Reviewerly/exHarmony huggingface dataset:
Fitting a Chair to a Surgeon’s Body: Mechanism of a Chair for Ophthalmologic Surgeon in an Operating Room

We report the design and testing of a chair to better fit and support an ophthalmologic surgeon during a procedure. The aim of the new chair design is to enhance the surgical performance by reducing physical fatigue. If surgeons are more comfortable, they may make fewer errors, leading to better patient safety. The seat surface is made of fiber-reinforced plastics and molded with a curvature that provides pelvic and side support. The cushioning material is not urethane; instead, it is plastic entwined in a thread structure that can be easily processed. A mechanism that allows the backrest to slide forward and backward allows adjustment for differences in thigh length while enhancing support in the sacral and pelvic regions. The clinical pilot test by surgeons indicated that the prototype chair decreased the pressure on the seat surface and prevented backward tilt of the pelvis compared to a conventional chair. The synergistic effect of the seat surface and backrest allows a stable work posture while minimizing forward displacement.
The People Who Live in Glass Houses Are Happy the Stones Weren't Thrown at Them [From the Editors]

Security problems that superficially seem trivial and obvious are deep, systemic, and neither easily nor quickly solved. The author describes many recent data and security breaches and then argues that these obvious problems are among those that we are failing to solve as a community today. The author then consolidates some of the categories of recommended actions found in the literature.
HScodeNet: Combining Hierarchical Sequential and Global Spatial Information of Text for Commodity HS Code Classification

Commodity Harmonization System (HS) code classification is an important customs procedure in cross-border trade. HS code classification is to identify the category (i.e., HS code) of a commodity according to its description information. In fact, HS code classification is essentially a text classification task. However, compared with general text classification, the challenge of this task is that commodity description texts are organized in special hierarchical structures and contain multiple independent semantic segments. What’s more, the label space (i.e., the HS code system) has hierarchical correlation. In this paper, we propose a HS code classification neural network (HScodeNet) by incorporating the hierarchical sequential and global spatial information of texts, in which a hierarchical sequence learning module is designed to capture the sequential information and a text graph learning module is designed to capture the spatial information of commodity description texts. In addition, a label correlation loss function is presented to train the model. Extensive experiments on several real-world customs commodity datasets show the superiority of our HScodeNet model.
Fast Evaluation and Approximation of the Gauss-Newton Hessian Matrix for the Multilayer Perceptron

We introduce a fast algorithm for entry-wise evaluation of the Gauss-NewtonHessian (GNH) matrix for the multilayer perceptron. The algorithm has aprecomputation step and a sampling step. While it generally requires $O(Nn)$work to compute an entry (and the entire column) in the GNH matrix for a neuralnetwork with $N$ parameters and $n$ data points, our fast sampling algorithmreduces the cost to $O(n+d/\epsilon^2)$ work, where $d$ is the output dimensionof the network and $\epsilon$ is a prescribed accuracy (independent of $N$).One application of our algorithm is constructing the hierarchical-matrix(\hmatrix{}) approximation of the GNH matrix for solving linear systems andeigenvalue problems. While it generally requires $O(N^2)$ memory and $O(N^3)$work to store and factorize the GNH matrix, respectively. The \hmatrix{}approximation requires only $\bigO(N r_o)$ memory footprint and $\bigO(Nr_o^2)$ work to be factorized, where $r_o \ll N$ is the maximum rank ofoff-diagonal blocks in the GNH matrix. We demonstrate the performance of ourfast algorithm and the \hmatrix{} approximation on classification andautoencoder neural networks.
Quick Start with AI for Automotive Development: Five Process Changes and One New Process

Artificial Intelligence offers great potential for automotive development because it offers the opportunity to develop new or enhanced functionalities. To make that a reality, automotive development processes and methods need to be enhanced. In this paper we show how inductive learning instead of programming of functions changes development and how the requirements for this need be expanded. Furthermore, there is a need for a specific AI test strategy and test execution. We will also argue the need to further reduce the duration of development cycles and to increase the flexibility of resource allocation. In addition, the provision of data becomes a basic building block of successful AI development, which needs to be planned and managed properly. All these changes can become severe challenges. Best practices and experiences will be used in a reference model for the quick and detailed assessment of AI based automotive development. This way, the necessary changes can be implemented and mastered much faster and the benefits from AI through can be utilized much earlier.
Trusted computing in power distribution IoT: A fuzzy set theory based analysis

As emerging technologies in information security protection, trusted computing and blockchain can guarantee a safe and reliable operating environment and management mechanism in the Power Distribution IoT. Relying on the comprehensive evaluation algorithm of fuzzy mathematics set theory in this paper, a blockchain-based distributed decision-making and collaborative autonomy model for the power distribution IoT is built, and the “manageable and controllable, precise protection, visible and credible, and smart defense” security protection model is established to carry out trusted computing and privacy protection of computer blockchain IoT nodes, and the information security defense level of the distribution network is improved comprehensively.
Research and Application of the Key Technology of Cloud Platform in Various Fields of Computer Internet of Things Technology

In the current rapid development of Internet technology, the application of various new technologies and new concepts are more common. The effective connection of different types of technologies and the integration of technologies can form a unified whole and promote the close integration between people and things. The current Internet of Things technology has a wide range of applications. In order to be able to research and analyze the actual application of the Internet of Things technology in various fields, we need to explore the specific applications of the Internet of Things in various fields and the key technologies of the Internet of Things. This is of positive significance to promote the further development of the Internet of Things.
Multipartite entanglement purification using time-bin entanglement

Abstract Multipartite entanglement has been widely employed in quantum information processing. However, the unavoidable noise in quantum channels causes the maximally multipartite entangled state to degrade to the mixed state. In this paper, we propose a highly efficient multipartite entanglement purification protocol (MEPP) for the polarization Greenberger–Horne–Zeilinger state. Unlike conventional MEPPs, where two pairs of polluted entanglement are usually required, our protocol only requires one noisy copy of polarization-time-bin hyperentanglement and exploits the time-bin entanglement to purify the polarization entanglement. This feature increases the efficiency of our MEPP. Our MEPP may have potential applications in long-distance quantum communication.
Intelligent Automatic Processing of Sports Image Based on Deep Learning Algorithm

With the continuous progress of science and technology, artificial intelligence has been applied to all aspects of life, and deep learning is closely connected with artificial intelligence. Therefore, the places where artificial intelligence is applied in all walks of life have the shadow of deep learning. Sports image is a kind of video which needs continuous motion. Behavior recognition is to analyze and classify the behavior in the video. This paper analyzes the detection of athletes' sports video, which refers to the identification of athletes' behavior. Therefore, based on the existing behavior detection and the extraction ability of deep learning, this paper uses the improved convolutional neural network (T-TP3DResNet) to study the behavior recognition. The experimental results show that the accuracy of the improved convolutional neural network is 89.9% compared with other behavior recognition algorithms.
CLONE: Collaborative Ontology Editor as a Service in the Cloud

The evolution of Web and cloud services technology has facilitated collaboration on the Web, providing the means for concurrent editing, change tracking and storing files in the cloud (e.g. Google Docs, Office 365). Ontology development teams could greatly benefit from this technology, that until now have been applied mainly to document processing. We introduce CLONE, a Web-based ontology editor that runs in the cloud and provides a real-time collaborative environment for creating and editing ontologies. CLONE is designed as a service-oriented architecture taking advantages of the easy extensibility and scalability features of this approach. CLONE provides all the essential features of stand-alone ontology editors, as well as significant collaboration features, including concurrent editing, editing history, team conversations and role-based access-authorization mechanisms.
The Application of Knowledge graph Technology in Commercial Bank Customer Association Risk Early Warning System

This technology mainly uses the relationship between guarantees and capital flows between enterprises. This technology uses knowledge graphs as the core technology, takes the customer group as the risk carrier, and focuses on the risk-related transmission. It designs a transmission method for customer default events. It can help banks better and more accurately locate potential risks of customers in advance.
Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features

During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the ‘scale-invariant features transform’ (SIFT), ‘histogram of oriented gradient’ (HOG) and ‘global invariant features transform’ (GIST) descriptors and classifies the activities by means of the deep belief network (DBN). First, in order to avoid ineffective features, a pre-processing course is performed on any image in the dataset. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of features, a small and distinguishing feature set is produced using the bag of words (BoW) technique. Finally, these reduced features are given to a deep belief network in order to recognize the human activities. Comparing the simulation results of the proposed approach with some other existing methods applied to the standard PASCAL VOC Challenge 2010 database with nine different activities demonstrates an improvement in the accuracy, precision and recall measures (reaching 96.39%, 85.77% and 86.72% respectively) for the approach of this work with respect to the other compared ones in the human activity recognition.
Scared, frustrated and quietly proud: Testers’ lived experience of tools and automation

Software testing is vital, expensive, time-consuming yet a necessary part of software development. Testers perform repeated actions during testing, where automation and tools could reduce costs, timescale and human error. However, challenges to tools adoption have been identified in academic research and industry, which are blockers to success with automation. In attempting to find whether testers were experiencing tool usability shortcomings, we followed an exploratory research path, collecting stories from over 100 test practitioners. We discovered a richer, more complex story than we expected. We realised that usability – while necessary – is not sufficient to enable success, and that other human factors challenge successful automation projects. In answering privately to questions about their experiences of tools and automation, testers expressed themselves in language that was more emotional and linked to their lived experience (LX) than we expected. We uncovered frustrations and fear, as well as pride. In this paper we present our findings so far about TX: The testers’ lived experience of tools and automation, and we suggest steps for future research.
Point-Based Grading System: Advantages and Disadvantages

One of the vital issues of modern education is assessing the level of students’ knowledge using a point-based grading system. Various approaches to definition of this phenomenon have been examined, and based on their analysis, the authors have come to the conclusion that the use of PBGS solves several important problems. First, it helps to evaluate students’ knowledge more accurately by increasing the maximum possible number of points. Second, it provides a possibility to evaluate individual work activities. Third, it encourages students to work hard to master new material throughout the semester. The authors have also scrutinized the disadvantages of this knowledge assessment system. These include: the risk of a biased judgment; fostering individualism when everyone is interested only in his/her personal growth; dominance of the written test over the oral answer; and the use of test tasks, which allows one to determine only the initial conceptual level of the subject without revealing the problematic, conceptual depth of knowledge, without identifying creative minds who can think outside the box. Then, through a mental experiment, various pedagogical situations have been assessed in which a formal use of a point-based grading system leads to biased results. The article proposes specific measures to improve the point-based grading system, so as to achieve a more objective assessment of competence level in students of higher educational institutions: 1) at least 50% of the total score should be planned for the final test event; 2) the final certification should include a small number of questions, which will allow, on the one hand, to save students’ time and effort, and, on the other, to master the material in depth; 3) dual accounting should be helpful – of the total number of points in the PBGS and of the final certification grades in the subject; 4) it is necessary to provide for an option to “transfer” points from one kind of work to another. In closing, the authors conclude that a point-based grading system has its own advantages and disadvantages, and, adhering to the above rules, it is possible to strengthen the former and weaken the latter. However, it should be emphasized that a complete switch to the PBGS and break with the traditional methods of testing knowledge seem inappropriate.
Constitutionalism During a Crisis: The Case of Aarogya Setu

Aarogya Setu is a contact tracing app launched by the Indian government on April 2, 2020, as a tool to combat the COVID-19 crisis. Issues concerning the privacy and security concerns with the app have been discussed extensively. In this short piece, we focus on the issue of lack of legislative foundations and certain practical governance oriented considerations related to the roll out of the app. We begin by considering the principles of evaluating executive action during a crisis and whether extraordinary times truly call for extraordinary measures. We then explore the importance of a clear and specific law and why the Disaster Management Act or Section 144, CrPC fail to provide an adequate legal foundation for the app. We then consider the importance of law and process of legislation and provide certain recommendations on addressing the procedural irregularities and governance related issues that were related to the roll out of the app.
HAIF: A Hierarchical Attention-Based Model of Filtering Invalid Webpage

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.
Access to Secure Ballot Drop-off Locations in Texas

Safe access to voting during the Covid-19 pandemic was a concern across the US. In Texas, a push for mail-in voting during the November 2020 presidential election was significantly scaled back when the governor restricted each county to one secure ballot drop-off location. We evaluate the effect of this change, finding that it increased travel times to drop-off locations for drivers and public transit users by 18 and 32 minutes, respectively, in the most affected county. When evaluated across the state, the policy had a disproportionate effect on eligible Asian, Black, and Latinx voters.
Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning

This paper describes experiments undertaken and their results as part of the BioNLP MEDIQA 2021 challenge. We participated in Task 3: Radiology Report Summarization. Multiple runs were submitted for evaluation, from solutions leveraging transfer learning from pre-trained transformer models, which were then fine tuned on a subset of MIMIC-CXR, for abstractive report summarization. The task was evaluated using ROUGE and our best performing system obtained a ROUGE-2 score of 0.392.
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks

First-order meta-learning algorithms have been widely used in practice to learn initial model parameters that can be quickly adapted to new tasks due to their efficiency and effectiveness.However, existing studies find that meta-learner can overfit to some specific adaptation when we have heterogeneous tasks, leading to significantly degraded performance.In Natural Language Processing (NLP) applications, datasets are often diverse and each task has its unique characteristics.Therefore, to address the overfitting issue when applying firstorder meta-learning to NLP applications, we propose to reduce the variance of the gradient estimator used in task adaptation.To this end, we develop a variance-reduced first-order meta-learning algorithm.The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation.Experiments on two NLP applications: few-shot text classification and multi-domain dialog state tracking demonstrate the superior performance of our proposed method.
Antenna Learning And Research For Students And Young Professionals In The Post-COVID-19 Era [Young Professionals]

The post-COVID-19 era has brought in some unique opportunities to the global community of students and YPs, especially in the AP domain. The shift toward online mechanisms can foster an atmosphere for interactive and visually rich learning as well as globally collaborative research on antennas and applied electromagnetics. Although traveling and conference visits will possibly resume in future with increased vaccinations down the line, virtual work-shops/webinars can be held on a bigger scale, empowering and benefiting students and YPs.
Performance Evaluation of Distributed Systems in Multiple Clouds using Docker Swarm

The design of distributed systems in multiple clouds have been gaining popularity due to various benefits of the multi-cloud infrastructure such as minimizing vendor lock-in, data loss and downtime. Nonetheless, this multi-cloud infrastructure also poses several challenges such as compatibility, interoperability, complex provisioning and configuration due to the variation in technologies and services of each cloud provider. Consequently, it is a tedious task to design distributed systems in multiple clouds. Virtualization is regarded as the base technology of the cloud and therefore, most cloud-based distributed systems are based on it. Nevertheless, virtual machines require substantial resources and cause several issues across multiple clouds such as provisioning, configuration management, load balancing and migration. Docker Swarm is a container-based clustering tool that resolves some of these issues and supports the design of distributed systems in multiple clouds. It has also incorporated several inbuilt attributes of the distributed system, however, it is still evolving. This paper initially presents the simulated development of a Docker Swarm-based distributed system which can be easily replicated in multiple clouds. Subsequently, based on the simulated Docker Swarm-based distributed system, it performs an evaluation of several attributes of this distributed system such as high availability and fault tolerance; automatic scalability, load balancing and maintainability of services; and scalability of large clusters.
Towards event-driven decentralized marketplaces on the blockchain

Blockchains have become a popular technology for lowering the trust-tax burden between transacting parties that cannot necessarily trust each other. They are used as substitutes for the centralized authorities typically incorporated in transactional workflows to perform verification tasks and have the advantage of being objective and incorruptible. For applications such as supply chain marketplaces, auxilliary functionalities beyond the core blockchain roles of recording and validating transactions such as event detection are important for enabling application participants be responsive to business conditions. Unfortunately, existing blockchain event frameworks are immature, syntactic, inflexible and not expressive enough for many application needs.
Light field illumination: Problem-specific lighting adjustment

Abstract Choosing a proper lighting approach is a crucial task in designing visual inspection systems. It becomes especially challenging for complex-shaped, transparent objects, which change the directional distribution of incoming light in various ways. We overcome this challenge by constructing a light field display and deploy it as a highly tunable lighting device. Thereby, an object-specific light field can be generated, which highlights the features of the object under test with maximum contrast. We explain the calibration procedure, the rendering pipeline and present examples of customized illuminations.