Reviewerly/exHarmony huggingface dataset:
Hospital Staff’s Adherence to Information Security Policy: A Quest for the Antecedents of Deterrence Variables

Information security has come to the forefront as an organizational priority since information systems are considered as some of the most important assets for achieving competitive advantages. Despite huge capital expenditures devoted to information security, the occurrence of security breaches is still very much on the rise. More studies are thus required to inform organizations with a better insight on how to adequately promote information security. To address this issue, this study investigates important factors influencing hospital staff’s adherence to Information Security Policy (ISP). Deterrence theory is adopted as the theoretical underpinning, in which punishment severity and punishment certainty are recognized as the most significant predictors of ISP adherence. Further, this study attempts to identify the antecedents of punishment severity and punishment certainty by drawing from upper echelon theory and well-acknowledged international standards of IS security practices. A survey approach was used to collect 299 valid responses from a large Taiwanese healthcare system, and hypotheses were tested by applying partial least squares-based structural equation modeling. Our empirical results show that Security Education, Training, and Awareness (SETA) programs, combined with internal auditing effectiveness are significant predictors of punishment severity and punishment certainty, while top management support is not. Further, punishment severity and punishment certainty are significant predictors of hospital staff’s ISP adherence intention. Our study highlights the importance of SETA programs and internal auditing for reinforcing hospital staff’s perceptions on punishment concerning ISP violation, hospitals can thus propose better internal strategies to improve their staff’s ISP compliance intention accordingly.
Quantum teleportation of hybrid qubits and single-photon qubits using Gaussian resources

In this paper, we compare single-photon qubits and hybrid qubits as information carriers through quantum teleportation using a Gaussian continuous-variable channel. A hybrid qubit in our study is in the form of entanglement between a coherent state and a single photon. We find that hybrid qubits outperform photonic qubits when coherent amplitudes of the hybrid qubits are as low as $\alpha\lesssim 1$, while photonic qubits yield better results for larger amplitudes. We analyze effects of photon losses, and observe that the overall character of teleportation for different qubits remains the same although the teleportation fidelities are degraded by photon losses. Our work provides a comparative look at practical quantum information processing with different types of qubits.
Over-the-air Learning Rate Optimization for Federated Learning

The sixth-generation (6G) wireless communication is expected to support ubiquitous artificial intelligence (AI) applications from the network core to the end devices. The computational intensive AI tasks are traditionally deployed in the server center, which inevitably gives rise to latency and privacy concerns. As a promising framework, federated learning can address the above concerns by featuring distributed learning at the devices and model aggregation at the aggregator. Exploiting over-the-air computing to aggregate these local models further improves communication efficiency compared with the conventional separated-communication-and-computation principle. However, the resulted distortion by the fading and noisy channels is critical as large error may degrade the learning and inference performance. Inspired by the changeable learning rate, we first propose to exploit the dynamic learning rate (DLR) to adapt to the fading channels, which is proved to be capable of further combating the distortion. The problem is formulated in a single-input-multiple-output system with an objective of aggregate error minimization. To address this problem, an iterative method is proposed. Extensive simulation results demonstrate the effectiveness of the proposed DLR in terms of mean squared error performance as well as the testing accuracy on the CIFAR10 dataset.
Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask

In this paper, we propose a learning-based approach for denoising raw videos captured under low lighting conditions. We propose to do this by first explicitly aligning the neighboring frames to the current frame using a convolutional neural network (CNN). We then fuse the registered frames using another CNN to obtain the final denoised frame. To avoid directly aligning the temporally distant frames, we perform the two processes of alignment and fusion in multiple stages. Specifically, at each stage, we perform the denoising process on three consecutive input frames to generate the intermediate denoised frames which are then passed as the input to the next stage. By performing the process in multiple stages, we can effectively utilize the information of neighboring frames without directly aligning the temporally distant frames. We train our multi-stage system using an adversarial loss with a conditional discriminator. Specifically, we condition the discriminator on a soft gradient mask to prevent introducing high-frequency artifacts in smooth regions. We show that our system is able to produce temporally coherent videos with realistic details. Furthermore, we demonstrate through extensive experiments that our approach outperforms state-of-the-art image and video denoising methods both numerically and visually.
Amodal Segmentation Just Like Doing a Jigsaw

Amodal segmentation is a new direction of instance segmentation while considering the segmentation of the visible and occluded parts of the instance. The existing state-of-the-art method uses multi-task branches to predict the amodal part and the visible part separately and subtract the visible part from the amodal part to obtain the occluded part. However, the amodal part contains visible information. Therefore, the separated prediction method will generate duplicate information. Different from this method, we propose a method of amodal segmentation based on the idea of the jigsaw. The method uses multi-task branches to predict the two naturally decoupled parts of visible and occluded, which is like getting two matching jigsaw pieces. Then put the two jigsaw pieces together to get the amodal part. This makes each branch focus on the modeling of the object. And we believe that there are certain rules in the occlusion relationship in the real world. This is a kind of occlusion context information. This jigsaw method can better model the occlusion relationship and use the occlusion context information, which is important for amodal segmentation. Experiments on two widely used amodally annotated datasets prove that our method exceeds existing state-of-the-art methods. In particular, on the amodal mask metric, our method outperforms the baseline by 5 percentage points on the COCOA cls dataset and 2 percentage points on the KINS dataset. The source code of this work will be made public soon.
A Census of Swedish Public Sector Employee Communication on Cybersecurity during the COVID-19 Pandemic

The COVID-19 pandemic has accelerated the digitalization of the Swedish public sector, and to ensure the success of this ongoing process cybersecurity plays an integral part. While Sweden has come far in digitalization, the maturity of cybersecurity work across entities covers a wide range. One way of improving cybersecurity is through communication, thereby enhancing employee cyber situation awareness. In this paper, we conduct a census of Swedish public sector employee communication on cybersecurity at the beginning of the COVID-19 pandemic using questionnaires. The study shows that public sector entities find the same sources of information useful for their cybersecurity work. We find that nearly two thirds of administrative authorities and almost three quarters of municipalities are not yet at the implemented cybersecurity level. We also find that 71 % of municipalities have less than one dedicated staff for cybersecurity.
Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift

The diagnosis of abnormal transformer oil temperature is of great significance to guarantee the normal operation of the transformer. Due to concept drift, the oil temperature abnormal diagnosis of the oil-immersed main power transformer is usually unstable via the classic data mining method. Thus, this paper proposes an adaptive abnormal oil temperature diagnosis method (AAOTD) of the transformer based on concept drift. First, the bagging ensemble learning method was used to predict the oil temperature. Then, abnormal diagnosis was performed based on the difference between the predicted oil temperature and the actual measured oil temperature. At the same time, based on the concept drift detection strategy and Adaboost ensemble learning methods, adaptive update of the base classifier in the abnormal diagnosis model was realized. Experiments validated that the algorithm proposed in this paper can significantly reduce the influence of concept drift and has higher oil temperature prediction accuracy. Furthermore, since this method only utilizes the existing power grid data resources to realize abnormal oil temperature diagnosis without extra monitoring equipment, it is an economic and efficient solution for practical scenarios in the electric power industry.
Survey of Immersive Techniques for Surgical Care Telemedicine Applications

Telemedicine is one of the rising and innovative tools in medicine that allows the remote delivery of health services over the internet. This technology enables people who do not have direct and urgent access to expert medical care to still get the services they need. Applications of telemedicine include telesurgeries, telementoring, teleproctoring and teleconsulting. Telesurgery is the case where an expert surgeon remotely performs a surgery on a patient while not physically in the operation room over a wired/wireless connection. Virtual reality (VR) and augmented reality (AR) are widely used to give the remote surgeon the required immersive experience and allow him/her to see exactly what the local surgeon is seeing. This paper focuses on surveying the current technologies used in telesurgeries. It starts off by exploring the tools used to create the immersive experience in telesurgery such as 3D cameras and 360 cameras, the headpieces and the robots used to give the remote surgeon the real feel and control of the surgery. It then continues to survey the techniques used to create that immersive experience including the different camera encodings, depth compression and estimation techniques and 3D modeling. Finally it briefly overviews some of the networking issues regarding the streaming of 3D videos in real-time.
Presentations in Lectures to Prompt Innovations in Higher Education

Considering the rich experience of using multimedia presentations as a teaching tool in higher education, in order to introduce new approaches and develop a methodology, there is a need to identify the strengths and weaknesses of existing practices, in particular to identify the emotional attitude of university students to existing lectures and presentations in the educational process and determine relevant opportunities to improve the practice of using interactive and visual educational tools.The empirical basis of the study was the results of a survey of students of 2–4 undergraduate courses at the Moscow State Institute of International Relations (University) of the Russian Ministry of Foreign Affairs in the 2019–2020 academic year. The survey involved 404 respondents. To process the obtained data, we used the Google Forms and Microsoft Office Excel programs, general scientific methods (comparison, generalization), statistical analysis methods, as well as content analysis.The empirical study showed a high level of student satisfaction with the volume and effectiveness of multimedia presentations used in the educational process, and also confirmed the theoretical ideas about the advantages and limitations of linear form presentations that dominate in the educational environment. Critical remarks included design errors, oversaturation of visual material, activity out of sync, and flaws in speaker behavior. Among the new techniques for working with presentation materials, photographing slides was noted. An important observation was the students’ request for self-sufficient visual materials of lectures and access to them.Prospects and opportunities for transforming familiar lectures and the learning environment as a whole were identified taking into account the existing experience of presentations at the university; ways of solving the problems with students’ perception and acquisition of material are proposed and specific recommendations are given for organizing a wide range of classes with visual and interactive components, today these are, first of all, remote online courses (MOOC, e-learning) and new forms of organization classroom activities (“flipped classroom”).
Building an Educational Product: Constructive Alignment and Requirements Engineering

Building an educational software product has two facets: software engineering and educational design. This paper proposes an approach that combines commonly used requirements engineering techniques with the educational concept of constructive alignment to develop an educational software product. The approach is novel in that it shows the direct correspondence of the engineering techniques with the constructive alignment, demonstrates its practical application through the case study project, and outlines a step-by-step procedure for using it.
Analysing the loss landscape features of generative adversarial networks

Generative adversarial networks (GANs) have recently gained popularity in artificial intelligence research due to their superior generation, enhancement and style transfer of content compared to other generative models. Introduced in 2014, GANs have been used in the fields of computer vision, natural language processing, medical applications, and cyber security, with the number of use cases rapidly growing. GANs are, however, difficult to train in practice due to their inherent high dimensionality, and the complexity associated with the adversarial learning task. Loss landscape analysis can aid in unravelling reasons for difficulty in training GANs as the analysis creates a topology of the search space. The vanilla and deep convolutional GAN architectures are examined in this study to gain a deeper understanding of their loss landscapes during training. The GAN loss landscape features are visualised through the use of loss gradient clouds (LGCs). The LGC analysis showcases the importance of volatility in the training of GANs, as a range of gradient magnitudes allows more exploration in finding an appropriate middle ground in balancing the loss objectives of the GAN.
Multi-surface hydraulic valve block technique hole plug inspection from monocular image

We propose a visual inspection framework that can detect technique hole plugs on multiple surfaces of hydraulic valve block from monocular RGB image. Firstly, the pose of the valve block relative to the camera is estimated, and then the visible surfaces of the valve block are corrected. Finally, according to the size information, the image patch around the technique hole are classified to judge whether there is a plug. This paper uses the LINEMOD object pose estimation framework as the pose estimation backbone. We propose a special contour extraction method for the counterbore features of valve blocks. Sample points of a matched template are used to construct a nonlinear optimization problem to obtain the accuracy pose. The experimental results show that our method can accurately estimate the pose of the valve block, and at the same time can cooperate with a simple machine learning algorithm to complete the plug inspection.
How Many Pop-Stacks Does It Take To Sort A Permutation?

Abstract Pop-stacks are variants of stacks that were introduced by Avis and Newborn in 1981. Coincidentally, a 1982 result of Unger implies that every permutation of length $n$ can be sorted by $n-1$ passes through a deterministic pop-stack. We give a new proof of this result inspired by Knuth’s zero-one principle.
Adapting a Software Acquisition Curriculum to Instruct Supply Chain Risk Management in a Project-Based Software Development Course

Project-Based Instruction has become a de-facto standard in many software engineering curricula. It engages the learner in critical thinking, fosters introspection, improves retention, and enables generalization of concepts to new engineering situations. However, as projects often focus on software construction, students tend to adopt third-party technologies without critical reflection. Such insufficient supply chain risk management (SCRM) throughout the project then threatens learning success, as students struggle to recover from inadequacies and vulnerabilities in the adopted technology. We therefore propose a capstone-style course structure to deliver an industry-realistic, project-based, and teamwork-centric software development approach. Using an existing SCRM curriculum, we add acquisition planning, bidding, and monitoring activities to an existing course design and assess learning outcomes.
Surprising image compositions

Visual metaphors are a powerful and effective way of communication in advertising, news, and art. By taking objects out of their natural context, artists create new and surprising composite images by leveraging visual, linguistic, or phonetic analogies. We build on recent image retrieval, completion and composition methods to design a new collage generation tool with the aim of assisting artists in creating interesting composite images. Given a selected object in an image, our model searches for visually similar but semantically different objects and performs the image blending automatically, leading to surprising image combinations. Using automatic metrics and a human study, we test our approach against baselines and show the potential of this novel artistic application.
Towards a framework for analyzing context-oriented programming languages

Context-aware systems keep on emerging in all of our daily activities. To cope with this new situation, programming languages were extended to support the notion of context. Although context-oriented programming languages exist for over 15 years, they were tested for their suitability in developing context-aware systems only to a limited extent. In this paper, we propose a framework for analyzing context-oriented languages from that viewpoint. In this framework, we refer to context definition and activation, reasoning capabilities, process aspects of how to work with the suggested extension, and pragmatic considerations. To demonstrate the use of the framework, we applied it to a relatively new context-oriented programming paradigm, namely context-oriented behavioral programming. The proposed framework can support users of context-oriented programming languages in deciding on the most fitted language for their purpose. Developers of context-oriented programming languages can also use the framework to improve such languages and their supporting tools.
Ret-GAN: Retinal Image Enhancement using Generative Adversarial Networks

With over 200K cases in the U.S. alone, retinal disorders are the most common cause of irreversible blindness. This serves as a primary aim to analyze automated screening tools to detect retinal disorders. We analyze the OCT dataset (84, 484 images) and enhance the images by using Generative Adversarial Networks (GANs). This work specifically focuses on enhancing the quality of source (training) images for better algorithm validatiorr/testing <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Authors contributed equally to the work.. We synthesize super resolution-based images using generators, discriminators and the adversarial nature of the GANs. The performance of the Ret-GAN is validated by PSNR, SSIM, and loss functions. To test the Ret-GAN generated images, we train a convolutional neural network (CNN) with the original dataset images and super-resolution images. We achieve an accuracy of 0.9825 on Ret-GAN generated image data, and 0.9525 on the original data. We statistically analyze the CNN with a number of evaluation metrics to further validate the results. The proposed scheme is compared to benchmark research findings on the same dataset. Our results are encouraging.
Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs

Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory is physically finite, which limits the image resolutions and batch sizes that could be used in training for better DNN performance. Unlike solutions that require physically upgrade GPUs, the Gradient Check-Pointing(GCP) training trades computation for more memory beyond existing GPU hardware. GCP only stores a subset of intermediate tensors, called Gradient Checkpoints (GCs), during forward. Then during backward, extra local forwards are conducted to compute the missing tensors. The total training memory cost becomes the sum of (1) the memory cost of the gradient checkpoints and (2) the maximum memory cost of local forwards. To achieve maximal memory cut-offs, one needs optimal algorithms to select GCs. Existing GCP approaches rely on either manual input of GCs or heuristics-based GC search on Linear Computation Graphs (LCGs), and cannot apply to Arbitrary Computation Graphs(ACGs). In this paper, we present theories and optimal algorithms on GC selection that, for the first time, are applicable to ACGs and achieve the maximal memory cut-offs. Extensive experiments show that our approach not only outperforms existing approaches (only applicable on LCGs), and is applicable to a vast family of LCG and ACG networks, such as Alexnet, VGG, ResNet, Densenet, Inception Net and highly complicated DNNs by Network Architecture Search. Our work enables GCP training on ACGs, and cuts off up-to 80% of training memory <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> with a moderate time overhead (∼ 30%-50%). Codes are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
Federated Learning in Multi-antenna Wireless Networks

In this work, we propose an analytical model to study the convergence performance of federated learning (FL) in a multi-antenna wireless network by comparing three different scheduling policies, i.e., the round robin (RR), random scheduling (RS), and proportional fair (PF). We derive tractable expressions for the convergence rates of FL by taking into account the scheduling policy, antenna number, channel fading, and intercell interference. Our results show that PF achieves the best convergence performance under the high signal-to-interference-plus-noise ratio (SINR) threshold, while the three scheduling policies achieve very similar convergence rates under extremely low SINR threshold. Given the number of antennas per base station (BS), we observe an optimal number of scheduled user equipments (UEs) per BS that maximizes the convergence rate of FL under high SINR threshold because of the trade-off between serving more UEs and achieving higher successful transmission probability.