramatic performance degradation due to the missing training data of old tasks. In this paper, we propose a Lifelong language method with Adaptive Uncertainty Regularization (LAUR), which can adapt a single BERT model to work with continuously arriving text examples from different NLP tasks. Specifically, LAUR is built on the Bayesian online learning framework, and three uncertainty regularization terms are devised to collaboratively control the parameters so as to resolve the stability-plasticity dilemma in lifelong language learning. The previous posterior constrain parameters that strongly determine the output results, preventing these parameters from changing drastically, while other parameters are encouraged to be updated over time. In addition, we propose a task-specific residual adaptation module in parallel to each layer of BERT to endow LAUR with the capacity to learn better task-specific knowledge. This configuration makes LAUR less prone to losing the knowledge stored in the base BERT network when learning a new task. Experimental results show that LAUR outperforms state-of-the-art lifelong learning models on a variety of NLP tasks. For reproducibility, we submit the code and data at:https://github.com/kiujhytgtrfd2021/LAUR.
Implication of giant photon bunching on quantum phase transition in the dissipative anisotropic quantum Rabi model

We investigate the quantum phase transition in the dissipative anisotropic quantum Rabi model in the framework of quantum dressed master equation. From perspectives of both numerical and analytical analysis, we unravel the implication of the giant photon-bunching feature on the first-order quantum phase transition. The observed two-photon statistics can be well described analytically within a few lowest eigenstates at the low temperature. Moreover, such significant photon-bunching peak is generally exhibited at the deep-strong qubit-photon coupling, which is however lacking in the dissipative isotropic quantum Rabi model. Therefore, we suggest that the photon-bunching measurement is helpful to characterize the first-order QPT of the qubit-photon hybrid systems.
HSFL: An Efficient Split Federated Learning Framework via Hierarchical Organization

Federated learning (FL) has emerged as a popular paradigm for distributed machine learning among vast clients. Unfortunately, resource-constrained clients often fail to participate in FL because they cannot pay for the memory resources required for model training due to their limited memory or bandwidth. Split federated learning (SFL) is a novel FL framework in which clients commit intermediate results of model training to a cloud server for client-server collaborative training of models, making resource-constrained clients also eligible for FL. However, existing SFL frameworks mostly require frequent communication with the cloud server to exchange intermediate results and model parameters, which results in significant communication overhead and elongated training time. In particular, this can be exacerbated by the imbalanced data distributions of clients. To tackle this issue, we propose HSFL, a hierarchical split federated learning framework that efficiently trains SFL model through hierarchical organization participants. Under the HSFL framework, we formulate a Cloud Aggregation Time Minimization (CATM) problem to minimize the global training time and design a light-weight client assignment algorithm based on dynamic programming to solve it. Moreover, we develop a self-adaption approach to cope with the dynamic computational resources of clients. Finally, we implement and evaluate HSFL on various real-world training tasks, elaborating on its effectiveness and superiority in terms of efficiency and accuracy compared to baselines.
In the same boat: Parents’ and teachers’ role in protecting elementary school students’ online rights

The study offers a socio-legal typology describing how parents and teachers facilitate or hinder the protection of elementary school children in the online world. Drawing on interviews with 75 elementary school students, 15 parents, and 20 teachers in four Israeli schools, the findings distinguish four patterns of adult practices: restricting children’s rights, mobilizing children’s rights in cases of rights infringements, fostering children’s rights consciousness, and waiving children’s rights. The findings showed that most children held a favorable view of practices that protected their rights and were critical of adults who waived their rights. The conclusions delineate the importance of applying rights terminology to analyze parents’ and teachers’ practices. A terminology of rights underscores that restrictive practices should be embedded in principles of proportionality, highlights the balance between autonomy and protection, clarifies the implications of ignoring online injury, distinguishes between practices whose implications differ although they co-occur, and provides a relatively stable framework that can be adapted to changing online contexts. The conclusions also underscore that parents’ and teachers’ roles in fulfilling young children’s online rights are inherently intertwined and complementary. This mutual responsibility can afford opportunities for effective collaboration but might also lead to waiving children’s rights to protection in the online world.
Explaining information seeking intentions: Insights from a Slovenian social engineering awareness campaign

The human factor remains one of the key challenges in cybersecurity despite effective technical countermeasures in place. This study aims to determine what motivates individuals to seek information about social engineering by investigating the determinants of behavioral intention to follow the materials of a social engineering awareness campaign in Slovenia. A quantitative survey of individuals in Slovenia (N=542) aged 15 or older was administered with participants recruited through University of Maribor students. Data were collected on constructs related to the protection motivation theory (PMT) and the theory of planned behavior (TPB) as well as privacy concerns and perceived performance of authorities. The survey instrument was validated with a confirmatory factor analysis. Covariance-based structural equation modeling (CB-SEM) was used to determine relationships between constructs and analysis of differences between students and employed individuals. Results indicate perceived threat, subjective norm, attitude toward behavior and authorities performance are all significant predictors of behavioral intention. The associations between perceived threat and behavioral intention, and privacy concern and attitude towards behavior was not significant among employed individuals. Among students, trust in authorities was not a significant predictor of authorities performance. This study has several implications. The results of this study suggest that fear appeals may be effective in motivating individuals to seek information about social engineering attacks thus improving the effectiveness of awareness campaigns. They also offer some insights into how to improve messaging towards the target populations. Messaging emphasizing perceived threat may directly increase information seeking intention while messaging emphasizing coping with social engineering may do so indirectly through attitude towards behavior. This study also indicates that messaging should be tailored to the target population (e.g., messaging emphasizing perceived threat may be much less effective for employed individuals than students).
Spatial and temporal information fusion for human action recognition via Center Boundary Balancing Multimodal Classifier

This paper proposes a novel multimodal data classifier named Center Boundary Balancing Multimodal Classifier (CBBMC) to fuse and classify the spatial and temporal descriptors for recognizing human actions from depth video sequences. CBBMC is a composite algorithm integrating feature fusion and feature classification, in which Center Boundary Balancing Projection (CBBP) is used to balance the center and boundary information of feature class spaces. In order to solve the problem of multimodal information redundancy and isolation, two feature selection and fusion schemes of CBBMC based on embedded feature selection are presented. Moreover, two new action descriptors called Gaussian Pyramid Depth Motion Images (GP-DMI) and Depth Temporal Maps (DTM) are introduced to capture the multi-scale spatial and fine-grained temporal information of human activities. Finally, we present an effective spatial and temporal information fusion framework based on CBBMC for human action recognition. In order to evaluate the performance of the proposed approach, extensive experiments are conducted. The proposed method achieved impressive results on four benchmark datasets, namely MSR Action3D (96.33%), UTD-MHAD (94.41%), DHA (95.65%), and NTU RGB+D (83.31% cross-subject and 87.66% cross-view), even though only the depth modality was used. The experimental results demonstrate the effectiveness of our method.
Integrating professional machine translation literacy and data literacy

Abstract The data-driven paradigm of neural machine translation is a powerful translation technology based on state-of-the art approaches in artificial intelligence research. This technology is employed extensively in the professional translation process, requiring an adequate degree of machine translation literacy on the part of professional translators. At the same time, the increasing datafication to be observed in modern society – both in private as well as in professional contexts – contributes to the rise in prominence of another digital literacy, i. e., data literacy, which is also of high relevance with regard to data-driven machine translation. The present paper analyses and discusses in detail the individual dimensions and subdimensions of professional machine translation literacy and data literacy and attempts to integrate the two concepts. It thereby lays the theoretical foundation for a didactic project concerned with teaching data literacy in its machine translation-specific form to students in the fields of translation and specialised communication studies.
Compact network embedding for fast node classification

Network embedding has shown promising performance in real-world applications. The network embedding typically lies in a continuous vector space, where storage and computation costs are high, especially in large-scale applications. This paper proposes more compact representation to fulfill the gap. The proposed discrete network embedding (DNE) leverages hash code to represent node in Hamming space. The Hamming similarity between hash codes approximates the ground-truth similarity. The embedding and classifier are jointly learned to improve compactness and discrimination. The proposed multi-class classifier is further constrained to be discrete to expedite classification. In addition, this paper further extends DNE and proposes deep discrete attributed network embedding (DDANE) to learn compact deep embedding from more informative attributed network. From the perspective of generalized signal smoothing, the proposed DDANE trains an improved graph convolutional network autoencoder to effectively leverage node attribute and network structure. Extensive experiments on node classification demonstrate the proposed methods exhibit lower storage and computational complexity than state-of-the-art network embedding methods, and achieve satisfactory accuracy.
Pacta sunt servanda: Legal contracts in

We present Stipula, a domain specific language that may assist legal practitioners in programming legal contracts through specific patterns. The language is based on a small set of programming abstractions that correspond to common patterns in legal contracts. We illustrate the language by means of two paradigmatic legal contracts: a bike rental and a bet contract. Stipula comes with a formal semantics, an observational equivalence and a type inference system, that provide for a clear account of the contracts' behaviour and illustrate how several concepts from concurrency theory can be adapted to automatically verify the properties and the correctness of software-based legal contracts. We also discuss a prototype centralized implementation of Stipula.
Graph Neural Networks for Colorectal Histopathological Image Classification

Representing data in array form is not always an efficient way. Sometimes it can cause loss of bias information that is inherent in data. With the help of taking an image to data matrix form as input instead of flattening to an array, deep learning methods, especially convolutional neural networks, are more successful than traditional machine learning techniques in terms of accuracy rate in image processing. Besides, graphs are a very efficient way to represent problems such as social networks, protein interface prediction, and images. Graph Neural Networks (GNNs) are deep learning methods that apply convolution logic to data like a graph, and many applications are efficiently applied. That is why representing histopathological images with graphs can be an advantage to know the connection between cores. The study uses GNNs to classify tissue types in the Chaoyang dataset. First, the superpixel graph is constructed from an image, and then GNNs models are applied to the constructed graph dataset. Experimental results present better accuracies than the compared literature methods.
Illumination-aware window transformer for RGBT modality fusion

Combination of RGB and thermal sensors has been proven to be useful for many vision applications. However, how to effectively fuse the information of two modalities remains a challenging problem. In this paper, we propose an Illumination-Aware Window Transformer (IAWT) fusion module to handle the RGB and thermal multi-modality fusion. Specifically, the IAWT fusion module adopts a window-based multi-modality attention combined with additional estimated illumination information. The window-based multi-modality attention infers dependency cross modalities within a local window, thus implicitly alleviate the problem caused by weakly spatial misalignment of the RGB and thermal image pairs within specific dataset. The introduction of estimated illumination feature enables the fusion module to adaptively merge the two modalities according to illumination conditions so as to make full use of the complementary characteristics of RGB and thermal images under different environments. Besides, our proposed fusion module is task-agnostic and data-specific, which means it can be used for different tasks with RGBT inputs. To evaluate the advances of the proposed fusion method, we embed the IAWT fusion module into different networks and conduct the experiments on various RGBT tasks, including pedestrian detection, semantic segmentation and crowd counting. Extensive results demonstrate the superior performance of our method.
SCPNet: Self-constrained parallelism network for keypoint-based lightweight object detection

Keypoint-based object detection achieves better performance without positioning calculations and extensive prediction. However, they have heavy backbone, and high-resolution is restored using upsampling that obtain unreliable features. We propose a self-constrained parallelism keypoint-based lightweight object detection network (SCPNet), which speeds inference, drops parameters, widens receptive fields, and makes prediction accurate. Specifically, the parallel multi-scale fusion module (PMFM) with parallel shuffle blocks (PSB) adopts parallel structure to obtain reliable features and reduce depth, adopts repeated multi-scale fusion to avoid too many parallel branches. The self-constrained detection module (SCDM) has a two-branch structure, with one branch predicting corners, and employing entad offset to match high-quality corner pairs, and the other branch predicting center keypoints. The distances between the paired corners' geometric centers and the center keypoints are used for self-constrained detection. On MS-COCO 2017 and PASCAL VOC, SCPNet's results are competitive with the state-of-the-art lightweight object detection. https://github.com/mengdie-wang/SCPNet.git.
Quantum computing for data-centric engineering and science

In this perspective I give my answer to the question of how quantum computing will impact on data-intensive applications in engineering and science. I focus on quantum Monte Carlo integration as a likely source of (relatively) near-term quantum advantage, but also discuss some other ideas that have garnered wide-spread interest.