Reviewerly/exHarmony huggingface dataset:
Improving Biomedical Question Answering by Data Augmentation and Model Weighting

Biomedical Question Answering aims to extract an answer to the given question from a biomedical context. Due to the strong professionalism of specific domain, it's more difficult to build large-scale datasets for specific domain question answering. Existing methods are limited by the lack of training data, and the performance is not as good as in open-domain settings, especially degrading when facing to the adversarial sample. We try to resolve the above issues. First, effective data augmentation strategies are adopted to improve the model training, including slide window, summarization and round-trip translation. Second, we propose a model weighting strategy for the final answer prediction in biomedical domain, which combines the advantage of two models, open-domain model QANet and BioBERT pre-trained in biomedical domain data. Finally, we give adversarial training to reinforce the robustness of the model. The public biomedical dataset collected from PubMed provided by BioASQ challenge is used to evaluate our approach. The results show that the model performance has been improved significantly compared to the single model and other models participated in BioASQ challenge. It can learn richer semantic expression from data augmentation and adversarial samples, which is beneficial to solve more complex question answering problems in biomedical domain.
An exploratory study of automatic text summarization in biomedical and healthcare domain

In the last two decades, the uses of automatic text summarization have been realized in a wide range of applications in various fields cutting across a number of verticals. Amongst these, one of the most inquired is the domain of healthcare and medicine. Many of the studies have revealed that the use of automatic text summarization in the biomedical and healthcare domain helps researchers and medical professionals save their time and access more information in considerably short spans of time. This article reports some of the recent studies that enumerate the benefits and limitations of the uses of automatic text summarization in the biomedical and healthcare domain. In addition, the paper also explores certain new possible applications of automatic text summarization in the biomedical and healthcare domain. Furthermore, it discusses the trends and vision towards future opportunities for possible research in automatic text summarization in the context of medical and healthcare domain.
Two New Datasets for Italian-Language Abstractive Text Summarization

Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.
Security First, Security by Design, or Security Pragmatism – Strategic Roles of IT Security in Digitalization Projects

Although digital transformation is geared to achieving strategic goals such as efficiency or competitive advantages, it involves digital threats. IT security is an overarching task for managers and specialists that currently receives little attention in digitalization projects. Therefore, the strategic potential of IT security mostly remains untapped due to a lack of appropriate decision-making and communication tools that support project managers to address IT security consciously. This work tackles this issue by introducing a method to strategically consider IT security in digitalization projects using a design science approach. As a result, three strategic variants of IT security in digitalization projects and their underlying drivers were identified. By consolidating this knowledge into an applicable artifact, this work helps explain existing action patterns in organizations. Moreover, it provides a managerial tool to evaluate and decide on appropriate IT security for digitalization projects.
SIG: Moving from Brain-Computer Interfaces to Personal Cognitive Informatics

Consumer neurotechnology is arriving en masse, even while algorithms for user state estimation are being actively defined and developed. Indeed, many consumable wearables are now available that try to estimate cognitive changes from wrist data or body movement. But does this data help people? It's a critical time to ask how users could be informed by wearable neurotechnology, in a way that would be relevant to their needs and serve their personal well-being. The aim of this SIG is to bring together the key HCI communities needed to address this: personal informatics, digital health and wellbeing, neuroergonomics, and neuroethics.
Self-Organization of Physics-Informed Mechanisms in Recurrent Neural Networks: A Case Study in Pneumatic Artificial Muscles

Complex nonlinear dynamics limit the indirect sensing accuracy of the pneumatic artificial muscle (PAM). We implemented an echo state network, which is a type of recurrent neural network with autonomously switching readouts, in the form of mixture of experts system, where the different gates become self-organized as experts in different situations. We demonstrated that the proposed architecture enables self-structuring of the physical properties of the PAM associated with hysteresis and deadband within the network. This was realized by minimizing the learning error of the length prediction task without employing any prior information of its underlying physics. Furthermore, we showed that this self-organized gate switching mechanism greatly enhances performance in predicting the length of the PAM. Our results suggest that the proposed scheme is capable of self-organizing physics-informed models in a bottom-up manner through learning.
Semantic-Based Multi-Keyword Ranked Search Schemes over Encrypted Cloud Data

Traditional searchable encryption schemes construct document vectors based on the term frequency-inverse document frequency (TF-IDF) model. Such vectors are not only high-dimensional and sparse but also ignore the semantic information of the documents. The Sentence Bidirectional Encoder Representations from Transformers (SBERT) model can be used to train vectors containing document semantic information to realize semantic-aware multi-keyword search. In this paper, we propose a privacy-preserving searchable encryption scheme based on the SBERT model. The SBERT model is used to train vectors containing the semantic information of documents, and these document vectors are then used as input to the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering algorithm. The HDBSCAN algorithm generates a soft cluster membership vector for each document. We treat each cluster as a topic, and the vector represents the probability that the document belongs to each topic. According to the clustering process in the schemes, the topic-term frequency-inverse topic frequency (TTF-ITF) model is proposed to generate keyword topic vectors. Through the SBERT model, searchable encryption scheme can achieve more precise semantic-aware keyword search. At the same time, the special index tree is used to improve search efficiency. The experimental results on real datasets prove the effectiveness of our scheme.
There’s no Such Thing as a Free Lunch: Lessons Learned from Exploring the Overhead Introduced by the Greenkeeper Dependency Bot in Npm

Dependency management bots are increasingly being used to support the software development process, for example, to automatically update a dependency when a new version is available. Yet, human intervention is often required to either accept or reject any action or recommendation the bot creates. In this article, our objective is to study the extent to which dependency management bots create additional, and sometimes unnecessary, work for their users. To accomplish this, we analyze 93,196 issue reports opened by Greenkeeper , a popular dependency management bot used in open source software projects in the npm ecosystem. We find that Greenkeeper is responsible for half of all issues reported in client projects, inducing a significant amount of overhead that must be addressed by clients, since many of these issues were created as a result of Greenkeeper taking incorrect action on a dependency update (i.e., false alarms). Reverting a broken dependency update to an older version, which is a potential solution that requires the least overhead and is automatically attempted by Greenkeeper , turns out to not be an effective mechanism. Finally, we observe that 56% of the commits referenced by Greenkeeper issue reports only change the client’s dependency specification file to resolve the issue. Based on our findings, we argue that dependency management bots should (i) be configurable to allow clients to reduce the amount of generated activity by the bots, (ii) take into consideration more sources of information than only the pass/fail status of the client’s build pipeline to help eliminate false alarms, and (iii) provide more effective incentives to encourage clients to resolve dependency issues.
Exploring challenges for Conversational Web Browsing with Blind and Visually Impaired Users

Conversational AI is changing the way we interact with digital services. However, there is still a lack of conversational paradigms facilitating access to the Web. This paper discusses a new approach for Conversational Web Browsing, and introduces a design space identified through a user-centered process that involved 26 blind and visually impaired users. The paper also illustrates the conceptual architecture of a software framework that can automatically generate conversational agents for the Conversational Web.
Discussion of University Chemistry Laboratory Management Using DOSA Platform and Safety Education Based on Blockchain

In recent years, there have been frequent explosions and accidents in university chemistry laboratories. Laboratory research and equipment are developing rapidly, but lab management and safety learning are still using the traditional way which is unreasonable and unsafe. It need use big data related technology to solve disorganized management and protect effectiveness of safe learning.
A Proposed Biometric Authentication Model to Improve Cloud Systems Security

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password.Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers.This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services.The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users' information.The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity.The research study introduces a novel algorithm called "Bio_Authen_as_a_Service" for implementing and evaluating the proposed model.The proposed system performs the biometric authentication process securely and preserves the privacy of user information.The experimental result was highly promising for securing cloud services using the proposed model.The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%.
Using a ChatBot to support clinical decision-making in free flap monitoring

Flap monitoring charts and escalation protocols are ubiquitous amongst microsurgical departments and can facilitate converting flap observations into flap monitoring decisions. However, human factors in the recognition-communication process of decision-making still pose a threat to timely intervention and thus are a key determinant of success in microvascular surgery. Digitally transforming paper-based pathways may facilitate early recognition and escalation to potentially salvage a free flap with compromised vascularity. We describe the early adoption and use case of a ChatBot to support clinical decision-making support for free flap monitoring - the 'FlapBot'.
A Real-Time Fatigue Sensing and Enhanced Feedback System

This paper describes a real-time fatigue sensing and enhanced feedback system designed for video terminal operating groups. This paper analyzes the advantages and disadvantages of various current acquisition devices and various algorithms for fatigue perception. After comparison, this study uses an eye movement instrument to collect user PERCLOS, and then calculates and determines the user’s fatigue state. A detailed fatigue discrimination calculation method is provided in this paper. The fatigue level is divided into three levels: mild fatigue, moderate fatigue and severe fatigue. Finally, this study uses the fatigue method demonstrated above to achieve real-time discrimination of the fatigue level of the user in front of the video operation terminal. This paper elaborates a method for waking up users and enhancing feedback based on their fatigue level and the importance of information. This study provides a solution for avoiding the operational risks caused by fatigue and lays the foundation for the machine to sense the user and provide different service solutions based on the user’s status.
Quasi-absolute interferometric testing of cylinders

A three-step quasi-absolute testing method for optical cylinders is proposed in this Letter. Three measurements are taken at the so-called cat's eye position and confocal null testing positions with a computer-generated hologram (CGH) rotated around the axis parallel to that of the cylinder. The quasi-absolute surface error of the cylinder is obtained by simple operations including addition/subtraction and flip of the datasets. The uncertainty is traceable to an optical flat. Two different CGHs are used for a convex cylinder and give consistent quasi-absolute testing results of the surface error, which experimentally validates the method.
Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning

The building-integrated photovoltaic (BIPV) system is provoking mention as a technology for generating the energy consumed in cities with renewable sources. As the number of BIPV systems increases, performance diagnosis through power-generation predictions becomes more essential. In the case of a colored BIPV module that has been installed on a wall, it is more difficult to predict the amount of power generation because the shading loss varies based on the entrance altitude of the irradiance. Recently, artificial intelligence technology that is able to predict power by learning the output data of the system has begun being used. In this paper, the power values of colored BIPV systems that have been installed on walls are predicted, and the system output values are compared. The current-voltage (I–V) curve data are measured to predict the power required changing the intensity of the irradiance, and the linear regression model is derived for the changes in the voltage and current at a maximum power operating point and during irradiance changes. To improve the power prediction accuracy by considering the shading loss of colored BIPVs, a new model is proposed via neural network machine learning (ML). In addition, the accuracy of the proposed prediction models is evaluated by comparing the metrics such as RMSE, MAE, and R2. As a result of testing the linear regression model and the proposed ML model, the R2 values for the voltage and current values of the proposed ML model were 5% higher for voltage and 2% higher for current. From this result, the proposed ML model of the RMSE about real power improved by more than 50% (0.0754 kW) compared to the simulation model (0.1581 KW). The proposed model demonstrates high-accuracy power estimations and is expected to help diagnose the performance of BIPV systems with colored modules.
A Particle Swarm Optimization Backtracking Technique Inspired by Science-Fiction Time Travel

Artificial intelligence techniques, such as particle swarm optimization, are used to solve problems throughout society. Optimization, in particular, seeks to identify the best possible decision within a search space. Problematically, particle swarm optimization will sometimes have particles that become trapped inside local minima, preventing them from identifying a global optimal solution. As a solution to this issue, this paper proposes a science-fiction inspired enhancement of particle swarm optimization where an impactful iteration is identified and the algorithm is rerun from this point, with a change made to the swarm. The proposed technique is tested using multiple variations on several different functions representing optimization problems and several standard test functions used to test various particle swarm optimization techniques.
A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices

Distributed machine learning paradigms have benefited from the concurrent advancement of deep learning and the Internet of Things (IoT), among which federated learning is one of the most promising frameworks, where a central server collaborates with local learners to train a global model. The inherent heterogeneity of IoT devices, i.e., non-independent and identically distributed (non-i.i.d.) data, and the inconsistent communication network environment results in the bottleneck of a degraded learning performance and slow convergence. Moreover, most weight averaging-based model aggregation schemes raise learning fairness concerns. In this paper, we propose a peer-to-peer decentralized learning framework to tackle the above issues. Particularly, each local client iteratively finds a learning pair to exchange the local learning model. By doing this, multiple learning objectives are optimized to advocate for learning fairness while avoiding small-group domination. The proposed fairness-aware approach allows local clients to adaptively aggregate the received model based on the local learning performance. The experimental results demonstrate that the proposed approach is capable of significantly improving the efficacy of federated learning and outperforms the state-of-the-art schemes under real-world scenarios, including balanced-i.i.d., unbalanced-i.i.d., balanced-non.i.i.d., and unbalanced-non.i.i.d. environments.
Professional Well-Being and Work Engagement of University Teachers Based on Expert Fuzzy Data and SOR Theory

The study of the professional well-being of university teachers not only encompasses the quality of teachers’ personal lives but can also reflect satisfaction and loyalty to their careers and has an irreplaceable impact on students’ thinking and political education. The article starts from explaining the connotation of professional happiness of university teachers and analyses the significance of improving the professional happiness of university teachers. Through a questionnaire survey of 504 young teachers in Jiangxi province universities, based on expert fuzzy data and SOR theory, the article analyses various influencing factors in this framework and finds that the current professional happiness of young teachers in universities is positively related to various influencing factors. Based on expert fuzzy data and SOR theory, this paper investigates the professional happiness and work commitment of college teachers. The results of several research tests show that young college teachers have different professional happiness under different conditions, which indicates that material incentives can improve the professional happiness of young college teachers to a certain extent. Therefore, the professional happiness of young university teachers can be improved in terms of enhancing comprehensive quality, improving social status, strengthening humanistic care, broadening growth platform, and establishing scientific system.
Feature-Based Adversarial Training for Deep Learning Models Resistant to Transferable Adversarial Examples

Although deep neural networks (DNNs) have achieved high performance across a variety of applications, they can often be deceived by adversarial examples that are generated by adding small perturbations to the original images. Adversaries may generate adversarial examples using the property of transferability, in which adversarial examples that deceive one model can also deceive other models because adversaries do not obtain any information on the DNNs deployed in real scenarios. Recent studies show that adversarial examples with feature space perturbations are more transferable than others. Adversarial training is an effective method to defend against adversarial attacks. However, it results in a decrease in the classification accuracy for natural images, and it is not sufficiently robust against transferable adversarial examples because it does not consider adversarial examples with feature space perturbations. We propose a novel adversarial training method to train DNNs to be robust against transferable adversarial examples and maximize their classification accuracy for natural images. The proposed method trains DNNs to correctly classify natural images and adversarial examples and also minimize the feature differences between them. The robustness of the proposed method was similar to those of the previous adversarial training methods for MNIST dataset and was up to average 6.13% and 9.24% more robust against transfer adversarial examples for CIFAR-10 and CIFAR-100 datasets, respectively. In addition, the proposed method yielded an average classification accuracy that was approximately 0.53%, 6.82%, and 10.60% greater than some state-of-the-art adversarial training methods for all datasets, respectively. The proposed method is robust against a variety of transferable adversarial examples, which enables its implementation in security applications that may benefit from high-performance classification but are at high risk of attack.
Data-driven decision making: new opportunities for DSS in data stream contexts

Traditionally, Decision Support Systems (DSS) data were stored statically and persistently in a database. Increasing volume and intensity of information and data streams create new opportunities and challenges for DSS experts, data scientists, and decision makers. Novel data stream contexts require that we move beyond static DSS modelling techniques to support data-driven decision-making. Implementing incremental and/or adaptive algorithms may help to solve some of the challenges arising from data streams. This research investigates the use of these algorithms to better understand how their performance compares with more traditional approaches. We show that an adaptive DSS engine has the potential to identify errors and improve the accuracy of the model. We briefly identify how this approach could be applied to unexpected highly uncertain decision scenarios. Future research considers new opportunities to pursue a multidisciplinary approach to adaptive DSS design, development, and implementation leveraging emerging machine learning techniques in tackling complex decision problems.