Reviewerly/exHarmony huggingface dataset:
Named entity recognition using neural language model and CRF for Hindi language

Named Entity Recognition (NER) plays an important role in various Natural Language Processing (NLP) applications to extract the key information from a huge amount of unstructured text data. NER is a task of identifying and classifying the named entities into predefined categories for a given text. Recently, language models are highly appreciable in several NLP tasks as these state-of-the-art models result better even in resource scarcity. In this paper, we perform NER task on the Hindi language by incorporating the recently released multilingual language model MuRIL which stands for Multilingual Representation for Indian Languages. MuRIL is specially trained for 16 Indian languages. We develop a Hindi NER system using MuRIL with a conditional random field (CRF) layer and fine-tune the model on the ICON 2013 Hindi NER dataset. Further, in the proposed approach, we compute the addition of the last 4 layers representations of the MuRIL model instead of just using the last layer's representation and fine-tune the whole model. Several variants of this model are presented by applying different computations on token representations provided by different layers of 12-layered MuRIL architecture. The proposed model achieves state-of-the-art results as 87.89% precision, 83.74% recall and 85.77% F1-score and outperforms all other existing Hindi NER systems developed on the ICON 2013 dataset. Additionally, we develop a similar Hindi NER system by replacing the MuRIL language model with another state-of-the-art language model, called multilingual Bidirectional Encoder Representations from Transformers (mBERT) to analyze the efficiency of both language models over the Hindi NER task.
It's Good to Talk: A Comparison of Using Voice Versus Screen-Based Interactions for Agent-Assisted Tasks

Voice assistants have become hugely popular in the home as domestic and entertainment devices. Recently, there has been a move towards developing them for work settings. For example, Alexa for Business and IBM Watson for Business were designed to improve productivity, by assisting with various tasks, such as scheduling meetings and taking minutes. However, this kind of assistance is largely limited to planning and managing user's work. How might they be developed to do more by way of empowering people at work? Our research is concerned with achieving this by developing an agent with the role of a facilitator that assists users during an ongoing task. Specifically, we were interested in whether the modality in which the agent interacts with users makes a difference: How does a voice versus screen-based agent interaction affect user behavior? We hypothesized that voice would be more immediate and emotive, resulting in more fluid conversations and interactions. Here, we describe a user study that compared the benefits of using voice versus screen-based interactions when interacting with a system incorporating an agent, involving pairs of participants doing an exploratory data analysis task that required them to make sense of a series of data visualizations. The findings from the study show marked differences between the two conditions, with voice resulting in more turn-taking in discussions, questions asked, more interactions with the system and a tendency towards more immediate, faster-paced discussions following agent prompts. We discuss the possible reasons for why talking and being prompted by a voice assistant may be preferable and more effective at mediating human-human conversations and we translate some of the key insights of this research into design implications.
How has design thinking being used and integrated into software development activities? A systematic mapping

Software companies have been using Design Thinking (DT) as a user-centered design approach, putting the user at the center of the software development process. In this article, we report a Systematic Mapping Study to investigate the use of DT in software development. We evaluated 127 papers from 2010 to 2021. We analyzed how DT is integrated in software development, what are the models and techniques, what are the criteria used for selecting DT techniques, and what are the key points that DT practitioners should be aware of when using DT. As a result, we identified 3 strategies to integrate DT in software development, 16 models, and 85 techniques. We also found that the selection of techniques is related to the models’ working spaces being performed, and identified 7 criteria used for selecting DT techniques. Furthermore, we summarized 16 key points that DT practitioners should pay attention when using DT, and we proposed 4 takeaways for applying DT in software development Thus, our study contributes to DT practitioners by providing information to be used either as a starting point, or to integrate it into activities already performed by teams, or as a strategy to foster changes in the entire organization’s mindset.
Fall Detection with Wearable Sensors: A Hierarchical Attention-based Convolutional Neural Network Approach

Falls are among the most life-threatening events that challenge senior citizens’ independent living. Wearable sensor technologies have emerged as a viable solution for fall detection. However, existing fall detection models either focus on manual feature engineering or lack explainability. To advance the state-of-the-art of wearable sensor-based health management, we follow the computational design science paradigm and develop a deep learning model to detect falls based on wearable sensor data. We propose a Hierarchical Attention-based Convolutional Neural Network (HACNN) to optimize the model effectiveness. We collected two large publicly available datasets to evaluate our fall detection model. We conduct extensive evaluations on our proposed HACNN and discuss a case study to illustrate its advantage and explainability, that could guide future set-ups for fall detection systems. We contribute to the information systems (IS) knowledge base by enabling explainable fall detection for chronic disease management. We also contribute to the design science theory by proposing generalizable design principles in model building.
Solving Burgers’ equation with quantum computing

Abstract Computational fluid dynamics (CFD) simulations are a vital part of the design process in the aerospace industry. Although reliable CFD results can be obtained with turbulence models, direct numerical simulation of complex bodies in three spatial dimensions (3D) is impracticable due to the massive amount of computational elements. For instance, a 3D direct numerical simulation of a turbulent boundary-layer over the wing of a commercial jetliner that resolves all relevant length scales using a serial CFD solver on a modern digital computer would take approximately 750 million years or roughly 20% of the earth’s age. Over the past 25 years, quantum computers have become the object of great interest worldwide as powerful quantum algorithms have been constructed for several important, computationally challenging problems that provide enormous speed-up over the best-known classical algorithms. In this paper, we adapt a recently introduced quantum algorithm for partial differential equations to Burgers’ equation and develop a quantum CFD solver that determines its solutions. We used our quantum CFD solver to verify the quantum Burgers’ equation algorithm to find the flow solution when a shockwave is and is not present. The quantum simulation results were compared to: (i) an exact analytical solution for a flow without a shockwave; and (ii) the results of a classical CFD solver for flows with and without a shockwave. Excellent agreement was found in both cases, and the error of the quantum CFD solver was comparable to that of the classical CFD solver.
Deviation Point Curriculum Learning for Trajectory Outlier Detection in Cooperative Intelligent Transport Systems

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F-measure</i> by 5.06% and 2.4% while compared with generic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Node2Vec</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Struct2Vec</i> methods.
Multi-Cluster Feature Selection Based on Isometric Mapping

This letter presents an unsupervised feature selection method based on machine learning. Feature selection is an important component of artificial intelligence, machine learning, which can effectively solve the curse of dimensionality problem. Since most of the labeled data is expensive to obtain, this paper focuses on the unsupervised feature selection method. The distance metric of traditional unsupervised feature selection algorithms is usually based on Euclidean distance, and it is maybe unreasonable to map high-dimensional data into low-dimensional space by using Euclidean distance. Inspired by this, this paper combines manifold learning to improve the multi-cluster unsupervised feature selection algorithm. By using geodesic distance, we propose a multi-cluster feature selection based on isometric mapping (MCFS-I) algorithm to perform unsupervised feature selection adaptively for multiple clusters. Experimental results show that the proposed method consistently improves the clustering performance compared to the existing competing methods.
Economic analysis of youth participation in agripreneurship in Benin

The study assessed the factors affecting youth participation in rural entrepreneurship in Benin using data from the School-to-Work Transition Survey (SWTS) and applying the binary logit and the multinomial logit models. The results showed youth who have a larger number of children are more likely to choose agricultural businesses (agripreneurship) while those who have formal education, who have received training on entrepreneurship, who have registered business, and those who have located in urban areas are more likely to engage in non-agricultural businesses. Within agripreneurship, youth who belong to a larger household are more likely to engage in farming while those who are educated, who have access to credit, and who are located in urban areas are more likely to be engaged in non-farming agri-businesses. The study also revealed that cash crop production among Beninese youth was positively influenced by access to credit. The findings suggest that it would be necessary to promote development programmes that are geared towards enhancing the capacities of the youth with regards to concepts and skills of entrepreneurship in agriculture and measures to overcome challenges associated with different agribusiness activities.
A secure searchable encryption scheme for cloud using hash-based indexing

Cloud service for data outsourcing has triggered security and privacy needs that can be satisfied by encrypting data prior to outsourcing to the cloud. Searchable Encryption that uses methods like homomorphic encryption is inefficient and impractical. In this paper, we have proposed a secure searchable encryption scheme using Hash based Indexing that consists of Elliptic curve based ElGamal additive homomorphic encryption. The use of indexing reduces computational load on the cloud server and users. The scheme obviates the need for generation and resource intensive processing on complex trapdoor system and binary query scheme. The security of this system relies on the Elliptic curve discrete logarithm problem assumption. Comparison analysis is performed and experimental results prove the suitability for the large data sets, while efficiently supporting the dynamic updates and effective ranking of precise files corresponding to the requested multi keywords.
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization

Existing studies show that code summaries help developers understand and maintain source code. Unfortunately, these summaries are often missing or outdated in software projects. Code summarization aims to generate natural language descriptions automatically for source code. According to Gros et al., code summaries are highly structured and have repetitive patterns (e.g. "return true if..."). Besides the patternized words, a code summary also contains important keywords, which are the key to reflecting the functionality of the code. However, the state-of-the-art approaches perform poorly on predicting the keywords, which leads to the generated summaries suffer a loss in informativeness. To alleviate this problem, this paper proposes a novel retrieve-and-edit approach named EditSum for code summarization. Specifically, EditSum first retrieves a similar code snippet from a pre-defined corpus and treats its summary as a prototype summary to learn the pattern. Then, EditSum edits the prototype automatically to combine the pattern in the prototype with the semantic information of input code. Our motivation is that the retrieved prototype provides a good start-point for post-generation because the summaries of similar code snippets often have the same pattern. The post-editing process further reuses the patternized words in prototype and generates keywords based on the semantic information of input code. We conduct experiments on a large-scale Java corpus (2M) and experimental results demonstrate that EditSum outperforms the state-of-the-art approaches by a substantial margin. The human evaluation also proves the summaries generated by EditSum are more informative and useful. We also verify that EditSum performs well on predicting the patternized words and keywords.
Perspectives of EFL teacher trainers and pre-service teachers on continued mandatory distance education during the pandemic

Due to the Covid-19 pandemic, the world experienced a fast transition to online education, starting in March 2020. This study aimed to review the process from the perspectives of prospective teachers and teacher trainers in a foreign language teacher education program after two semesters of mandatory distance education to make adjustments and take precautions for online possibilities in the future. Data was collected through questionnaires composed of open- and closed-ended items. A range of themes emerged from the responses, presented in terms of participants' preferences, their perceived benefits and challenges and recommendations for the future.
The Rise of Robots to Help Combat Covid-19

The Covid-19 pandemic caused worldwide tragic loss of human life along with social and economic disruptions. The hospitality and tourism sectors were particularly hit hard due to lockdowns for maintaining social distancing to handle the spread of the disease. In this crisis scenario, the adoption of artificial intelligence (AI) and robotics gave massive support to hospitals, hotels, airports, transportation systems, scenery areas and society. Technology played a significant role to reduce human contact and the potential spread of the virus. Humanoid robots, drones, autonomous driving vehicles, and socially intelligent robots performed essential tasks like disinfecting public areas, delivering necessities at people’s doorstep, giving safety, measuring body temperature and comforting the patients. This article depicts different roles played by the robots in various sectors during the pandemic and particularly highlights the opportunity that AI and robotics can provide to the hospitality and tourism industry. Tourism and hospitality scholars should develop and adopt robotic applications to foster guests’ experiences and protect natural and cultural resources. The use of robotic applications in future can be further used to improve people’s collaboration in tourism development.
VPNET: Variable Projection Networks

We introduce VPNet, a novel model-driven neural network architecture based on variable projection (VP). Applying VP operators to neural networks results in learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet and presents experiments. The VPNet approach was evaluated in the context of signal processing, where we classified a synthetic dataset and real electrocardiogram (ECG) signals. Compared to fully connected and one-dimensional convolutional networks, VPNet offers fast learning ability and good accuracy at a low computational cost of both training and inference. Based on these advantages and the promising results obtained, we anticipate a profound impact on the broader field of signal processing, in particular on classification, regression and clustering problems.
Neural software analysis

Developer tools that use a neural machine learning model to make predictions about previously unseen code.
Virtualization Risks and associated Issues in Cloud Environment

Cloud computing is dynamically evolving with high end services that are available in low costs and are easily accessible on-demand. Virtualization has made it possible to access data and resources from anywhere on a pay-as-you-use basis. With the magnification of cloud applications, there arises a need to identify the risks associated with virtualization technology. One big issue is configuration management of such a heterogeneous network. There is a need to design a secure access mechanism to handle the multi-tenancy. Virtualization faces a big challenge in resource allocation and developing trust among its users. Handling the data migration among virtual machines is another big issue. The evaluation of service level agreement of the service provider is also very important. It must cover all the details of agreement defining all the services that the CSP is promising along with the associated charges in detail.
Unrestricted Attention May Not Be All You Need–Masked Attention Mechanism Focuses Better on Relevant Parts in Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is one of the highly challenging tasks in natural language processing. It extracts fine-grained sentiment information in user-generated reviews, as it aims at predicting the polarities towards predefined aspect categories or relevant entities in free text. Previous deep learning approaches usually rely on large-scale pre-trained language models and the attention mechanism, which applies the complete computed attention weights and does not place any restriction on the attention assignment. We argue that the original attention mechanism is not the ideal configuration for ABSA, as for most of the time only a small portion of terms are strongly related to the sentiment polarity of an aspect or entity. In this paper, we propose a masked attention mechanism customized for ABSA, with two different approaches to generate the mask. The first method sets an attention weight threshold that is determined by the maximum of all weights, and keeps only attention scores above the threshold. The second selects the top words with the highest weights. Both remove the lower score parts that are assumed to be less relevant to the aspect of focus. By ignoring part of input that is claimed irrelevant, a large proportion of input noise is removed, keeping the downstream model more focused and reducing calculation cost. Experiments on the Multi-Aspect Multi-Sentiment (MAMS) and SemEval-2014 datasets show significant improvements over state-of-the-art pre-trained language models with full attention, which displays the value of the masked attention mechanism. Recent work shows that simple self-attention in Transformer quickly degenerates to a rank-1 matrix, and masked attention may be another cure for that trend.
Inflated 3D ConvNet context analysis for violence detection

Abstract According to the Wall Street Journal, one billion surveillance cameras will be deployed around the world by 2021. This amount of information can be hardly managed by humans. Using a Inflated 3D ConvNet as backbone, this paper introduces a novel automatic violence detection approach that outperforms state-of-the-art existing proposals. Most of those proposals consider a pre-processing step to only focus on some regions of interest in the scene, i.e., those actually containing a human subject. In this regard, this paper also reports the results of an extensive analysis on whether and how the context can affect or not the adopted classifier performance. The experiments show that context-free footage yields substantial deterioration of the classifier performance (2% to 5%) on publicly available datasets. However, they also demonstrate that performance stabilizes in context-free settings, no matter the level of context restriction applied. Finally, a cross-dataset experiment investigates the generalizability of results obtained in a single-collection experiment (same dataset used for training and testing) to cross-collection settings (different datasets used for training and testing).
Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation

In the real world, one object is usually described via multiple views or modalities. Many existing multiview clustering methods fuse the information of multiple views by learning a consensus representation. However, the feature learned in this manner is usually redundant and has neglected the distinctions among the different views. Addressing this issue, a method named nonredundancy regularization based nonnegative matrix factorization with manifold learning (NRRNMF-ML) is proposed in the paper. A novel nonredundancy regularizer defined with the Hilbert–Schmidt Independence Criterion (HSIC) is incorporated in the objective function of the proposed method. By minimizing this term, the redundant information among the multiple views can be effectively reduced and the distinct contributions of the different views can be encouraged. To further utilizing manifold structure information of the data, a manifold regularizer is also constructed and included in the objective function of the proposed method. For the proposed method, an iterative optimization strategy was designed to solve the problem; the corresponding proof is presented both theoretically and experimentally in this paper. Experimental results on five multiview data sets compared with several representative multiview clustering methods revealed the effectiveness of the proposed method.