Reviewerly/exHarmony huggingface dataset:
A virtual simulation experiment platform of subway emergency ventilation system and study on its teaching effect

For safety engineering majors, it is very important to cultivate the practical ability of professional talents. Due to the difficulty of conducting experiments in dangerous environments, a virtual simulation experiment teaching platform was established. The platform allowed students to understand the structure of the subway ventilation room, and master the control requirements of the ventilation system in the event of sudden fire, blockage, and failure in the subway. Its construction used technologies such as 3D modeling, human-computer interaction, and VR. To test the teaching effect of the simulation experiment platform, two indexes of operating skills and cognitive load were selected to study and analyze the experimental results of students. The research adopts the method of stratified sampling, 46 boys and 10 girls were selected from the first-year students majoring in safety engineering, and they were randomly divided into experimental group and control group, with 23 boys and 5 girls in each group. The experimental group used the simulation platform for teaching, while the control group used the traditional teaching method. The score of the assessment module in the platform was taken as the index of students' operating skills, and the cognitive load test was carried out by questionnaire to test the teaching effect. The test module scores showed that the average score of the experimental group was 32.79 points higher than that of the control group, and the results of the cognitive load test questionnaire showed that the experimental group scored 35.14% lower than the control group. The research shows that the virtual simulation experiment has a stronger teaching effect than the field experiment.
Language-enhanced object reasoning networks for video moment retrieval with text query

Video moment retrieval with text query aims to retrieve the most relevant video segment from the untrimmed video based on the given text query. Most existing works take advantage of pre-trained video understanding networks, which greatly limit its expressiveness and may ignore the fine-grained interaction between objects in the video. Motivated by this issue, we propose a novel model termed Language-enhanced Object Reasoning Networks (LEORN) which uses the object features extracted by the object detection network and incorporates the language features to model the relation between the objects within the frame. Besides, we design a new temporal shift mechanism to model the temporal relationship of objects while reducing the noise from irrelevant objects in the adjacent frames. The experiments on two challenging datasets, show that our proposed LEORN method with object-level features is a good complement to existing pre-trained video features and outperforms state-of-the-art methods by a simple combination.
Knowledge Discovery in a Recommender System: The Matrix Factorization Approach

Two famous matrix factorization techniques, the Singular Value Decomposition (SVD) and the Nonnegative Matrix Factorization (NMF), are popularly used by recommender system applications. Recommender system data matrices have many missing entries, and to make them suitable for factorization, the missing entries need to be filled. For matrix completion, we use mean, median and mode as three different cases of imputation. The natural clusters produced after factorization are used to formulate simple out-of-sample extension algorithms and methods to generate recommendation for a new user. Two cluster evaluation measures, Normalized Mutual Information (NMI) and Purity are used to evaluate the quality of clusters.
Neural network training with highly incomplete medical datasets

Abstract Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artifacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.
The Franke–Gorini–Kossakowski–Lindblad–Sudarshan (FGKLS) Equation for Two-Dimensional Systems

Open quantum systems are, in general, described by a density matrix that is evolving under transformations belonging to a dynamical semigroup. They can obey the Franke–Gorini–Kossakowski–Lindblad–Sudarshan (FGKLS) equation. We exhaustively study the case of a Hilbert space of dimension 2. First, we find final fixed states (called pointers) of an evolution of an open system, and we then obtain a general solution to the FGKLS equation and confirm that it converges to a pointer. After this, we check that the solution has physical meaning, i.e., it is Hermitian, positive and has trace equal to 1, and find a moment of time starting from which the FGKLS equation can be used—the range of applicability of the semigroup symmetry. Next, we study the behavior of a solution for a weak interaction with an environment and make a distinction between interacting and non-interacting cases. Finally, we prove that there cannot exist oscillating solutions to the FGKLS equation, which would resemble the behavior of a closed quantum system.
Energy in Schools: Empowering Children to Deliver Behavioural Change for Sustainability

This short paper introduces Energy in Schools, an interactive platform designed to improve energy efficiency in UK schools through a combination of environmental measurement and behavioural change. Uniquely, Energy in Schools is designed to empower all the stakeholders within a school, including children, teachers and leadership groups to reduce their energy costs and carbon emissions. The paper reports on the motivation and design of the platform, a discussion on how interactive materials and educational activities were developed to raise awareness and understanding of energy and the climate emergency, how children led activities were used to instrument and analyses energy use via child friendly Internet of Things technologies. Finally, an analysis of its success in encouraging behavioural change through the qualitative and quantitative evaluation of a live deployment across 20 schools is presented. Overall, it was observed that encouraging multiple stakeholders to work together interactively can decrease energy consumption in the majority of schools by over 5%.
Novel multi‐domain attention for abstractive summarisation

Abstract The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary, and the summary generated by models lacks the cover of the subject of source document due to models' small perspective. In order to make up these disadvantages, a multi‐domain attention pointer (MDA‐Pointer) abstractive summarisation model is proposed in this work. First, the model uses bidirectional long short‐term memory to encode, respectively, the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level. Furthermore, the multi‐domain attention mechanism between the semantic representations and the summary word is established, and the proposed model can generate summary words under the proposed attention mechanism based on the words and sentences. Then, the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary, and the coverage mechanism is introduced, respectively, into word and sentence level to reduce the redundancy of summary content. Finally, experiment validation is conducted on CNN/Daily Mail dataset. ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively, and the results verify the validation of model proposed by this paper.
Enhancing Creativity as Innovation via Asynchronous Crowdwork

Synchronous, face-to-face interactions such as brainstorming are considered essential for creative tasks (the old normal). However, face-to-face interactions are difficult to arrange because of the diverse locations and conflicting availability of people—a challenge made more prominent by work-from-home practices during the COVID-19 pandemic (the new normal). In addition, face-to-face interactions are susceptible to cognitive interference.
The Role of Intermediaries, Terrorist Assemblage, and Re-skilling in the Adoption of Cashless Transaction Systems in Bangladesh

This work addresses the challenges associated with cashless transactions and Mobile Financial Services (MFS) in the Global South. In our 19-months long interview study in Dhaka, Bangladesh, we engaged with 38 participants, including everyday users, bank employees, and policymakers, and investigated their experiences and perspectives associated with financial services. Our findings reveal a wide range of factors, naming intermediaries, terrorist assemblage, and re-skilling the existing employees that impede the mass adoption of cashless transaction services in Bangladesh. The findings from this study contribute to the ongoing discourse on the challenges and opportunities offered by the digitization of financial systems in Bangladesh. Our recommendations aim to improve the integration of the cashless systems within the societal context of Bangladesh and, more broadly, the Global South.
Text Detection and Post-OCR Correction in Engineering Documents

As the amount of born-analog engineering documents is still very large, the information they contain can not be processed by a machine or any automatic process. To overcome this, a whole process of digital transformation must be implemented on this type of documents. In this paper, we propose to detect and recognize all textual entities present on this type of documents. They can be part of technical details about a technical diagrams, bill of material or functional descriptions, or simple tags written in a standardized format. These texts are present in the document in an unstructured way, so that they can be located anywhere on the plan. They can also be of any size and orientation. We propose here a study allowing the text detection and recognition with or without associated semantics (symbolic annotations and dictionary words). A solution coupling a text detector based on a deep learning architecture, an open-source OCR for string recognition and an OCR post-correction process based on text clustering is proposed as a first step in the digital transformation process of industrial plans and P&ID schemes. The results applied to a database of 30 images of industrial maps and plans from different industries (oil, gas, water...) are very promising and close to 84% of correct detection and 82% of correct tags (and lexicon-free words) recognition after post-correction.
Language Learning with Mobile Augmented Reality and Artificial Intelligence for Children with Autism Spectrum Disorder

Augmented Reality has gained popularity among special needs communities. The flexibility and the advanced capabilities of this technology, empowered with AI, are opening new opportunities for further research in the area of digitally-aided language interventions for children with ASD. The goal of this research is to investigate and develop new knowledge tools to support language learning in children with ASD. The research methodology would be both qualitative and quantitative, using interviews, participatory design, and observations. Initially, this project will define the theoretical and existing applications of adaptive AR interventions for children with autism. A preliminary investigation has been conducted, suggesting promising results that can shed a light on the opportunities offered by AR and AI for language learning. In the next phase, we will design and develop a novel adaptive mobile AR application based on activities like workshops and participatory design, followed by systematic empirical evaluations of the proposed solution.
Multimodal Zero-Shot Hateful Meme Detection

Facebook has recently launched the hateful meme detection challenge, which garnered much attention in academic and industry research communities. Researchers have proposed multimodal deep learning classification methods to perform hateful meme detection. While the proposed methods have yielded promising results, these classification methods are mostly supervised and heavily rely on labeled data that are not always available in the real-world setting. Therefore, this paper explores and aims to perform hateful meme detection in a zero-shot setting. Working towards this goal, we propose Target-Aware Multimodal Enhancement (TAME), which is a novel deep generative framework that can improve existing hateful meme classification models' performance in detecting unseen types of hateful memes. We conduct extensive experiments on the Facebook hateful meme dataset, and the results show that TAME can significantly improve the state-of-the-art hateful meme classification methods' performance in seen and unseen settings.
Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.
Intelligent solutions for earthquake data analysis and prediction for future smart cities

The analysis and prediction of the Earthquake for smart cities are of greatest significance because all the critical infrastructure like drinking water resources, mobile networks, healthcare, power grid, and transportation, may affect the social-economic balance of the place which is tampered with such natural disasters. An earthquake has some effect on the environment due to the sudden movement of tectonic plates known as Earthquake Environmental Effects (EEE). These changes can be divided into 2 categories namely primary (e.g. subsidence, surface faulting) and secondary effects (displaced rocks, tsunami, landslides). In this paper, the major contribution revolves around earthquakes and their analysis. Earthquakes occur due to the shifting of tectonic plates on the lithosphere. These movements occur due to the Frictional stress between gliding plate borders builds up and leads to fault line failure. This sudden shaking of the plates releases a certain kind of energy in the Lithosphere which creates seismic waves. Earthquakes are also categorized into different classes ranging from minor to great, depending on their magnitude.
Contact tracing in the context of COVID-19: a case study from Oman

In April 2020, the Ministry of Health (MoH) in Oman, a high-income country in the Eastern Mediterranean Region (EMR), implemented a robust contact tracing (CT) system for COVID-19. To capitalise on Oman's experience, EMRO has developed a case study presenting the methodology used to conduct the CT activities, main functions of the system, challenges encountered, lessons learnt, and the way forward. To develop the case study, a key informant interview was conducted virtually with the CT focal point in the MoH, using a semistructured questionnaire adapted from the WHO questionnaire for the assessment of CT activities. The Oman MoH launched a CT system based on three complementary digital tools: Tarassud plus, Medical Scout and HMushrif applications. Oman's CT strategy classifies contacts into close and casual contacts. Only close contacts are listed using the Tarassud plus application, while casual contacts are requested to self-monitor for 14 days using the other two applications. With the evolution of the outbreak, Oman MoH implemented stricter policies and prioritised the follow-up of close contacts to keep the CT activity manageable. Community health workers and volunteers facilitated the CT activities through sensitisation of the local community to the follow-up process and reducing the COVID-19-associated stigma. Challenges encountered revolved around contact data management, given the offline in-operability of the applications, and lack of national risk communication guidelines to address community concerns and widespread rumours.
On the control of flying qubits

The control of flying quantum bits (qubits) carried by traveling quantum fields is crucial for coherent information transmission in quantum networks. In this paper, we develop a general framework for modeling the control of flying qubits based on the quantum stochastic differential equation (QSDE) that describes the input-output process actuated by a standing quantum system. Under the continuous time-ordered photon-number basis, the infinite-dimensional QSDE is reduced to a low-dimensional deterministic differential equation for the non-unitary state evolution of the standing quantum system, and the outgoing flying-qubit states can be expressed in the form of randomly occurring quantum jumps. This makes it possible, as demonstrated by examples of flying-qubit generation and transformation, to analyze general cases when the number of excitations is not reserved. The proposed framework lays the foundation for the design of flying-qubit control systems, with which advanced control techniques can be incorporated for practical applications.
Exploring the Performance of Deep Neural Networks on Embedded Many-Core Processors

This paper explores and evaluates the potential of deep neural network (DNN)-based machine learning algorithms on embed-ded many-core processors in cyber-physical systems, such as self-driving systems. To run applications in embedded systems, a plat-form characterized by low power consumption with high accuracy and real-time performance is required. Furthermore, a platform is required that allows the coexistence of DNN applications and other applications, including conventional real-time control soft-ware, to enable advanced embedded systems, such as self-driving systems. Clustered many-core processors, such as Kalray MPPA3-80 Coolidge, can run multiple applications on a single platform because each cluster can run applications independently. Moreover, MPPA3-80 integrates multiple arithmetic elements that operate at low frequencies, thereby enabling high performance and low power consumption comparable to that of embedded graphics processing units. Furthermore, the Kalray Neural Network (KaNN) code generator, a deep learning inference compiler for the MPPA3-80 platform, can efficiently perform DNN inference on MPPA3-80. This paper evaluates DNN models, including You Only Look Once (YOLO)-based and Single Shot MultiBox Detector (SSD)-based mod-els, on MPPA3-80. The evaluation examines the frame rate and power consumption in relation to the size of the input image, the computational accuracy, and the number of clusters.
Robot Path Planning Based on an Improved Salp Swarm Algorithm

This paper demonstrates an improved version of the Salp Swarm Algorithm (SSA) to solve the problems of slow convergence and local minima of the original version. In the population initialization of this scheme, ten chaotic searches with dynamic inertia coefficients are introduced to increase the diversity so that the probability of being trapped in local minima is reduced. Genetic algorithms are then applied to improve the global search ability and convergence speed. The experiments with 12 test functions show that the improved version achieves better accuracy and convergence speed over the original SSA. In the test with robot path planning problem, the proposed algorithm shows improved performance in the average number of iterations, path length, and average number of turns by 69.2%, 19.1%, and 43%, respectively, compared with the original SSA.
Cumulative dual-branch network framework for long-tailed multi-class classification

The long-tailed data distribution problem (i.e., a few classes account for majority data, while most classes account for minority data) is widespread in large-scale and real-world datasets, and it poses a huge challenge to the computer vision field. Existing methods of long-tailed classification mainly focus on re-sampling, re-weighting, and transfer learning. Although class imbalance learning can yield better long-tailed classification performance, the feature representative ability of the feature extraction network is damaged to a certain extent. To deal with these issues, the present work proposes a novel cumulative dual-branch network framework (CDBNF), which takes into account the class imbalance learning and feature representation learning at the same time by the dual-branch network architecture. In CDBNF, the class imbalance learning branch greatly improves the classification performance of tail classes, while the few-shot learning branch enhances the feature representative ability. Furthermore, a cumulative learning strategy (CLS) is proposed in CDBNF to make it pay more attention to the tail classes gradually in the training process. The effectiveness and practicability of the proposed CDBNF are verified by the four benchmark datasets. Experimental results show that the classification performance of CDBNF is superior to other state-of-the-art methods, while the intra-class feature variance is smaller than other state-of-the-art methods.