Reviewerly/exHarmony huggingface dataset:
Enhanced Accesability and Anti-Fraudulent System for Polling Stations and Mobile Voting in Hospitals

This paper analyses the state of voting systems around the world in order to attempt to draw a template for the steps needed to be taken to implement electronic voting and to determine if the implementation of such a system is beneficial. We attempt to clarify the term electronic voting by classifying the types of machines used by the role they serve in the voting process. Also, we provide a blueprint for Electronic Voting Systems (EVS) and the associated information necessary to use electronic voting machines (EVM) in elections. We also discuss voting machine and voting system security and propose a technical solution for an anti-fraudulent architecture that will allow electronic vote's encryption while simultaneously ensuring traceability and vote confidentiality. The implementation of such machines and system could allow for hospitalized voters and voters with disabilities that make use of the mobile voting system, to more easily express their vote.
BumbleBee: Application-aware adaptation for edge-cloud orchestration

Modern developers rely on container-orchestration frameworks like Kubernetes to deploy and manage hybrid workloads that span the edge and cloud. When network conditions between the edge and cloud change unexpectedly, a workload must adapt its internal behavior. Unfortunately, container-orchestration frameworks do not offer an easy way to express, deploy, and manage adaptation strategies. As a result, fine-tuning or modifying a workload's adaptive behavior can require modifying containers built from large, complex codebases that may be maintained by separate development teams. This paper presents BumbleBee, a lightweight extension for container-orchestration frameworks that separates the concerns of application logic and adaptation logic. BumbleBee provides a simple in-network programming abstraction for making decisions about network data using application semantics. Experiments with a BumbleBee prototype show that edge ML-workloads can adapt to network variability and survive disconnections, edge stream-processing workloads can improve benchmark results between 37.8% and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{23\mathrm{x}}$</tex> , and HLS video-streaming can reduce stalled playback by 77%.
CopyPoppy – A Source Code Plagiarism Detector

As the technology advances over the years, students have flooded into courses that are related to computer science across the globe, recognizing them as an opportunity to work at known companies like Google and Facebook as one of their main interests. Though, the spike in interest for these courses coincided with an undesirable side effect: a rising number of source code plagiarism. This paper is about solving programming plagiarism problems by developing a source code plagiarism system in Java called CopyPoppy. It is important to note that there are students that understand code solutions and use them as a foundation to write their own code. These kinds of cases may not be necessarily considered as plagiarising as they are only using other’s source code as an inspiration for their finished code. Therefore, there are a lot of aspects that need to be taken into account when deciding whether a source code is plagiarised or not. Therefore, CopyPoppy is designed to be able to distinguish between what is considered plagiarism and what is not. The existence of CopyPoppy hopefully will encourage more and more students to learn and write their own source code with their highest level of ability and quality as a way to develop self-discipline and to grasp their level of understanding through concepts and skills that they have learnt.
Transfer Learning Pre-training Dataset and Fine-tuning Effect Analysis on Cancer Histopathology Images

Due to the shortage of training data, transfer learning is frequently used in constructing medical imaging models. In this study, we perform transfer learning pre-training dataset and fine-tuning effect analysis in cancer histopathology imaging by evaluating three popular deep neural network algorithms on three target datasets under various fine-tuning configurations. Pre-training models with cancer histopathology image datasets appear to perform worse or not better than pre-training models with ImageNet or random initialization. Furthermore, this study demonstrates that the performance of pre-trained models improves with the increase of images used in fine-tuning, which was previously overlooked.
Invariant Content Synergistic Learning for Domain Generalization on Medical Image Segmentation

Although deep convolution neural networks (DC-NNs) can achieve remarkable success on medical image segmentation, their performance might significantly deteriorate when confronting testing data with the new distribution. Recent studies suggest that one major cause of this issue is the strong inductive bias of DCNNs, which towards image styles (e.g., superficial texture) that are sensitive to change, instead of the invariant content (e.g., object shapes). Inspired by this, we propose a novel method, named Invariant Content Synergistic Learning (ICSL), to improve the generalization ability of DCNNs on unseen data by controlling the inductive bias. Specifically, ICSL first mixes the style of training instances to perturb the training distribution, so that more diverse domains or styles would be made available for training DCNNs. Then, based on the perturbed distribution, we carefully design a dual-branches invariant content synergistic learning strategy to prevent style-biased predictions and maintain the invariant content. Extensive experimental results demonstrate the superior performance of the proposed method over state-of-the-art domain generalization methods on two typical medical segmentation tasks.
EHR2HG: Modeling of EHRs Data Based on Hypergraphs for Disease Prediction

EHRs contain the patient’s historical disease information, and a natural idea is to predict the patient’s disease or diagnose the patient based on EHRs. However, existing deep learning models using EHRs are not satisfactory in solving several key challenges: 1) most of the existing methods are lack of priori knowledge assistance during model learning; and 2) higher-order relationships among diseases and patients are not explored sufficiently. To address these issues, in this paper we propose a hypergraph-based deep learning model for disease prediction, namely EHR2HG. First, we propose to utilize the existing disease classification information to give the model a better initialization condition. Second, by constructing hypergraphs, we consider all of the patients together instead of separate individuals, so our models can model entity relationships such as comorbidities or patient class groups. We have evaluated the proposed model on a real-world EHRs dataset and the results demonstrated that EHR2HG can achieve comparable and better performance than several state-of-the-art baseline methods.
Cross-supervised learning for cloud detection

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Modern approaches to teaching future teachers of mathematics: the use of mobile applications and their impact on students’ motivation and academic success in the context of STEM education

ABSTRACTABSTRACTTeachers are increasingly incorporating new learning materials into their classes: mobile apps, VR, and other results of humanity's digital development. The aim of the study was to compare the impact of the traditional approach to teaching mathematics and the use of MalMath application on the academic performance of students in terms of conceptual and procedural knowledge, problem solving, and the level of their learning motivation. The participants were 72 students. A quasi-experimental study was conducted, which revealed that the participants of the experimental MalMath group were more academically successful (45.11) than the control group students (33.23), which was statistically significant. The results of the study confirmed the increased learning motivation when learning with the MalMath application (t-test was 5.396). The study results confirm the data of other scientific papers on the positive impact of MalMath application on the academic performance and motivation of students. Theoretical and practical implications of this study are based on the possibilities of further theoretical study and practical use of mobile applications in the process of learning mathematics, comparing the effectiveness of MalMath with other applications.KEYWORDS: Achievementlearning mathematicsMalMathmathematics educationmodern learning technologiesmotivation AcknowledgementViktor Shurygin and Tatyana Anisimova has been supported by the Kazan Federal University Strategic Academic Leadership Program.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be available on request.Additional informationFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Adaptive dynamic networks for object detection in aerial images

In this paper, we propose an entropy-dynamic resolution detection (EDRdet) method for object detection in aerial images. Most conventional object detection methods usually detect each region in aerial images directly with a fixed resolution, so that the resolution of the hard-to-detect region in the whole aerial image is not enough and that of the easy-to-detect region is not necessary. We argue that different resolutions of regions are required for efficient and accurate object detection in aerial images. Our EDRdet dynamically adjusts the resolution of each region via measuring the challenge of the detection process, therefore, our model can achieve a good trade-off between the computational cost and the accuracy effectively. Specifically, our EDRdet searches hard-to-detect regions through low-resolution global context information, and inputs higher-resolution patches to supplement lost feature at low resolution for fine-grained detection. We apply the entropy to determine the regions in aerial images required to be detected at higher resolutions, and where the entropy represents the uncertainty of the detection result. Moreover, we design a patch sampling algorithm to make the selected regions sparse to further improve the efficiency of patch generation. Extensive experiments on the DOTA and Visdrone2019 datasets verify that our EDRdet can reduce the computational cost and improve the model accuracy effectively.
Assessing operational accuracy of CNN-based image classifiers using an oracle surrogate

Assessing the accuracy in operation of a Machine Learning (ML) system for image classification on arbitrary (unlabeled) inputs is hard. This is due to the oracle problem, which impacts the ability of automatically judging the output of the classification, thus hindering the accuracy of the assessment when unlabeled previously unseen inputs are submitted to the system. We propose the Image Classification Oracle Surrogate (ICOS), a technique to automatically evaluate the accuracy in operation of image classifiers based on Convolutional Neural Networks (CNNs). To establish whether the classification of an arbitrary image is correct or not, ICOS leverages three knowledge sources: operational input data, training data, and the ML algorithm. Knowledge is expressed through likely invariants - properties which should not be violated by correct classifications. ICOS infers and filters invariants to improve the correct detection of misclassifications, reducing the number of false positives. We evaluate ICOS experimentally on twelve CNNs – using the popular MNIST, CIFAR10, CIFAR100, and ImageNet datasets. We compare it to two alternative strategies, namely cross-referencing and self-checking. Experimental results show that ICOS exhibits performance comparable to the other strategies in terms of accuracy, showing higher stability over a variety of CNNs and datasets with different complexity and size. ICOS likely invariants are shown to be effective in automatically detecting misclassifications by CNNs used in image classification tasks when the expected output is unknown; ICOS ultimately yields faithful assessments of their accuracy in operation. Knowledge about input data can also be manually incorporated into ICOS, to increase robustness against unexpected phenomena in operation, like label shift.
Unsupervised self-attention lightweight photo-to-sketch synthesis with feature maps

Face-sketch synthesis is important for gaining a clear portrait photo of suspects when solving crimes. Recent research has made a great process in self-attention generative adversarial networks. We propose a method of unsupervised learning in the synthesis of face sketch-to-photo using a new attention module. The method of processing on a small reference set of photo-sketch pairs adds to the attention module, a focus on the regions distinguishing photos from sketches on the basis of the feature maps obtained by the auxiliary classifier. Unlike previous attention-based methods, which cannot handle the geometric changes between domains, our model can translate images requiring holistic changes. At the same time, we reduce the layers of the discriminator according to different residual layers to optimize our network. With the proposed approach, we can train our networks using a small reference set of photo-sketch pairs together with a large number of face-photo datasets and more distinguishing facial-feature regions in the self-attention model. Experiments have shown the superiority of the proposed method to existing face sketch-to-photo synthesis models using fixed network architectures and hyper-parameters.
Evolution of the entanglement, photon statistics and quantum Fisher information of a single qubit parity deformed JCM

In this paper we use the atomic version of the quantum Fisher information to estimate the quantum state of the parity deformed Jaynes Cummings Model (PD-JCM) that describing the interaction between a SQ (single qubit) and parity deformed field. The nonclassical properties of the field are identified by the evolution of the second-order correlation function. The von Neumann entropy is provided for the non-local correlation between the SQ and parity deformed field. Also, the single qubit quantum Fisher information within the phase shift estimator is obtained and compared with the von Neumann entropy during the interaction time. We examine the effect of the deformation parameter and time-dependent coupling or qubit speed on the dynamical behavior of the proposed quantities. The results verified that the dynamical behavior of the proposed quantities can be controlled by the qubit speed, deformation parameter and time-dependent coupling or qubit speed parameters.
Geometric Deep Learning Graph Pruning to Speed-Up the Run-Time of Maximum Clique Enumerarion Algorithms

In this paper we propose a method to reduce the running time to solve the Maximum Clique Enumeration (MCE) problem. Specifically, given a network we employ geometric deep learning in order to find a simpler network on which running the algorithm to derive the MCE. Our approach is based on finding a strategy to remove from the network nodes that are not functional to the solution. In doing so, the resulting network will have a reduced size and, as a result, search times of the MCE is reduced. We show that our approach is able to obtain a solver speed-up up to 42 times, while keeping all the maximum cliques.
An Adaptive Network Model Simulating the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning

This paper investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organizational culture results in better mistake management and thus better organizational learning, (2) Effective organizational learning requires a transformational leader to have both high social and formal status and consistency, and (3) Company culture and leader’s behavior must align for the best learning effects. Computational simulations of the introduced adaptive network were analyzed in different contexts varying in organization culture and leader characteristics. Statistical analysis results proved to be significant and supported the research hypotheses. Ultimately, this paper provides insight into how organizations that foster a mistake-tolerant attitude in alignment with the leader, can result in significantly better organizational learning on a team and individual level.
The Problem of Defining the Concept of “Electronic Document for Long-Term Storage”

The definitions of the concepts of an electronic document existing in domestic and international regulatory documents were investigated in this article. Important components of an electronic document for long-term storage were identified in the course of the study, namely: metadata, a normalized copy, related directories and classifiers, indices, components of an electronic signature, etc. An electronic document was defined in relation to the organization of long-term storage and the composition was determined in general his information. The mathematical model of the electronic document is presented in general form. The ways of further scientific research are determined: development of a mathematical model of the semantic blocks of an electronic document; assessment of the alienability of an electronic document from the software and hardware storage environment; assessment of the interpretability of electronic documents. The author plans to prepare a series of articles to describe the solution to the problems of ensuring the preservation of the document in the course of further research.
Auto Filmer: Autonomous Aerial Videography Under Human Interaction

The advance of unmanned aerial vehicles (UAVs) has enabled customers and directors to film from the air. However, operating the drone to produce desired videos upon a moving object is hard to achieve. This letter proposes an autonomous aerial videography system that integrates customized shots and drone dynamics. We design a user-friendly interface for the operator to create the desired shot in real-time. The shot information is then transmitted to the kinodynamic path search process, in which a safe shooting path will be evaluated. Later, feasible regions and safe flight corridors are constructed for safety and visibility. Finally, a joint optimization is carried out to generate the trajectory of the quadrotor and the gimbal to maintain the required image composition. Extensive simulation and real-world experiments validate the effectiveness of our method. The supplementary video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> presents the results.
Monopolar and bipolar electrooculography signal characteristics due to target displacements—have we seen the whole picture?

The development of electrooculography (EOG)-based human-computer interface systems is generally based on the processing of the commonly referred to horizontal and vertical bipolar EOG channels, which are computed from a horizontally-aligned and another vertically-aligned pair of electrodes, respectively. Horizontal (vertical) target displacements are assumed to result in changes in the horizontal (vertical) EOG channel only, and any cross-talk between the bipolar channels is often neglected or incorrectly attributed solely to electrode misalignment with respect to the ocular rotation axes.Objective. The aim of this work is to demonstrate that such cross-talk is intrinsic to the geometric relationship between the orientation of the verging ocular globes and the planar displacement of the gaze target with respect to the primary gaze position.Approach. Since it is difficult to record actual EOG data with electrodes which are perfectly-aligned with the ocular rotation axes, this is studied by simulating the EOG potential values for various horizontally- and vertically-displacing targets using a dipole model of the eye.Main results. We show that cross-talk between the horizontal and vertical bipolar EOG channels is manifested even if the electrodes are aligned with the ocular rotation axes. Specifically, for a horizontally- (vertically-)displaced target, while the monopolar EOG signals obtained from the horizontally- (vertically-)aligned electrodes exhibit an expected predominant potential displacement, a smaller displacement is also exhibited in the monopolar EOG signals obtained from the vertically- (horizontally-)aligned electrodes. These unexpected displacements in the vertically- (horizontally-)aligned monopolar channels may have different magnitudes, resulting in an effective potential displacement in the vertical (horizontal) bipolar EOG channel.Significance. This is significant as it shows that, unlike in many works published so far for EOG-based ocular pose estimation, it is not sufficient to only use the horizontal (vertical) bipolar EOG channel to estimate the horizontal (vertical) displacement of the ocular pose.
Object detection via inner-inter relational reasoning network

Exploiting relationships between objects and (or) labels under graph message passing mechanism to facilitate object detection has been widely investigated in recent years. However, these methods heavily rely on hand-crafted graph structures, which may introduce unreliable relationships and in turn hurt the object detection performance. Aiming to address this issue, we propose a novel object detection framework that fully explores the relational representations for objects and labels under a full attention architecture. Specifically, we directly regard the extracted proposals and candidate labels as two independent sets in visual feature space and label embedding space, respectively. And we design a self-attention module to discover the inner-relationships within the visual feature space or label embedding space. In addition, a cross-attention module is developed to explore the inter-relationships between the two spaces. Furthermore, both the inner-relationships and inter-relationships are utilized to enhance the object features and label embedding representations to facilitate the object detection. To validate the proposed framework in improving object detection performance, we embed it into several state-of-the-art baselines and perform extensive experiments on two public benchmarks (named Pascal VOC and COCO 2017). The experimental results demonstrate the effectiveness and flexibility of the proposed framework.
BGCFI: Efficient Verification in Fine-Grained Control-Flow Integrity Based on Bipartite Graph

Control-flow integrity (CFI) is considered a principled mitigation against control-flow hijacking even under the most powerful attacker who can arbitrarily write and read memory.However, existing schemes still demonstrated limitations in either guaranteeing high security level or achieving low performance and memory overhead.These limitations have restricted the application of CFI in real software.To improve its applicability similar to mandatory protection schemes such as DEP and ASLR, it is essential to improve both high security guarantee and low overhead.In this paper, we propose ''BGCFI'', which is a fine-grained CFI based on a Bipartite Graph.The relationship between an indirect branch and a valid target address at the branch is represented by an edge in the bipartite graph.The verification of the indirect branch is achieved by checking the existence of the corresponding edge in the bipartite graph.The verification method for fine-grained CFI results in more efficiency on both computational and memory overhead, while completely preserving high security guarantee.We demonstrate our results through the implementation of a proof-of-concept module and evaluation on the SPEC CPU 2017 suite and the Firefox browser.