Reviewerly/exHarmony huggingface dataset:
Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach

Abstract Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient‐monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph‐theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyber risk.
Generic optimization by fast chaotic exploration and slow feedback fixation

Living systems adapt to various environmental conditions by changing their internal states through processes such as gene expression and epigenetic modification. In this paper, we propose a generic mechanism for optimization that combines fast oscillatory dynamics with a slower feedback fixation process. Through extensive model simulations, we demonstrate that the fast chaotic dynamics serve as a global search for optimal states, which are then fixed by the slower dynamics. This mechanism becomes more effective as the number of elements is increased. We also discuss the potential relevance of this optimization mechanism to problems in artificial neural networks.
A Methodology to Analyze and Estimate the Software Development Process Using Machine Learning Techniques

Analyzing the software development process and estimating the effort required for its completion is an essential task. In the case of Agile methodology, the values of the parameters used for estimation vary frequently as the scope of the project changes with changes in the requirements of the clients. Hence, the estimation done at the initial phase will not be appropriate until the completion of the project. Therefore, to overcome this issue, a methodology is proposed to estimate the duration of a project by applying machine learning techniques. The use-case point method is used for estimating the duration. Information about the number of use cases and values for environmental and technical factors is stored in a repository. Few values may be uncertain, and to estimate the effort for a new project with few unknown or uncertain values, the machine learning algorithm Gaussian Process Regression (GPR) is used. The repository information is taken as the training dataset, and the new project data is taken as the test dataset. The estimated value shows the accurate duration for the new project. The result is validated with a popular dataset.
Scanline intersection similarity: A similarity metric for joint trace maps

Although various automatic joint trace survey methods have been proposed, it has been difficult to evaluate their accuracy. This difficulty lies in the lack of comprehensive metrics for expressing the similarity between the automatically derived trace maps and those derived manually. In this study, a novel metric is proposed to express the similarity between joint trace maps using a single numerical value. The proposed metric, scanline intersection similarity (SIS), expresses trace map similarity by evaluating the similarity between trace-scanline intersections on many orthogonal scanline pairs. The expressiveness of SIS was tested on multiple synthetic trace map pairs, and the results confirmed that SIS comprehensively reflects trace map similarity in terms of trace frequency, trace orientation, and the spatial distribution of traces. Comparisons with conventional metrics used in computer vision, namely, intersection over union (IoU) and boundary F1 (BF) scores, revealed that SIS expresses trace map similarity more accurately. IoU and BF scores led to misleading conclusions because of their evaluation of trace map similarity using the areal overlap of traces. To further demonstrate the applicability of SIS, the accuracy of deep-learning-based trace detection using U-Net (University of Freiburg, Germany) and DeepLabV3+ (Google, USA) was evaluated and compared using SIS.
Геохимические свойства и трансформация микроэлементного состава почв при разработке коренных месторождений алмазов в Якутии

Extraction of diamonds from primary deposits in Siberia is associated with the development of kimberlite pipes in challenging environmental conditions, accompanied by a complex impact on the environment. The article presents the results of monitoring the soil cover of the Nakyn kimberlite field in the Yakutia diamond province, which is affected by the facilities of the Nyurba Mining and Processing Division. Development of primary diamond deposits has a large-scale impact on the subsoil, topography, and soil cover: creation of the world's largest quarries, formation of dumps more than 100 m high, arrangement of extensive tailings, formation of solid and liquid industrial wastes of various chemical composition. The research is aimed at studying the spatial and temporal patterns of the technogenic impact on the soil cover, identifying the nature and level of transformation of the microelement composition of soils based on the analysis of the intra-profile and lateral distribution of mobile forms of trace elements. The study targets in 2007-2018 were zonal types of permafrost soils of northern taiga landscapes, cryozems, occupying 84 % of the total study area, which are characterized by biogenic accumulation of mobile forms of Ni, Mn, and Cd in the upper AO, Acr horizons, and Cr, Ni, Co, Mn, Cu in the suprapermafrost CR horizon. We found out that the contamination of the soil cover of the industrial site at the Nyurba Mining and Processing Division is of a multielement nature with local highly to very highly contaminated areas. Over a ten-year observation period, areas of stable soil contamination are formed, where the main pollutants are mobile forms of Mn, Zn, Ni. We suggest that against the background of a natural geochemical anomaly associated with trap and kimberlite magmatism, technogenic anomalies are formed in the surface horizons of soils. They are spatially linked to technogenically transformed landscapes. One of the sources of pollutants is the dispersion of the solid phase of dust emissions in the direction of the prevailing winds, which leads to the formation of soils with abnormally high contents of mobile forms of Mn, Zn, Ni.
Checklists to support decision-making in regression testing

Practitioners working in large-scale software development face many challenges in regression testing activities. One of the reasons is the lack of a structured regression testing process. In this regard, checklists can help practitioners keep track of essential regression testing activities and add structure to the regression testing process to a certain extent. This study aims to introduce regression testing checklists so test managers/teams can use them: (1) to assess whether test teams/members are ready to begin regression testing, and (2) to keep track of essential regression testing activities while planning and executing regression tests. We used interviews, workshops, and questionnaires to design, evolve, and evaluate regression testing checklists. In total, 25 practitioners from 12 companies participated in creating the checklist. Twenty-three of them participated in checklists evolution and evaluation. We identified activities practitioners consider significant while planning, performing, and analyzing regression testing. We designed regression testing checklists based on these activities to help practitioners make informed decisions during regression testing. With the help of practitioners, we evolved these checklists into two iterations. Finally, the practitioners provided feedback on the proposed checklists. All respondents think the proposed checklists are useful and customizable for their environments, and 80% think checklists cover aspects essential for regression testing. The proposed regression testing checklists can be useful for test managers to assess their team/team members' readiness and decide when to start and stop regression testing. The checklists can be used to record the steps required while planning and executing regression testing. Further, these checklists can provide a basis for structuring the regression testing process in varying contexts.
Multi-Objective Multi-Stage Optimize Scheduling Algorithm for Nonlinear Virtual Work-Flow Based on Pareto

Work-flow scheduling is for finding the allocation method to achieve optimal resource utilization. In the scheduling process, constraints, such as time, cost and quality, need to be considered. How to balance these parameters is a NP-hard problem, and the nonlinear manufacturing process increases the difficulty of scheduling, so it is necessary to provide an effective heuristic algorithm. Aiming at these problems, a multi-objective nonlinear virtual work-flow model was set up, and a multi-objective staged scheduling optimization algorithm with the objectives of minimizing cost and time and maximizing quality was proposed. The algorithm includes three phases: the virtualization phase abstracts tasks and services into virtual nodes to generate a virtual work-flow model; the virtual scheduling phase divides optimized segments and obtains the solution set through reverse iteration; the generation phase obtains the scheduling path according to the Pareto dominance. The proposed algorithm performed 10.5% better in production quality than the minimum critical path algorithm, reduced the time to meet the time constraint by 9.1% and saves 13.7% more of the cost than the production accuracy maximization algorithm.
A Dynamic Fusion of Local and Non-Local Features-Based Feedback Network on Super-Resolution

Many Symmetry blocks were proposed in the Single Image Super-Resolution (SISR) task. The Attention-based block is powerful but costly on non-local features, while the Convolutional-based block is good at efficiently handling the local features. However, assembling two different Symmetry blocks will generate an Asymmetry block, making the classic Symmetry-block-based Super-Resolution (SR) architecture fail to deal with these Asymmetry blocks. In this paper, we proposed a new Dynamic fusion of Local and Non-local features-based Feedback Network (DLNFN) for SR, which focus on optimizing the traditional Symmetry-block-based SR architecture to hold two Symmetry blocks in parallel, making two Symmetry-blocks working on what they do best. (1) We introduce the Convolutional-based block for the local features and Attention-based network block for non-local features and propose the Delivery–Adjust–Fusion framework to hold these blocks. (2) we propose a Dynamic Weight block (DW block) which can generate different weight values to fuse the outputs on different feedback iterations. (3) We introduce the MAConv layer to optimize the In block, which is critical for our two blocks-based feedback algorithm. Experiments show our proposed DLNFN can take full advantage of two different blocks and outperform other state-of-the-art algorithms.
A Mixed-Reality-Based Unknown Space Navigation Method of a Flexible Manipulator

A hyper-redundant flexible manipulator is characterized by high degree(s) of freedom (DoF), flexibility, and environmental adaptability. It has been used for missions in complex and unknown spaces, such as debris rescue and pipeline inspection, where the manipulator is not intelligent enough to face complex situations. Therefore, human intervention is required to assist in decision-making and control. In this paper, we designed an interactive navigation method based on mixed reality (MR) of a hyper-redundant flexible manipulator in an unknown space. A novel teleoperation system frame is put forward. An MR-based interface was developed to provide a virtual model of the remote workspace and virtual interactive interface, allowing the operator to observe the real-time situation from a third perspective and issue commands to the manipulator. As for environmental modeling, a simultaneous localization and mapping (SLAM) algorithm based on an RGB-D camera is applied. Additionally, a path-finding and obstacle avoidance method based on artificial potential field (APF) is introduced to ensure that the manipulator can move automatically under the artificial command in the remote space without collision. The results of the simulations and experiments validate that the system exhibits good real-time performance, accuracy, security, and user-friendliness.
UGC Guidelines for Establishment of Education Departments in Multidisciplinary Institutions

The NEP 2020, has suggested several policy directions for offering multidisciplinary education, as well as to establish Department of Education in such institutions. This Holistic and Multidisciplinary Education underlines pulling of courses and resources from a variety of disciplines and helping in the holistic individual development including social and life skills. The purpose of this type of Education according to NEP policy is to link the teaching-learning process with life, community and the world of work including the environment across all disciplines/fields of study as well as STEM education. The ‘Education Departments’ are visualized as instruments towards contributing to multidisciplinary teaching-learning (curriculum, pedagogy, technology-enabled blended learning, assessment and evaluation, etc.) as visualized in the NEP 2020 and develop specialized expert in these areas. The present article has described in detail the entire procedure of establishing the ‘Department of Education’ in Multidisciplinary Institutions in terms of present and proposed Structural Arrangement; Functions and Programmes; Linkage to HEI’s as well as the operationalization of the NEP 2020 policies in this regard. Thus, the Education Departments of universities and colleges need to comprehensively focus on this task of merging. The UGC Guidelines for Transforming HEI’s into Multidisciplinary Institutions concluded with a statement that the micro-details/functions during the process of implementation may be worked out subsequently.
Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy

Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment.
One Weight Codes Over the Ring $F_q[v]/(v^s-1)$

In this study, we obtain one-Lee weight codes over a class of nonchain rings and study their structures.&#x0D; We give an explicit construction for one-Lee weight codes. A method to derive more one-Lee weight codes from given a one-Lee weight code is also represented. By defining and making use of a distance-preserving Gray map, we get a family of optimal one-Hamming weight codes over finite fields.
Pelabelan Jarak Tak Teratur Titik Pada Graf Persahabatan Lengkap Diperumum

Graph labeling is the labeling of graph elements such as vertex, edge and both. distance vertex irregular labeling is a type of labeling resulting from the development of distance magic labeling and (a, b)-distance anti-magic labeling. Let , be a simple graph. The distance vertex irregular labeling of is a vertex labeling so that the weight of each vertex is different. The weight of is calculated based on the sum of vertices label in the set of neighboring vertex , namely Distance vertex irregularity strength of , denoted as d , is the smallest value of the largest label so that has a distance vertex irregular labeling. This study aims to construct a generalized complete friendship graph , determine the labeling function, determine the distance vertex irregularity strength then formulate and prove the theorem resulting from the labeling. The object of this research is to label each vertex on a generalized complete friendship graph. This research method is a literature study obtained through various sources. Based on the research results, it is known that the graph has distance vertex irregular labeling. For an integer m and n, , the labeling function of is and . Distance vertex irregularity strength of generalized complete friendship graph is .
Comparisons of Online Assessment Platforms for Action Naming Tests in Healthy Young and Middle-aged Adults

Objectives: The purpose of this study was to evaluate the validity of action naming tests when using a chatbot-based online platform. The performance of healthy young adults and healthy middle-aged adults on action naming tests was compared between online assessment platforms.Methods: A total of 81 participants were included in this study (40 young adults, 41 middle-aged adults). Participants were divided into two groups according to the online assessment platforms, 40 individuals (20 young adults, 20 middle-aged adults) receiving the action naming tests via Zoom and 41 individuals (20 young adults, 21 middle-aged adults) via chatbot.Results: The results indicated that there was no significant difference in action naming performance across online assessment platforms. In addition, young adults showed statistically higher performance compared to the middle-aged adults. There was a significant main effect of verb argument structure on action naming performance, with one- and two-place verbs having a higher correct response rate than three-place verbs.Conclusion: Although the younger group performed better than the middle-aged group, there were no significant differences in action naming performance between chatbot and Zoom groups, suggesting that chatbot-based action naming tests can be used interchangeably with the established online platforms. Accordingly, it is expected that we will be able to provide appropriate online language assessment services for each client in a future clinical setting.
Task Alarm – An Application for Location-based Reminders

Smartphones have advanced rapidly over a very short time and one of the products of this advancements is the ability to accurately track location using GPS hardware present on phones. We can take advantage of this by using it to allow the user to make location-based reminders. In this paper we look at a solution developed by our team. We believe that our solution will help users to keep track of their tasks effectively. The application will be targeted towards people with an active lifestyle who prefer to do things in person or at location. This report goes in detail about our team’s motivation and reason to develop this application. It also goes into the working of the designed application
Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features

In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise. Therefore, intermediate-resolution sub-bands are good for invariant pattern recognition. Experiments on one printed Chinese character dataset and one 2D aircraft dataset show that our new method is better than two existing methods for a combination of rotation angles, scaling factors, and different noise levels in the input pattern images in most testing cases.
‘All possible sounds’: speech, music, and the emergence of machine listening

“Machine listening” is one common term for a fast-growing interdisciplinary field of science and engineering that “uses signal processing and machine learning to extract useful information from sound”. This article contributes to the critical literature on machine listening by presenting some of its history as a field. From the 1940s to the 1990s, work on artificial intelligence and audio developed along two streams. There was work on speech recognition/understanding, and work in computer music. In the early 1990s, another stream began to emerge. At institutions such as MIT Media Lab and Stanford’s CCRMA, researchers started turning towards “more fundamental problems of audition”. Propelled by work being done by and alongside musicians, speech and music would increasingly be understood by computer scientists as particular sounds within a broader “auditory scene”. Researchers began to develop machine listening systems for a more diverse range of sounds and classification tasks: often in the service of speech recognition, but also increasingly for their own sake. The soundscape itself was becoming an object of computational concern. Today, the ambition is “to cover all possible sounds”. That is the aspiration with which we must now contend politically, and which this article sets out to historicise and understand.