Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Bugs in COVID-19 Software Projects

The dire consequences of the COVID-19 pandemic have influenced development of COVID-19 software i.e., software used for analysis and mitigation of COVID-19. Bugs in COVID-19 software can be consequential, as COVID-19 software projects can impact public health policy and user data privacy. The goal of this paper is to help practitioners and researchers improve the quality of COVID-19 software through an empirical study of open source software projects related to COVID-19. We use 129 open source COVID-19 software projects hosted on GitHub to conduct our empirical study. Next, we apply qualitative analysis on 550 bug reports from the collected projects to identify bug categories. We identify 8 bug categories, which include data bugs i.e., bugs that occur during mining and storage of COVID-19 data. The identified bug categories appear for 7 categories of software projects including (i) projects that use statistical modeling to perform predictions related to COVID-19, and (ii) medical equipment software that are used to design and implement medical equipment, such as ventilators. Based on our findings, we advocate for robust statistical model construction through better synergies between data science practitioners and public health experts. Existence of security bugs in user tracking software necessitates development of tools that will detect data privacy violations and security weaknesses.
Self-Supervised Audio-Visual Feature Learning for Single-Modal Incremental Terrain Type Clustering

The key to an accurate understanding of terrain is to extract the informative features from the multi-modal data obtained from different devices. Sensors, such as RGB cameras, depth sensors, vibration sensors, and microphones, are used as the multi-modal data. Many studies have explored ways to use them, especially in the robotics field. Some papers have successfully introduced single-modal or multi-modal methods. However, in practice, robots can be faced with extreme conditions; microphones do not work well in crowded scenes, and an RGB camera cannot capture terrains well in the dark. In this paper, we present a novel framework using the multi-modal variational autoencoder and the Gaussian mixture model clustering algorithm on image data and audio data for terrain type clustering by forcing the features to be closer together in the feature space. Our method enables the terrain type clustering even if one of the modalities (either image or audio) is missing at the test-time. We evaluated the clustering accuracy with a conventional multi-modal terrain type clustering method and we conducted ablation studies to show the effectiveness of our approach.
Id Based Adaptive-Key Signcryption for Data Security in Cloud Environment

Towards assurance of the accuracy of public keys, the customary public-key cryptography (PKC) needs records to be retained through a certification authority (CA). Though in case important safety services, the accepted PKC agonizes since the organization of substructure associate records. An identity-based signcryption (IBS) scheme is a cryptographic technique which provides both confidentiality and authenticity. However, the IBS agonizes since the key contact difficult, that one of the unembellished refuge intimidations to authentic cryptographic methods above the days. Hence, in this paper we have designed a secure ID based Adaptive Key Signcryption (IBAKS) method for securing the confidential data to be stored on cloud computing. The important knowledge of future IBAKS deceits in apprising the private key adaptively based on the time period, although keeping the similar public key. Also, the encryption method utilizes the triple key DES for enabling higher security. The proposed methodology will be implemented in the working platform of JAVA and the performance outcomes were analyzed.
$K$-Shot Contrastive Learning of Visual Features With Multiple Instance Augmentations

In this paper, we propose the K-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances. It aims to combine the advantages of inter-instance discrimination by learning discriminative features to distinguish between different instances, as well as intra-instance variations by matching queries against the variants of augmented samples over instances. Particularly, for each instance, it constructs an instance subspace to model the configuration of how the significant factors of variations in K-shot augmentations can be combined to form the variants of augmentations. Given a query, the most relevant variant of instances is then retrieved by projecting the query onto their subspaces to predict the positive instance class. This generalizes the existing contrastive learning that can be viewed as a special one-shot case. An eigenvalue decomposition is performed to configure instance subspaces, and the embedding network can be trained end-to-end through the differentiable subspace configuration. Experiment results demonstrate the proposed K-shot contrastive learning achieves superior performances to the state-of-the-art unsupervised methods.
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo technique. We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.
[DC] Glanceable AR: Towards an Always-on Augmented Reality Future

Augmented Reality (AR) glasses are widely believed to become the next-generation personal computing platform. They could be worn all-day, displaying everyday information continuously and pervasively to users. However, design and validation challenges remain unaddressed to resolve distractions, occlusions, and privacy concerns of applying AR in all sorts of everyday scenarios. This paper proposes a research agenda that aims at addressing these challenges to design for an always-on yet unobtrusive AR future.
Bluejay: A Cross-Tooling Audit Framework For Agile Software Teams

Agile software teams are expected to follow a number of specific Team Practices (TPs) during each iteration, such as estimating the effort ("points") required to complete user stories and coordinating the management of the codebase with the delivery of features. For software engineering instructors trying to teach such TPs to student teams, manually auditing teams if teams are following the TPs and improving over time is tedious, time-consuming and error-prone. It is even more difficult when those TPs involve two or more tools. For example, starting work on a feature in a project-management tool such as Pivotal Tracker should usually be followed relatively quickly by the creation of a feature branch on GitHub. Merging a feature branch on GitHub should usually be followed relatively quickly by deploying the new feature to a staging server for customer feedback. Few systems are designed specifically to audit such TPs, and existing ones, as far as we know, are limited to a single specific tool. We present Bluejay, an open-source extensible platform that uses the APIs of multiple tools to collect raw data, synthesize it into TP measurements, and present dashboards to audit the TPs. A key insight in Bluejay's design is that TPs can be expressed in terminology similar to that used for modeling and auditing Service Level Agreement (SLA) compliance. Bluejay therefore builds on mature tools used in that ecosystem and adapts them for describing, auditing, and reporting on TPs. Bluejay currently consumes data from five different widely-used development tools, and can be customized by connecting it to any service with a REST API. Video showcase available at governify.io/showcase/bluejay
Decomposition of rock micro-fracture signals based on a singular value empirical mode decomposition algorithm

Rock burst early warning technology is currently applied mainly in microseismic monitoring. Rock burst signals indicate the micro-fracture phenomena of a rock and can transmit earthquake waves through the rock before they are finally received by a detector. A characteristic decomposition of rock micro-fracture signals was conducted by the singular value Empirical Mode Decomposition (EMD) algorithm to effectively decompose the characteristic signals of a rock micro-fracture from mixed microseismic signals, with a low signal to noise ratio to ensure prediction precision. When comparing the proposed method with wavelet decomposition and EMD, it was found that the local characteristics of the signals were retained effectively. The proposed algorithm was verified by applying it in a laboratory simulation and to the decomposition of microseismic signals from a hydro-power station. It was concluded that the improved algorithm had a better decomposition precision than wavelet decomposition and EMD decomposition and could effectively separate the characteristic signals of micro-earthquakes. This could provide a significant basis for the identification of the abnormal microseismic signals of rock micro-fractures as well as a pre-warning of rock fractures. It is therefore of practical significance to study rock fracture early warning technology.
Artificial intelligence in pathology and laboratory medicine

Increased healthcare demand has placed pressure on laboratory medicine to improve turnover and optimise efficiency using digitalisation, automation and artificial intelligence (AI). This will bring new challenges for the clinical laboratory. Laboratorians need to understand the utility of AI, its limitations and implementation. The management of big data requires ready access and accurate and contextual analysis. AI uses complex algorithms and data from medical and laboratory data to mimic human analysis and this requires accurate and reliable data. The role of AI in laboratory medicine is rapidly expanding owing to recognition of its potential to improve detection, laboratory workflows, decision support and reduce costs and increase efficiency. In this thematic issue on AI, we have attempted to present an overview of the uses of AI in laboratory medicine and advances in the digitalisation of pathology.Rakha et al 1 from the University of Nottingham and Google Health provide an erudite overview of current and future applications of AI in pathology. The applications of AI in pathology range from prognostic/predictive applications to workflow and diagnostic …
Education and Music Culture in the Context of Web 2.0

Total digitalization of culture requires the extensive integration of Web 2.0 ser-vices and capabilities into music education. The penetration of digital technologies is not reduced to the technologies for fixing and distributing musical works or the means of their broadcasting but also dramatically transforms the process of creat-ing musical works. Web 2.0 offers a new distributed creativity culture and pro-vides musicians with specific opportunities to work and distribute creative prod-ucts. The study is devoted to the integration of music education and the services and opportunities that Web 2.0 provides to the mass audience (free specialized and non-specialized services), its impact on the quality of music education, as well as on the most important psychological parameters of students of music uni-versities. The study involved 150 students of R. Glier Kyiv Municipal Academy of Music (Kyiv, Ukraine); there were three groups of 50 participants: two exper-imental groups and a control one. In Experimental Group 1, training was partially integrated with Web 2.0 services; in Experimental Group 2, there was a more complete and structured integration. Based on the results of semester training and the objective test scores, a significant increase in the results of experimental groups was observed (an increase by 1.3 points in Group 1 and 2.84 points in Group 2 on a 5-point scale); Group 2 demonstrated a more significant improve-ment. The subjective assessment of the level of involvement, motivation and anx-iety showed a statistically significant decrease in academic anxiety in the experi-mental groups (2.99 and 2.01 compared to 3.98 in the control group, respective-ly) and a statistically significant increase in motivation and involvement in the group with deep integration of Web 2.0 services (Experimental Group 2). The hypothesis of the study that deep integration of Web 2.0 services and capabilities into music education will increase objective academic results, motivation and stu-dent involvement has been fully confirmed. It has also been revealed that it helps students reduce academic anxiety. The research results can be used as a basis for further research and in the course of the development of music education curricu-la based on online learning, mobile learning and the integration of music learning into a digital environment.
From AR to AI: Augmentation Technology for Intelligent Surgery and Medical Treatments

With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more information beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly determine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly deformable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency.
FORMATION OF ETHICAL CULTURE OF INTENDING EDUCATORS IN THE EDUCATIONAL PROCESS OF HIGHER EDUCATION INSTITUTION

The study intends to expand a methodology of shaping intending educators’ ethical culture. The examples of effective techniques that were used in a teacher-training process are provided. Some samples of authentic pedagogical cases created by teachers and students are presented. The main stages of work on the formulation of the “Teacher’s Code of Ethics” are revealed. Such interaction methods as “Brain Storming”, “Decision Tree” are given. Fragments of the lesson “Pedagogical techniques in the structure of pedagogical communication of a teacher” are described, where students analyze videos on pedagogical topics, identify phrases that violate the ethical norms of the teaching profession and choose alternatives.The excerpts from the works of intending educators, where students reflect on the teachers’ functions and responsibilities, are analysed. A structured set of traditional (lectures, tutorials, laboratory classes) and innovative (brainstorming, role-playing, case studies, decision tree, pedagogical marathon, etc.) methods, which accelerate students’ gradual progress to the highest creative level of ethic culture is presented. Such organization of the educational process reflects the developmental changes from intuitive actions in different situations on the basis of initial ethics knowledge to the willingness of intending educators to work independently, guided by high-minded principles and norms in professional situations.
Light-Field Imaging Reconstruction Using Deep Learning Enabling Intelligent Autonomous Transportation System

Light-field (LF) cameras, also known as plenoptic cameras, permit the recording of the 4D LF distribution of target scenes. However, many times, surface errors of a microlens array (MLA) are responsible for degradation in the images captured by a plenoptic camera. Additionally, the limited pixel count of the sensor can cause missing parallax information. The aforementioned issues are crucial for creating accurate maps for Intelligent Autonomous Transport System (IATS), because they cause loss of LF information, and need to be addressed. To tackle this problem, a learning-based framework by directly simulating the LF distribution is proposed. A high-dimensional convolution layer with densely sampled LFs in 4D space and considering a soft activation function based on ReLU segmentation correction is used to generate a superresolution (SR) LF image, improving the convergence rate in the deep learning network. Experimental results show that our proposed LF image reconstruction framework outperforms the existing state-of-the-art approaches; specifically, it is effective for learning the LF distribution and generating high-quality LF images. Different image quality assessment methods are used to evaluate the performance of the proposed framework, such as PSNR, SSIM, IWSSIM, FSIM, GFM, MDFM, and HDR-VDP. Additionally, the computational efficiency was evaluated in terms of number of parameters and FLOPs, and experimental results demonstrated that our proposed framework reached the highest performance in most of the datasets used.
PoTS: Proof of Tunnel Signature for Certificate Based on Blockchain Technology

Proof of Tunnel Signature (PoTS) is designed to avoid the main problems found in certificates based on Blockchain technology. In this case, it is so closely related to Cybersecurity. A lightweight protocol such as a Certificate Authenticated Key Agreement (CAKA) is needed to reduce the vulnerability of a system's operation, namely overcoming management overhead by using a decentralized system according to the characteristics of Blockchain Technology. PoTS is the second stage after determining the Key Agreement (KA) or certificate hash in authenticating a node, and this is also a significant step in minimizing computation costs. The nodes generated after the signing process remain anonymous and can be verified optimally. Smart contracts are also used as a support so that this research can ensure transparency and openness of transaction nodes to maintain and improve the efficiency of transaction security for a certificate based on Blockchain Technology.
Automatic Interlinear Glossing for Otomi language

In linguistics, interlinear glossing is an essential procedure for analyzing the morphology of languages.This type of annotation is useful for language documentation, and it can also provide valuable data for NLP applications.We perform automatic glossing for Otomi, an under-resourced language.Our work also comprises the pre-processing and annotation of the corpus.We implement different sequential labelers.CRF models represented an efficient and good solution for our task (accuracy above 90%).Two main observations emerged from our work: 1) models with a higher number of parameters (RNNs) performed worse in our lowresource scenario; and 2) the information encoded in the CRF feature function plays an important role in the prediction of labels; however, even in cases where POS tags are not available it is still possible to achieve competitive results.
Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform

We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling mobility platforms by decoding how the user looks at the environment to understand where they want to navigate their mobility device. However, many natural eye-movements are not relevant for action intention decoding, only some are, which places a challenge on decoding, the so-called Midas Touch Problem. Here, we present a new solution, consisting of 1. deep computer vision to understand what object a user is looking at in their field of view, with 2. an analysis of where on the object's bounding box the user is looking, to 3. use a simple machine learning classifier to determine whether the overt visual attention on the object is predictive of a navigation intention to that object. Our decoding system ultimately determines whether the user wants to drive to e.g., a door or just looks at it. Crucially, we find that when users look at an object and imagine they were moving towards it, the resulting eye-movements from this motor imagery (akin to neural interfaces) remain decodable. Once a driving intention and thus also the location is detected our system instructs our autonomous wheelchair platform, the A.Eye-Drive, to navigate to the desired object while avoiding static and moving obstacles. Thus, for navigation purposes, we have realised a cognitive-level human interface, as it requires the user only to cognitively interact with the desired goal, not to continuously steer their wheelchair to the target (low-level human interfacing).
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition

We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for classification and sequence labelling) to jointly extract dialogue states.Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images

Most existing research on visual question answering (VQA) is limited to information explicitly present in an image or a video.In this paper, we take visual understanding to a higher level where systems are challenged to answer questions that involve mentally simulating the hypothetical consequences of performing specific actions in a given scenario.Towards that end, we formulate a vision-language question answering task based on the CLEVR (Johnson et al., 2017a) dataset.Wethen modify the best existing VQA methods and propose baseline solvers for this task.Finally, we motivate the development of better vision-language models by providing insights about the capability of diverse architectures to perform joint reasoning over image-text modality 1 .'change_attr(<A>,filter_size(<Z>,filter