Reviewerly/exHarmony huggingface dataset:
The governance of personal data for COVID-19 response: perspective from the Access to COVID-19 Tools Accelerator

COVID-19 is the world’s first digital pandemic. Digital tools and technologies have been developed to track and trace the spread of the virus, screen for infection, and the pandemic has accelerated the use of digital technology in the delivery of healthcare. The continued development of these tools and technologies, the monitoring of the virus and the development of new tests, treatments and vaccines are dependent on the collection of and access to vast amounts of personal data. This includes clinical data, epidemiological data and public health data that may be collected from laboratories, medical records, wearables and smartphone apps. Previous public health emergencies (PHEs) have demonstrated the importance in making this data available, and early in the COVID-19 pandemic, there were calls for making all kinds of data, including clinical trial data, routine surveillance data, genetic sequencing, and data on the ongoing monitoring of disease control programmes, openly and rapidly available. As part of this, personal data on age, race, sex, health, ethnic group, and socioeconomic factors have been shared. This has helped led to the rapid development of COVID-19 interventions. It has also enabled the better understanding of factors contributing to difference in infection rates and effectiveness of tests, treatments, and vaccines. However, the use of this particularly sensitive data can infringe upon individual and group privacy, increase the risks of individual and group stigma and discrimination, and it may negatively impact already vulnerable, marginalised or minority populations. [...]
A Survey of Models for Constructing Text Features to Classify Texts in Natural Language

In this survey we systematize the state-of-the-art features that are used to model texts for text classification tasks: topical and sentiment classification, authorship attribution, style detection, etc. We classify text models into three categories: standard models that use popular features, linguistic models that apply complex linguistic features, and modern universal models that combine deep neural networks with text graphs or language models. For each category we describe particular models and their adaptations, note the most effective solutions, summarize advantages, disadvantages and limitations, and make suggestions for future research.
On tensor products of matrix factorizations

Let K be a field. Let f∈K[[x1,...,xr]] and g∈K[[y1,...,ys]] be nonzero elements. If X (resp. Y) is a matrix factorization of f (resp. g), Yoshino had constructed a tensor product (of matrix factorizations) ⊗ˆ such that X⊗ˆY is a matrix factorization of f+g∈K[[x1,...,xr,y1,...,ys]]. In this paper, we propose a bifunctorial operation ⊗˜ and its variant ⊗˜′ such that X⊗˜Y and X⊗˜′Y are two different matrix factorizations of fg∈K[[x1,...,xr,y1,...,ys]]. We call ⊗˜ the multiplicative tensor product of X and Y. Several properties of ⊗˜ are proved. Moreover, we find three functorial variants of Yoshino's tensor product ⊗ˆ. Then, ⊗˜ (or its variant) is used in conjunction with ⊗ˆ (or any of its variants) to give an improved version of the standard algorithm for factoring polynomials using matrices on the class of summand-reducible polynomials defined in this paper. Our algorithm produces matrix factors whose size is at most one half the size one obtains using the standard method.
Tackling SAR Imagery Ship Classification Imbalance via Deep Convolutional Generative Adversarial Network

Shipping constitutes the majority of the world trade, and Synthetic Aperture Radar (SAR) imagery is the primary involuntary all-condition ship monitoring and classification approach. However, large SAR datasets for deep learning are difficult to curate, usually leading to imbalanced classes. Herein, conventional methods such as weighted cost function or over-sampling are shown to be insufficient for our application. Therefore, we propose to utilize a Deep Convolutional Generative Adversarial Network (DCGAN) to be trained on the minority class, and generate new SAR chips to balance the training dataset. A case study is consequently presented that utilizes our methodology, and a base classifier is devised to evaluate its performance. The investigation of the classification metrics confirms that DCGAN is an effective alternative for tackling imbalanced ship SAR data for deep learning applications.
Upsampling Matters for Road Marking Segmentation of Autonomous Driving

Although autonomous driving have become applicable to the industry, the prevalent application of key techniques to the autonomous vehicles still needs to be refined. For instance, how to fast and accurately segment road markings in order to assist the next pedestrian path prediction and the creation of high-definition (HD) map respectively is useful for autonomous driving to be more practical. Current road marking segmentation mainly rely on the techniques of semantic segmentation of computer vision with encoder-decoder architecture. However, as demonstrated in this paper, the upsampling layer of convolutional neural networks with encoder-decoder architecture plays a significant role in the efficiency and accuracy of the road marking segmentation. The bilinear upsampling layer is fast due to its intrinsic simple interpolation but with less accuracy; on the contrary, the upsampling layer with offsets is relatively accurate but with more computational cost. Therefore, at least, in terms of prevalent application, efficiency, and accuracy, the upsampling layer of decoder of convolution neural networks should be paid more attention to for the next research work of autonomous driving.
The US Department of Justice stumbles on visual perception

A large and highly valuable category of forensic evidence consists of patterned impressions created during the perpetration of a crime. These crime scene artifacts, such as fingerprints or tire tracks, offer visual sensory information that is assessed by trained human observers and compared to sensory experiences elicited by model patterns that would have been produced under a hypothesized set of conditions. By means of this "forensic feature comparison," the observer makes a judgment about whether the evidence and the model are sufficiently similar to support common origin. In light of documented failures of this approach, significant concerns have been raised about its scientific validity. In response to these concerns, the US Department of Justice has made assertions about how forensic examiners perform feature comparison tasks that are not consistent with modern scientific understanding of the processes of sensation and perception. Clarification of these processes highlights new ways of thinking about and improving the accuracy of forensic feature comparison and underscores the vital role of science in achieving justice.
Answer Similarity Analysis at the Group Level

Answer similarity indices were developed to detect pairs of test takers who may have worked together on an exam or instances in which one test taker copied from another. For any pair of test takers, an answer similarity index can be used to estimate the probability that the pair would exhibit the observed response similarity or a greater degree of similarity under the assumption that the test takers worked independently. To identify groups of test takers with unusually similar response patterns, Wollack and Maynes suggested conducting cluster analysis using probabilities obtained from an answer similarity index as measures of distance. However, interpretation of results at the cluster level can be challenging because the method is sensitive to the choice of clustering procedure and only enables probabilistic statements about pairwise relationships. This article addresses these challenges by presenting a statistical test that can be applied to clusters of examinees rather than pairs. The method is illustrated with both simulated and real data.
Zero-Keep Filter Pruning for Energy/Power Efficient Deep Neural Networks

Recent deep learning models succeed in achieving high accuracy and fast inference time, but they require high-performance computing resources because they have a large number of parameters. However, not all systems have high-performance hardware. Sometimes, a deep learning model needs to be run on edge devices such as IoT devices or smartphones. On edge devices, however, limited computing resources are available and the amount of computation must be reduced to launch the deep learning models. Pruning is one of the well-known approaches for deriving light-weight models by eliminating weights, channels or filters. In this work, we propose “zero-keep filter pruning” for energy-efficient deep neural networks. The proposed method maximizes the number of zero elements in filters by replacing small values with zero and pruning the filter that has the lowest number of zeros. In the conventional approach, the filters that have the highest number of zeros are generally pruned. As a result, through this zero-keep filter pruning, we can have the filters that have many zeros in a model. We compared the results of the proposed method with the random filter pruning and proved that our method shows better performance with many fewer non-zero elements with a marginal drop in accuracy. Finally, we discuss a possible multiplier architecture, zero-skip multiplier circuit, which skips the multiplications with zero to accelerate and reduce energy consumption.
Context Aggregation Network For Semantic Labeling In Histopathology Images

Digital histopathology using deep learning segmentation models has seen a significant improvement towards aiding pathologists. For reliable deployment, it is essential that the model accurately identifies the object boundaries between benign and malignant regions. A pathologist performs this challenging task using context to extract complex features from a Whole Slide Image (WSI). We leverage this idea and propose a model that combines multiscale features in the encoder and the decoder stages. We show that the proposed architecture beats the baseline by a large margin and achieves a performance improvement over state-of-the-art segmentation architecture using a significantly fewer number of parameters.
Research on Unexpected DNS Response from Open DNS Resolvers

Abstract As the backbone of the domain name system (DNS), DNS resolvers are essential to the Internet. Nowadays, the measurement of DNS resolvers, especially open DNS resolvers, has become a research focus. Previous research works show that DNS responses returned from some open DNS resolvers are not expected for clients and the Internet. We call these DNS responses ‘unexpected DNS responses’. Research on unexpected DNS responses is beneficial to the research, usage and management of open DNS resolvers. This paper explores unexpected DNS responses returned from open DNS resolvers in terms of identification and classification to better understand the behaviours of open DNS resolvers. First, an identification method is proposed to identify all kinds of DNS responses from each section of DNS messages. Second, a classification method is proposed to classify unexpected DNS responses by their influences on clients and the Internet. Furthermore, an efficient identification and classification method is proposed to simplify the above process. Among about 9 million responding open DNS resolvers in the experiments on the IPv4 address space, about 40% return unexpected DNS responses. Experimental results show that the proposed methods can identify and classify all kinds of DNS responses returned from open DNS resolvers.
Toward a user experience tool selector for voice user interfaces

Voice user interfaces (VUI) are currently a trending topic but the ability to measure and improve the user experience (UX) is still missing. We aim to develop a tool selector as a web application that can provide a suitable tool to measure UX quality of VUIs. The UX tool selector for VUIs will include a UX measurement toolbox containing several existing and new VUI assessment methods. The UX tool selector will provide context-dependent measurement recommendations without prior extensive research to evaluate and improve VUIs.
Fine-Grained Attribute-Based Multikeyword Search for Shared Multiowner in Internet of Things

At present, with the popularity of Internet of things (IoT), a huge number of datasets generated by IoT devices are being uploaded to the cloud storage in remote data management service, but a series of security and privacy defects also arises, where one of the best ways for preventing data disclosure is encryption. Among them, searchable encryption (SE) is considered to be a very attractive cryptographic technology, since it allows users to search records in an encrypted form and to protect user’s data on an untrusted server. For the sake of enhancing search permission, attribute-based keyword search (ABKS) is an efficient method to provide secure search queries and fine-grained access authentications over ciphertexts. However, most existing ABKS schemes concentrate on single keyword search, which usually returns redundant and irrelevant results, so it would cost some unnecessary computation and communication resources. Furthermore, existing work in the literature mostly only supports unshared multiowner where a specific data owner owns each file, which is not able to satisfy more desired expressive search. In this work, we propose a novel attribute-based multikeyword search for shared multiowner (ABMKS-SM) primitive in IoT to achieve enhanced access control for users; meanwhile, it can support multikeyword search over ciphertexts and give a formal security analysis in the adaptive against chosen keyword attack (IND-CKA) model. Finally, we have also implemented this prototype to show efficiency when compared with some previous schemes.
Implementation of Immersive Technologies in Professional Training of Teachers

The inclusion of immersive technologies such as augmented reality, virtual reality and mixed reality in the curriculum provides potential pedagogical benefits: providing a platform to increase student engagement, interaction, enjoyment, and a managed reflective learning experience. Research goal: theoretical justification for the use of immersive technologies in the educational process and the development of a blended course "Using the technologies of augmented and virtual reality in the practice of modern educational institutions" for retraining teachers. Research objectives: to determine the role and place of augmented and virtual reality technologies in the educational process and their use in teacher retraining for the formation of professional competence. Object of research: the formation of professional competencies of teachers in the retraining process. Subject of research: immersive technologies as a component of the school educational environment. Used research methods: theoretical methods containing analysis of scientific sources; empirical methods of interviewing and questioning teachers. Research results: analysis of scientific publications allows us to define the concept of augmented and virtual reality, its types, directions of using augmented and virtual reality in education, examples of its application in the educational process. The developed course "Using the technologies of augmented and virtual reality in the practice of modern educational institutions" for the retraining of teachers allows the formation of professional information and communication technologies competencies of teachers. Main conclusions: the introduction of immersive technologies in the educational process increases the effectiveness of training, promotes the development of logical thinking of students and increases the level of motivation of participants in the educational process. The identified gaps indicate the shortcomings of technical and methodological support for teachers to implement immersive technologies for education, which may motivate further work in the field.
Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints

In most tissue-based biomedical research, the lack of sufficient pathology training images with well-annotated ground truth inevitably limits the performance of deep learning systems. In this study, we propose a convolutional neural network with foveal blur enriching datasets with multiple local nuclei regions of interest derived from original pathology images. We further propose a human-knowledge boosted deep learning system by inclusion to the convolutional neural network new loss function terms capturing shape prior knowledge and imposing smoothness constraints on the predicted probability maps.Our proposed system outperforms all state-of-the-art deep learning and non-deep learning methods by Jaccard coefficient, Dice coefficient, Accuracy and Panoptic Quality in three independent datasets. The high segmentation accuracy and execution speed suggest its promising potential for automating histopathology nuclei segmentation in biomedical research and clinical settings.The codes, the documentation and example data are available on an open source at: https://github.com/HongyiDuanmu26/FovealBoosted.Supplementary data are available at Bioinformatics online.
A Bonus-Malus Framework for Cyber Risk Insurance and Optimal Cybersecurity Provisioning

The cyber risk insurance market is at a nascent stage of its development, even as the magnitude of cyber losses is significant and the rate of cyber risk events is increasing. Existing cyber risk insurance products as well as academic studies have been focusing on classifying cyber risk events and developing models of these events, but little attention has been paid to proposing insurance risk transfer strategies that incentivize mitigation of cyber loss through adjusting the premium of the risk transfer product.To address this important gap, we develop a Bonus-Malus model for cyber risk insurance. Specifically, we propose a mathematical model of cyber risk insurance and cybersecurity provisioning supported with an efficient numerical algorithm based on dynamic programming. Through a numerical experiment, we demonstrate how a properly designed cyber risk insurance contract with a Bonus-Malus system can resolve the issue of moral hazard and benefit the insurer.
Timestamp-based Consistency Checking of Collaboratively Developed Engineering Artifacts

Engineering projects typically involve many engineers who work concurrently on a multitude of engineering artifacts such as requirements, models, code, or even non-software artifacts, e.g., drawings or computations. These projects require tight collaboration within and across engineering disciplines in order to guarantee a consistent engineering process. Existing collaborative engineering tools provide limited functionality in this regard. Inconsistencies among engineering artifacts are currently identified only after all involved engineers merge their artifacts. This is problematic because considerable amount of time may elapse between these merges and engineers inadvertently may spend considerable time working under wrong/changed assumptions. This leads to more inconsistencies. This paper proposes an approach for checking the consistency of the engineers' artifacts against the continuously evolving, timestamped version history of a public artifact storage. This avoids costly reworks as engineers understand the implications of their work long before they merge their engineering artifacts. To evaluate our work, we conducted an empirical study utilizing the proposed approach.
An efficient AI-based method to play the Mahjong game with the knowledge and game-tree searching strategy

The Mahjong game has widely been acknowledged to be a difficult problem in the field of imperfect information games. Because of its unique characteristics of asymmetric, serialized and multi-player game information, conventional methods of dealing with perfect information games are difficult to be applied directly on the Mahjong game. Therefore, AI (artificial intelligence)-based studies to handle the Mahjong game become challenging. In this study, an efficient AI-based method to play the Mahjong game is proposed based on the knowledge and game-tree searching strategy. Technically, we simplify the Mahjong game framework from multi-player to single-player. Based on the above intuition, an improved search algorithm is proposed to explore the path of winning. Meanwhile, three node extension strategies are proposed based on heuristic information to improve the search efficiency. Then, an evaluation function is designed to calculate the optimal solution by combining the winning rate, score and risk value assessment. In addition, we combine knowledge and Monte Carlo simulation to construct an opponent model to predict hidden information and translate it into available relative probabilities. Finally, dozens of experiments are designed to prove the effectiveness of each algorithm module. It is also worthy to mention that, the first version of the proposed method, which is named as KF-TREE, has won the silver medal in the Mahjong tournament of 2019 Computer Olympiad.
Neural computations in control problems: aspects of computability and spatial-time characterization of cognitive functions

Abstract The thesis of the futurologist A. Clark that «sufficiently advanced technologies are indistinguishable from magic» is relevant nowadays as never before. Modern science, unlike magic, although it relies on logically consistent physical theories, formalisms of mathematics and the possibilities of computing, has not made the world less mysterious, especially with regard to the natural phenomena of consciousness, thinking and intelligence. From the point of view of computer science, a distinctive feature of these phenomena is their network-centric neuromorphic organization and the availability of complex memory resources - a non-casual computing mechanism for «transporting» processed data, the results of calculations and information interactions in time. Formalization of these features makes it possible to determine a model of cognitive processes on a multidimensional set of states, on which a strict relation between the time order and numerical equality is not introduced, therefore, contextually significant information, about past as well as current or forecast states, can be used for calculations at the same time states. For control systems operating under uncertainty, a distributed heterogeneous reconfigurable structure is proposed that implements the technology of «computation in memory» both for algorithms reflecting causal relationships and learning processes that require operational reconfiguration of the computation field.
Maximal violation of steering inequalities and the matrix cube

In this work, we characterize the amount of steerability present in quantum theory by connecting the maximal violation of a steering inequality to an inclusion problem of free spectrahedra. In particular, we show that the maximal violation of an arbitrary unbiased dichotomic steering inequality is given by the inclusion constants of the matrix cube, which is a well-studied object in convex optimization theory. This allows us to find new upper bounds on the maximal violation of steering inequalities and to show that previously obtained violations are optimal. In order to do this, we prove lower bounds on the inclusion constants of the complex matrix cube, which might be of independent interest. Finally, we show that the inclusion constants of the matrix cube and the matrix diamond are the same. This allows us to derive new bounds on the amount of incompatibility available in dichotomic quantum measurements in fixed dimension.