Reviewerly/exHarmony huggingface dataset:
Document Retrieval for Precision Medicine Using a Deep Learning Ensemble Method

With the development of biomedicine, the number of biomedical documents has increased rapidly bringing a great challenge for researchers trying to retrieve the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in specific retrieval tasks, thereby increasing the difficulty of biomedical information retrieval.This study aimed to find a more systematic method for retrieving relevant scientific literature for a given patient.In the initial retrieval stage, we supplemented query terms through query expansion strategies and applied query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employed a text classification model and relevance matching model to evaluate documents from different dimensions and then combined the outputs through logistic regression to re-rank all the documents from the initial ranking list.The proposed ensemble method contributed to the improvement of biomedical retrieval performance. Compared with the existing deep learning-based methods, experimental results showed that our method achieved state-of-the-art performance on the data collection provided by the Text Retrieval Conference 2019 Precision Medicine Track.In this paper, we proposed a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and relevance matching model better captured semantic context information and improved retrieval performance.
Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations

We present PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) that generalize Group equivariant Convolutional Neural Networks (G-CNNs). In PDE-G-CNNs a network layer is a set of PDE-solvers where geometrically meaningful PDE-coefficients become trainable weights. The underlying PDEs are morphological and linear scale space PDEs on the homogeneous space \(\mathbb {M}_d\) of positions and orientations. They provide an equivariant, geometrical PDE-design and model interpretability of the network.
Fixed-point iterative linear inverse solver with extended precision

Solving linear systems, often accomplished by iterative algorithms, is a ubiquitous task in science and engineering. To accommodate the dynamic range and precision requirements, these iterative solvers are carried out on floating-point processing units, which are not efficient in handling large-scale matrix multiplications and inversions. Low-precision, fixed-point digital or analog processors consume only a fraction of the energy per operation than their floating-point counterparts, yet their current usages exclude iterative solvers due to the cumulative computational errors arising from fixed-point arithmetic. In this work, we show that for a simple iterative algorithm, such as Richardson iteration, using a fixed-point processor can provide the same convergence rate and achieve solutions beyond its native precision when combined with residual iteration. These results indicate that power-efficient computing platforms consisting of analog computing devices can be used to solve a broad range of problems without compromising the speed or precision.
Vehicle Crash Prediction using Vision

Road accidents due to vehicles crashing each other not only result in loss of lives but also cause heavy damage to the infrastructure. Several infrastructural changes, for example, placement of mirror on blind corners, appropriate placement of traffic signs and lights etc., do reduce the number of vehicle crashes. However, there are numerous factors contributing to the crashes. It is understood that the last few seconds are crucial and if a crash can be predicted in them then it can be avoided. In this paper, we propose a computer vision based vehicle crash prediction pipeline which uses RetinaNet to detect vehicles, Kernalized Correlation filter to track them on road using CCTV view, and a Gaussian distribution approximation to predict the trajectories of vehicles in the video frames, by exploiting short term stationarity of the time process. The proposed prediction algorithm gives a short term prediction and yields quite precise results. Our proposed methodology is quite light as well as data independent as opposed to the ones in literature.
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies

Cybersecurity has recently gained considerable interest in today's security issues because of the popularity of the Internet-of-Things (IoT), the considerable growth of mobile networks, and many related apps. Therefore, detecting numerous cyber-attacks in a network and creating an effective intrusion detection system plays a vital role in today's security. However, it is difficult to accurately model cyber threats since modern security databases contain large number of security features that could include Outliers. In this paper, we present an Isolation Forest Learning-Based Outlier Detection Model for effectively classifying cyber anomalies. In order to evaluate the efficacy of the resulting Outlier Detection model, we also use several conventional machine learning approaches, such as Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost Classifier (ABC), Naive Bayes (NB), and K-Nearest Neighbor (KNN). The effectiveness of our propsoed Outlier Detection model is evaluated by conducting experiments on Network Intrusion Dataset with evaluation metrics such as precision, recall, F1-score, and accuracy. Experimental results show that the classification accuracy of cyber anomalies has been improved after removing outliers.
Microfabricated fiducial markers for digital image correlation-based micromechanical testing of LIGA Ni alloys

Photolithographically defined thin film Au dots were used as micro fiducial markers for digital image correlation (DIC), to enable two-dimensional strain measurement of 200 μm-thick LIGA (Lithographie, Galvanformung, Abformung) nickel alloys. Due to the sensitivity of electrodeposited films' microstructure and properties on the processing conditions, characterization of LIGA mechanical properties continues to be necessary for microsystems commercialization. DIC offers advantages over laser-based strain measurement techniques but creating suitable speckle patterns on specimens with dimensions under a millimeter is challenging. The material surface roughness itself is often used as the speckle pattern, or micro- or nanoparticles are applied to the surface. But for materials with highly polished surfaces, such as commercial LIGA alloys, the surface roughness is not always suitable, while application of particles still poses technical challenges in uniformity and reproducibility. We fabricated freestanding tensile specimens, with gauge sections 700 μm wide × 3 mm long × 200 μm thick, from electrodeposited Ni-10% Co using a commercial LIGA process, and conducted microtensile tests at strain rate 0.001 s-1. Designing and fabricating arrays of randomly oriented 1.5 μm-thick Au dots on the specimens provided a suitable way to obtain full-field surface strains over the entire gauge lengths and was reproducible from one specimen to another. Microfabricated fiducial markers therefore can be a useful surface-preparation approach for investigating micromechanical behavior, particularly plasticity and fracture, of LIGA films using DIC.
Research on Web Interface barrier-free Design for Elderly People

Traditional technology products are often neglected because of the high learning costs required. With the acceleration of the global ageing trend, the demand for barrier-free design will become more critical in the future. The traditional research focus is often only on the physiological degradation of the elderly to improve the design, lacking the need for the level of mental degeneration in the elderly. Therefore, this paper takes the Internet interface as the research carrier, makes the elderly's use of interface design as the research object, uses the semi-structured questionnaire to collect the needs of the elderly, and uses the hierarchical analysis method to analyse the difference of the interface design of the elderly. The weight of application for the optimisation of interface usability is given by the elderly. Finally, suggestions for improvement of the barrier-free interface for the elderly were proposed. This will help the future of the network world barrier-free design and enhance the participation of the elderly in online life.
Predicting the Number of COVID-19 Sufferers in Malang City Using the Backpropagation Neural Network with the Fletcher–Reeves Method

COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system. COVID-19 spreads through direct contact, wherein the infected individual spreads the COVID-19 virus through cough, sneeze, or close contacts. Predicting the number of COVID-19 sufferers becomes an important task in the effort to curb the spread of COVID-19. Artificial neural network (ANN) is the prediction method that delivers effective results in doing this job. Backpropagation, a type of ANN algorithm, offers predictive problem solving with good performance. However, its performance depends on the optimization method applied during the training process. In general, the optimization method in ANN is the gradient descent method, which is known to have a slow convergence rate. Meanwhile, the Fletcher–Reeves method has a faster convergence rate than the gradient descent method. Based on this hypothesis, this paper proposes a prediction model for the number of COVID-19 sufferers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. The experimental results show that the Backpropagation neural network with the Fletcher–Reeves method has a better performance than the Backpropagation neural network with the gradient descent method. This is shown by the Means Square Error (MSE) resulting from the proposed method which is smaller than the MSE resulting from the Backpropagation neural network with the gradient descent method.
Learning to Recognise Words Using Visually Grounded Speech

We investigated word recognition in a Visually Grounded Speech model. The model has been trained on pairs of images and spoken captions to create visually grounded embeddings which can be used for speech to image retrieval and vice versa. We investigate whether such a model can be used to recognise words by embedding isolated words and using them to retrieve images of their visual referents. We investigate the time- course of word recognition using a gating paradigm and perform a statistical analysis to see whether well known word competition effects in human speech processing influence word recognition. Our experiments show that the model is able to recognise words, and the gating paradigm reveals that words can be recognised from partial input as well and that recognition is negatively influenced by word competition from the word initial cohort.
Harnessing Smart Integration: Blockchain-Enabled B2C Reverse Supply Chain

B2C cross-border e-commerce witnessed an exponential growth across the MENA (Middle East & North Africa) region especially during the pandemic time. Though, cross-border e-commerce is high in this region, the customer hesitates to fill a cart from overseas company due to the fear of counterfeit/ fake products as well as hassle of returning the product. To the retailer the challenges is multiplied manifold. The trickiest barrier is complying with customs documentation for reversal, and collaboration with the intermediaries for reclamation. The return of goods faces challenges due to lack of collaborative and integrated technology linkage between the multiple partners participating in the operation. Additionally, the customer does not have a visibility and verification of the source from where the product is received. Blockchain technology is a game-changing technology to solve product traceability, identify fraudulent transactions, and provide digitized transaction details to improve customer experience. The paper aims to analyze the challenges in operational processing the return of goods for the B2C e-commerce transaction. Based on the literature review, pain points in cross-border e-returns were identified. To explore the operational bottlenecks, conducted focus group interview with a small cohort of 45 logistics managers from companies in UAE. A conceptual framework is proposed based on Blockchain technology that would enhance interoperability and digital document transactions, enabling product traceability and transparency. This research contributes to the theoretical and practical literature on cross-border e-commerce, reverse supply chain management, document reversal, re-export issues, and how Blockchain technology's deployment will help the companies overcome the challenges and sustain in a fiercely competitive environment. The survey was conducted with a small sample size, future research can be conducted with a larger sample size and other countries to widen the knowledge of cross-border issues. The conceptual model needs further industrial validation from the practitioners before using it at a large scale to facilitate information collaboration and integration between the key entities in the B2C cross-border reverse supply chain operation.
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing

Millions of mobile apps are available in app stores, such as Apple's App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging app issues. These studies are usually based on topic modeling or clustering techniques. However, the short-length characteristics and sentiment of user reviews have not been considered. In this paper, we propose a novel emerging issue detection approach named MERIT to take into consideration the two aforementioned characteristics. Specifically, we propose an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling topics and corresponding sentiments that takes into consideration app versions. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version, and automatically interpret the meaning of the topics with most relevant phrases and sentences. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22.3 percent in terms of F1-score. In terms of efficiency, MERIT can return results within acceptable time.
A Privacy-Preserving Trust Management System based on Blockchain for Vehicular Networks

Blockchain-based trust management has attracted great attention for vehicular networks due to its decentralized, transparent, and tamper-proof natures. However, the highly dynamic vehicular environment challenges the reliability of trust evaluation as well as the privacy preservation of vehicles against tracking attacks. In this paper, we propose a privacy-preserving trust management system to evaluate the trustworthiness of vehicles by exploiting the recent advanced blockchain techniques. Specifically, we build up a trust evaluation blockchain, where the trustworthiness of an involved vehicle is evaluated by distributed road-side units (RSUs) based on the rating feedback from neighboring vehicles. To enable efficient and privacy-preserving trust evaluation, we deploy the feedback messages aggregation and trust evaluation on two smart contracts, which are executed and verified by distributed RSUs automatically. In particular, identity authentication based on Elliptic Curve Cryptography (ECC) cryptosystem is introduced to prevent privacy leakage of vehicles. Security analysis and performance evaluation reveal that our system is secure and efficient to manage the trust evaluation while guaranteeing privacy-preservation for vehicular networks.
Optimal Design of High-Voltage Disconnecting Switch Drive System Based on ADAMS and Particle Swarm Optimization Algorithm

This paper focuses on the analysis of the stability of the GW17 high-voltage disconnecting switch drive system. Firstly, the optimization model of the disconnector is established, and the simulation analysis is carried out by ADAMS (Automatic Dynamic Analysis of Mechanical Systems) and the simulation results are verified by experiments. Afterwards, ADAMS optimization design and particle swarm optimization algorithm (PSO) are used to optimize the drive system of the disconnector, and the results are verified on the experimental platform. After optimization, the space rod is reduced by 15 mm, the minimum corner angle of the lower conductive rod is reduced by 71.0%, the minimum folding arm angle is reduced by 88.7% and the maximum force of the ball pair is reduced by 35.7%, which realizes the lightweight of the rod, reduces the wear of the ball pair, and improves the stability of the equipment operation.
Intern retrieval from community question answering websites: A new variation of expert finding problem

In recent years, with the advent of new scientific and technical areas, hiring suitable interns has become very important for large companies. In internship programs, to reduce the risk of unsuccessful recruitment, companies are actively seeking those candidates who are ready to become skillful experts, and also incur the least financial costs. Therefore, generalists (i.e. Hyphen-shaped people) are the most suitable candidates for such positions. These candidates have general knowledge in the required skills of the position and do not have expertise in any other area. One of the environments that accurately reflect the knowledge of people is community question answering (CQA). This study is the first that focuses on retrieving interns from CQA websites. It uses the concepts of generalist and shape of expertise to identify suitable candidates for the internship programs of companies. Specifically, in this paper, we define the intern retrieval problem, in which given a set of required skills of an internship position, a ranking of candidates is generated so that the generalists who have general knowledge in those skills are retrieved in top ranks. We propose two retrieval models to address the problem. In order to evaluate the performance of these models, we introduce two specific measures (i.e. coverage and optimality). Our experiments on three test collections extracted from StackOverflow demonstrate the effectiveness of our models in comparison with several baselines.
Differential privacy applied to smart meters

Smart meters and the smart grid will allow utility companies and customers to monitor their electricity and utility usage in fine-grained detail instead of the previously common monthly or yearly measurements. With this fine-grained detail comes serious privacy concerns. One of the most promising solutions for measuring and preserving privacy loss is differential privacy. Both differential privacy and the smart grid are relatively young developments that will require more research before they can be confidently implemented worldwide. With this systematic mapping study, we will provide an overview of the current literature and attempt to determine the future directions the research may take.
Independent Spaces of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>q</mml:mi></mml:math>-Polymatroids

This paper is devoted to the study of independent spaces of q-polymatroids. With the aid of an auxiliary q-matroid it is shown that the collection of independent spaces satisfies the same properties as for q-matroids. However, in contrast to q-matroids, the rank value of an independent space does not agree with its dimension. Nonetheless, the rank values of the independent spaces fully determine the q-polymatroid, and this fact can be exploited to derive a cryptomorphism of q-polymatroids. Finally, the notions of minimal spanning spaces, maximally strongly independent spaces, and bases will be elaborated on.
Examining Interactions Between Community Members and University Safety Organizations through Community-Sourced Risk Systems

An increasing number of safety departments in organizations across the U.S. are offering mobile apps that allow their local community members to report potential risks, such as hazards, suspicious events, ongoing incidents, and crimes. These "community-sourced risk'' systems are designed for the safety departments to take action to prevent or reduce the severity of situations that may harm the community. However, little is known about the actual use of such community-sourced risk systems from the perspective of both community members and the safety departments. This study is the first large-scale empirical analysis of community-sourced risk systems. More specifically, we conducted a comprehensive system log analysis of LiveSafe--a community-sourced risk system--that has been used by more than two hundred universities and colleges. Our findings revealed a mismatch between what the safety departments expected to receive and what their community members actually reported, and identified several factors (e.g., anonymity, organization, and tip type) that were associated with the safety departments' responses to their members' tips. Our findings provide design implications for chatbot-enabled community-risk systems and make practical contributions for safety organizations and practitioners to improve community engagement.
Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation

This work establishes a robocentric framework around a non-linear Model Predictive Control (NMPC) for autonomous navigation of quadrotors in tunnel-like environments. The proposed framework enables obstacle free navigation capabilities for resource constraint platforms in areas with critical challenges including darkness, textureless surfaces as well as areas with self-similar geometries, without any prior knowledge. The core contribution of the proposed framework stems from the merging of perception dynamics in a model-based optimization approach, aligning the vehicles heading to the tunnels’ open space expressed in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> axis coordinate in the image frame of the most distant area. Moreover, the aerial vehicle is considered as a free-flying object that plans its actions using egocentric onboard sensors. The proposed method can be deployed in both fully illuminated indoor corridors or featureless dark tunnels, leveraging visual processing from either RGB-D or monocular sensors for generating direction commands to keep flying in the proper direction. Multiple experimental field trials demonstrate the effectiveness of the proposed method in challenging environments.