Reviewerly/exHarmony huggingface dataset:
Active personalized construction safety training using run-time data collection in physical and virtual reality work environments

Purpose Accidents resulting from poorly planned or setup work environments are a major concern within the construction industry. While traditional education and training of personnel offer well-known approaches for establishing safe work practices, serious games in virtual reality (VR) are increasingly being used as a complementary approach for active learning experiences. By taking full advantage of data collection and the interactions possible in the virtual environment, the education and training of construction personnel improves by using non-biased feedback and immersion. Design/methodology/approach This research presents a framework for the generation and automated assessment of VR data. The proposed approach is tested and evaluated in a virtual work environment consisting of multiple hazards. VR requires expensive hardware, technical knowledge and user acceptance to run the games effectively. An effort has been made to transfer the advantages VR gives to a physical setup. This is done using a light detection and ranging sensing system, which collects similar data and enables the same learning experiences. Findings Encouraging results on the participants’ experiences are presented and discussed based on actual needs in the Danish construction industry. An outlook presents future avenues towards enhancing existing learning methods. Practical implications The proposed method will help develop active learning environments, which could lead to safer construction work stations in the future, either through VR or physical simulations. Originality/value The utilization of run-time data collection and automatic analysis allows for better personalized feedback in the construction safety training. Furthermore, this study investigates the possibility of transferring the benefits of this system to a physical setup that is easier to use on construction sites without investing in a full VR setup.
Applications of Blockchain Technology to Higher Education Arena: A Bibliometric Analysis

Research related to blockchain is rapidly gaining importance in the higher education. This opportunity collaborates with a proposal for a review of papers on the main blockchain topic. The bibliometric analysis included 61 peer-reviewed articles published in the Scopus database during the period of 2016 to 2021. This paper offers the identification of gaps in the literature enabling studies on the subject in higher education. The article identifies the main applications of blockchain technology in higher education around the world, as well as suggests future investigations. For further scientific investigation, we propose the operationalization of each of the researched approaches, especially combining the blockchain relationship, artificial intelligence, digital innovation, digital maturity, and customer experience in higher education.
What makes a good commit message?

A key issue in collaborative software development is communication among developers. One modality of communication is a commit message, in which developers describe the changes they make in a repository. As such, commit messages serve as an "audit trail" by which developers can understand how the source code of a project has changed---and why. Hence, the quality of commit messages affects the effectiveness of communication among developers. Commit messages are often of poor quality as developers lack time and motivation to craft a good message. Several automatic approaches have been proposed to generate commit messages. However, these are based on uncurated datasets including considerable proportions of poorly phrased commit messages. In this multi-method study, we first define what constitutes a "good" commit message, and then establish what proportion of commit messages lack information using a sample of almost 1,600 messages from five highly active open source projects. We find that an average of circa 44% of messages could be improved, suggesting the use of uncurated datasets may be a major threat when commit message generators are trained with such data. We also observe that prior work has not considered semantics of commit messages, and there is surprisingly little guidance available for writing good commit messages. To that end, we develop a taxonomy based on recurring patterns in commit messages' expressions. Finally, we investigate whether "good" commit messages can be automatically identified; such automation could prompt developers to write better commit messages.
Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F -score = 92).
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SRl achieves new state-of-the-art performance when combined with NSM, a subgraph-oriented reasoner, for embedding-based KBQA methods.
Augmented reality on industrial assembly line: Impact on effectiveness and mental workload

Studies examining the potential of augmented reality (AR) to improve assembly tasks are often unrepresentative of real assembly line conditions and assess mental workload only through subjective measurements and leads to conflicting results. We proposed a study directly carried out in industrial settings, to compare the impact of AR-based instructions to computerized instructions, on assembly effectiveness (completion time and errors) and mental workload using objective (eye tracking), subjective (NASA-TLX) and behavioral measurements (dual task paradigm). According to our results, AR did not improve effectiveness (increased assembly times and no decrease in assembly errors). Two out of three measurements indicated that AR led to more mental workload for simple assembly workstation, but equated computer instructions for complex workstation. Our data also suggest that, AR users were less able to detect external events (danger, alert), which may play an important role in the occurrence of work accidents.
Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices

The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.
HEBO: An Empirical Study of Assumptions in Bayesian Optimisation

In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers. Based on these findings, we propose a Heteroscedastic and Evolutionary Bayesian Optimisation solver (HEBO). HEBO performs non-linear input and output warping, admits exact marginal log-likelihood optimisation and is robust to the values of learned parameters. We demonstrate HEBO’s empirical efficacy on the NeurIPS 2020 Black-Box Optimisation challenge, where HEBO placed first. Upon further analysis, we observe that HEBO significantly outperforms existing black-box optimisers on 108 machine learning hyperparameter tuning tasks comprising the Bayesmark benchmark. Our findings indicate that the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, multiobjective acquisition ensembles with Pareto front solutions improve queried configurations, and robust acquisition maximisers afford empirical advantages relative to their non-robust counterparts. We hope these findings may serve as guiding principles for practitioners of Bayesian optimisation.
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities

Transfer learning has proven to be crucial in advancing the state of speech and natural language processing research in recent years.In speech, a model pre-trained by self-supervised learning transfers remarkably well on multiple tasks.However, the lack of a consistent evaluation methodology is limiting towards a holistic understanding of the efficacy of such models.SUPERB was a step towards introducing a common benchmark to evaluate pretrained models across various speech tasks.In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.We use a lightweight methodology to test the robustness of representations learned by pre-trained models under shifts in data domain and quality across different types of tasks.It entails freezing pretrained model parameters, only using simple task-specific trainable heads.The goal is to be inclusive of all researchers, and encourage efficient use of computational resources.We also show that the task diversity of SUPERB-SG coupled with limited task supervision is an effective recipe for evaluating the generalizability of model representation.˚Equal contribution.
Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.
Assessing the State of Self-Supervised Human Activity Recognition Using Wearables

The emergence of self-supervised learning in the field of wearables-based human activity recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the field, namely to exploit unlabeled data to derive reliable recognition systems for scenarios where only small amounts of labeled training samples can be collected. As such, self-supervision, i.e., the paradigm of 'pretrain-then-finetune' has the potential to become a strong alternative to the predominant end-to-end training approaches, let alone hand-crafted features for the classic activity recognition chain. Recently a number of contributions have been made that introduced self-supervised learning into the field of HAR, including, Multi-task self-supervision, Masked Reconstruction, CPC, and SimCLR, to name but a few. With the initial success of these methods, the time has come for a systematic inventory and analysis of the potential self-supervised learning has for the field. This paper provides exactly that. We assess the progress of self-supervised HAR research by introducing a framework that performs a multi-faceted exploration of model performance. We organize the framework into three dimensions, each containing three constituent criteria, such that each dimension captures specific aspects of performance, including the robustness to differing source and target conditions, the influence of dataset characteristics, and the feature space characteristics. We utilize this framework to assess seven state-of-the-art self-supervised methods for HAR, leading to the formulation of insights into the properties of these techniques and to establish their value towards learning representations for diverse scenarios.
Conformer: Local Features Coupling Global Representations for Recognition and Detection

With convolution operations, Convolutional Neural Networks (CNNs) are good at extracting local features but experience difficulty to capture global representations. With cascaded self-attention modules, vision transformers can capture long-distance feature dependencies but unfortunately deteriorate local feature details. In this paper, we propose a hybrid network structure, termed Conformer, to take both advantages of convolution operations and self-attention mechanisms for enhanced representation learning. Conformer roots in feature coupling of CNN local features and transformer global representations under different resolutions in an interactive fashion. Conformer adopts a dual structure so that local details and global dependencies are retained to the maximum extent. We also propose a Conformer-based detector (ConformerDet), which learns to predict and refine object proposals, by performing region-level feature coupling in an augmented cross-attention fashion. Experiments on ImageNet and MS COCO datasets validate Conformer's superiority for visual recognition and object detection, demonstrating its potential to be a general backbone network.
Information science based on FEBAB’s census of Brazilian library science: postgraduate data

The Library and Information Science areas are relatively new compared to other fields of knowledge, with few updated and reliable data that portray their profiles and scarce scientific studies focused on understanding the characteristics of the people composing them. The guiding objective of the research presented in this work is to evidence the data of postgraduates (master’s and doctoral students) and postgraduates (masters and doctors) from the Brazilian Library and Information Science Census. Four filters related to the education mentioned above levels were applied, with the data worked in Excel dynamic spreadsheets. The results show a predominant profile of white cisgender women between 36 and 40 years old, working in federal higher education institutions, earning above R$10,454.00, with experience in the area between 11 and 15 years. The geographical concentration is in Rio de Janeiro, and the Federal University of Rio de Janeiro has the highest number of graduates who continued their studies in stricto sensu post-graduation. However, the Federal University of Santa Catarina has the highest number of master’s and doctor’s respondents. The research objective was achieved to the extent that it presents the profile from the socioeconomic aspects, academic training, and professional performance. We hope that other studies will be conducted using the dynamics of filters provided by the Brazilian Library and Information Science Census so that the government, universities, class entities, and the area itself will better know its profile.
The Pursuit of Uniqueness: Extending Valiant-Vazirani Theorem to the Probabilistic and Quantum Settings

Valiant-Vazirani showed in 1985 \cite{VV85} that solving NP with the promise that "yes" instances have only one witness is powerful enough to solve the entire NP class (under randomized reductions).We are interested in extending this result to the quantum setting. We prove extensions to the classes Merlin-Arthur MA and Quantum-Classical-Merlin-Arthur QCMA \cite{AN02}. Our results have implications for the complexity of approximating the ground state energy of a quantum local Hamiltonian with a unique ground state and an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">inverse polynomial</mml:mtext></mml:mrow></mml:math> spectral gap. We show that the estimation (to within polynomial accuracy) of the ground state energy of poly-gapped 1-D local Hamiltonians is QCMA-hard, under randomized reductions. This is in stark contrast to the case of constant gapped 1-D Hamiltonians, which is in NP \cite{Has07}. Moreover, it shows that unless QCMA can be reduced to NP by randomized reductions, there is no classical description of the ground state of every poly-gapped local Hamiltonian that allows efficient calculation of expectation values.Finally, we discuss a few of the obstacles to the establishment of an analogous result to the class Quantum-Merlin-Arthur (QMA). In particular, we show that random projections fail to provide a polynomial gap between two witnesses.
P4: Portable Parallel Processing Pipelines for Interactive Information Visualization

We present P4, an information visualization toolkit that combines declarative design specification and GPU computing for building high-performance interactive systems. Most of the existing information visualization toolkits do not harness the power of parallel processors in today's mainstream computers. P4 leverages GPU computing to accelerate both data processing and visualization rendering for interactive visualization applications. P4's programming interface offers a declarative visualization grammar for rapid specifications of data transformations, visual encodings, and interactions. By simplifying the development of GPU-accelerated visualization systems while supporting a high degree of flexibility and customization for design specification, P4 narrows the gap between expressiveness and scalability in information visualization toolkits. Through a range of examples and benchmark tests, we demonstrate that P4 provides high efficiency for creating interactive visualizations and offers drastic performance improvement over current state-of-the-art toolkits.
Competence-based Approach to Learning Outcomes Assessment: Russian Experience in Sociological Education

The paper considers the methodological approaches to the description of the competency outcomes of education when implementing the federal standards of higher education on the example of the Sociology as an area of study. The authors have offered a methodic for assessment of the competences formation which is based on the so called indicators of achievement of each of the related competences and on the distribution of these indicators among all elements (modules) of the educational program of BA or MA in Sociology. In the paper, a list of indicators of universal (to be achieved in each higher education program regardless of the area) and general professional competences of the Bachelor’s and Master’s degrees in Sociology is proposed, as a result of monitoring and evaluation of several educational programs in the area of Sociology; moreover, the authors adduce the examples of content-related, concrete indicators of education called descriptors for competences. Besides, the paper describes the testing schemes which enable to adjust indicators of universal competences and to evaluate the level of competence of students who have completed the related educational level.