Reviewerly/exHarmony huggingface dataset:
Measurement of linear shear through optical vortices in digital shearing speckle pattern interferometry

Digital shearing speckle pattern interferometry (DSSPI) is a powerful interferometric technique used to visualize the slope contours undergoing static and dynamic deformations. Precise determination of the shear amount is crucial for quantitative analysis in DSSPI. However, accurately measuring the shear amount is often challenging due to factors such as optical device dimensions, deflections, aberrations, and misalignments. In this paper, we propose a novel method utilizing optical vortices deflection in pseudo-phase for shear measurement. This method eliminates the need for attaching calibration objects and replacing the light source, making it applicable to inaccessible or fragile samples. Experimental results demonstrate the effectiveness and accuracy of the proposed method in determining shear amounts in DSSPI. The method can be easily automated and integrated into existing setups, offering broader application prospects.
Fast Monocular Visual-Inertial Initialization Leveraging Learned Single-View Depth

In monocular visual-inertial navigation systems, it is ideal to initialize as quickly and robustly as possible.State-of-theart initialization methods typically make linear approximations using the image features and inertial information in order to initialize in closed-form, and then refine the states with a nonlinear optimization.While the standard methods typically wait for a 2sec data window, a recent work has shown that it is possible to initialize faster (0.5sec) by adding constraints from a robust but only up-to-scale monocular depth network in the nonlinear optimization.To further expedite the initialization, in this work, we leverage the scale-less depth measurements instead in the linear initialization step that is performed prior to the nonlinear one, which only requires a single depth image for the first frame.We show that the typical estimation of each feature state independently in the closed-form solution can be replaced by just estimating the scale and offset parameters of the learned depth map.Interestingly, our formulation makes it possible to construct small minimal problems in a RANSAC loop, whereas the typical linear system's minimal problem is quite large and includes every feature state.Experiments show that our method can improve the overall initialization performance on popular public datasets (EuRoC MAV and TUM-VI) over stateof-the-art methods.For the TUM-VI dataset, we show superior initialization performance with only a 0.3sec window of data, which is the smallest ever reported, and show that our method can initialize more often, robustly, and accurately in different challenging scenarios.
Hindsight States: Blending Sim &amp; Real Task Elements for Efficient Reinforcement Learning

Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort.In robotics, one approach to generate training data builds on simulations based on dynamics models derived from first principles.However, for tasks that, for instance, involve complex soft robots, devising such models is substantially more challenging.Being able to train effectively in increasingly complicated scenarios with reinforcement learning enables to take advantage of complex systems such as soft robots.Here, we leverage the imbalance in complexity of the dynamics to learn more sample-efficiently.We (i) abstract the task into distinct components, (ii) off-load the simple dynamics parts into the simulation, and (iii) multiply these virtual parts to generate more data in hindsight.Our new method, Hindsight States (HiS), uses this data and selects the most useful transitions for training.It can be used with an arbitrary off-policy algorithm.We validate our method on several challenging simulated tasks and demonstrate that it improves learning both alone and when combined with an existing hindsight algorithm, Hindsight Experience Replay (HER).Finally, we evaluate HiS on a physical system and show that it boosts performance on a complex table tennis task with a muscular robot.Videos and code of the experiments can be found on webdav.tuebingen.mpg.de/his/.
Motion Planning (In)feasibility Detection using a Prior Roadmap via Path and Cut Search

Motion planning seeks a collision-free path in a configuration space (C-space), representing all possible robot configurations in the environment.As it is challenging to construct a C-space explicitly for a high-dimensional robot, we generally build a graph structure called a roadmap, a discrete approximation of a complex continuous C-space, to reason about connectivity.Checking collision-free connectivity in the roadmap requires expensive edge-evaluation computations, and thus, reducing the number of evaluations has become a significant research objective.However, in practice, we often face infeasible problems: those in which there is no collision-free path in the roadmap between the start and the goal locations.Existing studies often overlook the possibility of infeasibility, becoming highly inefficient by performing many edge evaluations.In this work, we address this oversight in scenarios where a prior roadmap is available; that is, the edges of the roadmap contain the probability of being a collision-free edge learned from past experience.To this end, we propose an algorithm called iterative path and cut finding (IPC) that iteratively searches for a path and a cut in a prior roadmap to detect infeasibility while reducing expensive edge evaluations as much as possible.We further improve the efficiency of IPC by introducing a second algorithm, iterative decomposition and path and cut finding (IDPC), that leverages the fact that cut-finding algorithms partition the roadmap into smaller subgraphs.We analyze the theoretical properties of IPC and IDPC, such as completeness and computational complexity, and evaluate their performance in terms of completion time and the number of edge evaluations in large-scale simulations.
Assessment of knowledge and attitude regarding self-care among patients undergoing hemodialysis

Background: Chronic kidney disease is characterized by kidney impairment and a glomerular filtration rate lower than sixty milliliters per minute for more than three months.Furthermore, hemodialysis patients are at significant risk for a range of problems, which might result from kidney disorders or the hemodialysis operations themselves.The purpose of this study is to assess hemodialysis patients' knowledge and attitudes toward personalized care.Methods: A four-month cross-sectional study was conducted.The nonprobability convenient method of sampling was used, and the sample size was 250 participants (dialysis patients).After removing individuals who did not respond to the survey), our final number of participants was 170 hemodialyses (HD) individuals, and the information was obtained utilizing an online/offline survey with a questionnaire.Results: This study included an overall sample of 170 people ranging in age from 18 to 85 years, with 62 (36.5%) females and 108 (63.5%) males.Diabetes mellitus was found to be the disease with the greatest incidence among the group of individuals (67.41%), followed by hypertension 50 (29.4%),cardiovascular disease 25 (14.7%),hepatic disease 10 (5.88%), and most commonly used treatment in our study was antidiabetic medications.Conclusion: When it came to renal diet, drugs, and modifications to their lifestyles, the individuals in this study exhibited low Knowledge, Attitude, and Self-Care.Our research showed that male individuals knew more about the side effects of hemodialysis than female subjects.The maintenance hemodialysis patients' comprehension and practice were enhanced by receiving guidance on diet, medication, and lifestyle adjustments.
Developing skills for adaptation to the digital era

The modern concept of continuing education supports the idea of the need to update knowledge, skills and abilities throughout life. This concept contributes to the permanent development of human resources, which must meet certain requirements, arising primarily in the economic environment. Therefore, it is important to pay special attention to the formation of the quality of human resources arising from the specifics of the economic conditions. From the perspective of the individual, this would mean understanding his life prospects and his own professional and educational trajectories through the lens of the development of the economic context. At the organizational level, the qualitative training of human resources is related to the ability of various structures to adapt to the new economic conditions. The digitization of the economy, which is becoming an objective reality for many countries, proposes special requirements for the qualitative characteristics of human resources. Currently, one of the main problems and a barrier in the transition to the digital economy is the lack of human resources prepared for work in the new conditions. In the context of Industry 4.0, a significant increase in the number of qualified specialists with a high level of IT potential is required, which can only be achieved by adjusting the education system to the realities of the functioning of the digital economy. This article discusses the state and prospects of the development of the digital economy and the digitization processes of education in the context of the qualitative training of future human resources.
Cross-domain video action recognition via adaptive gradual learning

Video-based Unsupervised Domain Adaptation (UDA) methods concentrate on addressing domain shift and improving the robustness of video models. It can be naturally applied to cross-domain action recognition tasks, but the inherent complexity of videos makes this task more challenging. Though several recent attempts have achieved superior performance in the video UDA field, most of them intend to mitigate domain shift directly from the source domain to the target domain. These methods will cause a performance drop when the domain distribution shift is large. To better realize the domain adaptation, this paper proposes an effective gradual learning framework by constructing multiple auxiliary domains to achieve progressive transfer for cross-domain video action recognition. Specifically, a Dynamic CutMix Mechanism (DCM) is introduced to build the auxiliary domains that mitigate the domain gap caused by object distance and background discrepancies. Furthermore, the Gradual Transfer Strategy (GTS) utilizes these auxiliary domains to realize the cross-domain action classification gradually. Extensive experiments validate the effectiveness of our proposed method, and the experimental results can significantly outperform state-of-the-art methods on multiple benchmark cross-domain datasets.
Exploring the Relationship Between Cybersecurity Culture and Cyber-Crime Prevention: A Systematic Review

Cybercrime requires a multidimensional approach addressing technical, organisational, and human factors to prevent cyber-attacks. This systematic review highlights the significance of a strong cybersecurity culture in mitigating the risks of cyberattacks and enhancing an organisation's resilience. Implementing training and awareness programmes, establishing leadership support and accountability mechanisms, and fostering employee engagement are strategies for promoting a strong cybersecurity culture. However, fostering a robust culture of cybersecurity can be challenging and requires the participation of various stakeholders. Training and awareness programmes can significantly improve employee cybersecurity knowledge and behaviour, and leadership support is essential for establishing a cybersecurity culture. It is possible to enforce employee compliance with cybersecurity policies and procedures through accountability mechanisms, such as consequences for noncompliance and periodic security audits. Involving employees in the creation of cybersecurity policies and procedures, as well as recognising and rewarding responsible behaviour, can increase employee engagement and investment in cybersecurity. Although technical measures such as firewalls and encryption are essential for defending against cyber-attacks, a strong cybersecurity culture is necessary to mitigate cybercrime risks and enhance an organisation's resilience. Future research should identify and evaluate effective strategies for cultivating a robust cybersecurity culture in the context of preventing cybercrime. Cybersecurity should be a top priority for businesses and individuals, who must take preventative measures against cyber-attacks. By fostering a robust culture of cybersecurity, businesses can increase their resilience and reduce the risks of cybercrime.
Quality Management

Abstract Quality Management (QM) is responsible for overseeing all activities and tasks needed to maintain a desired level of quality. QM in Software Development traditionally has three main components: quality planning, quality assurance, and quality control. In many agile organizations, QM is becoming closely integrated with the DevOps organization. Quality Assurance (QA) is responsible for setting up the organization and its processes to ensure the desired level of quality. In an agile organization, this means that QA needs to be closely aligned with DevOps. Quality Control (QC) is responsible for the output, usually by implementing a test strategy along the various stages of the DevOps cycle. Quality Planning is responsible for setting up the quality and test plans. In a DevOps organization, this will be a continuous process (Fig. 30.1).
Information and the Formation of Inflation Expectations by Firms: Evidence from a Survey of Israeli Firms

This study analyzes how firms form their inflation expectations during a regime change in monetary policy and a transition to a low-inflation environment. Using the Bank of Israel survey of firms, we document the basic properties of firmsâ€™ inflation expectations and examine how Israeli firms update their inflation expectations after receiving new information about inflation or monetary policy. We find that even after successful de-dollarization and disinflation a positive inflation surprise leads to a sizable upward adjustment in inflation expectations for the next year and quarter. A surprise hike in the monetary interest rate leads to a downward adjustment in inflation expectations.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Chan-Vese Attention U-Net: An Attention Mechanism for Robust Segmentation

When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioning the possibility of using such an algorithm in applications where there is little room for doubt. We propose in this paper a new attention gate based on the use of Chan-Vese energy minimization to control more precisely the segmentation masks given by a standard CNN architecture such as the U-Net model. This mechanism allows to obtain a constraint on the segmentation based on the resolution of a PDE. The study of the results allows us to observe the spatial information retained by the neural network on the region of interest and obtains competitive results on the binary segmentation. We illustrate the efficiency of this approach for medical image segmentation on a database of MRI brain images.
Analysis Of School Literacy Policies In The Face Of The Society Era 5.0

Industry 4.0, also known as the disruptive period, stresses increasing efforts to place humans at the center of innovation (human-centered) while technical developments are used to improve the quality of life, social responsibility, and sustainability in all disciplines, including education. In light of the 5.0 social media age, the purpose of this study is to examine school literacy policies. This study uses qualitative analysis in conjunction with a library study and the Society 5.0 concept approach to literacy policy. Journals, articles, books, and other pertinent references were used as data sources for this study. Using library studies and documentation, data collection strategies are used. The findings revealed that in the period of society 5.0, literacy policies based on old literacy (basic literacy) must be replaced with new literacy based on the internet, technology, and social media.
A Belief Model for Conflicting and Uncertain Evidence: Connecting Dempster-Shafer Theory and the Topology of Evidence

One problem to solve in the context of information fusion, decision-making, and other artificial intelligence challenges is to compute justified beliefs based on evidence. In real-life examples, this evidence may be inconsistent, incomplete, or uncertain, making the problem of evidence fusion highly non-trivial. In this paper, we propose a new model for measuring degrees of beliefs based on possibly inconsistent, incomplete, and uncertain evidence, by combining tools from Dempster-Shafer Theory and Topological Models of Evidence. Our belief model is more general than the aforementioned approaches in two important ways: (1) it can reproduce them when appropriate constraints are imposed, and, more notably, (2) it is flexible enough to compute beliefs according to various standards that represent agents' evidential demands. The latter novelty allows the users of our model to employ it to compute an agent's (possibly) distinct degrees of belief, based on the same evidence, in situations when, e.g, the agent prioritizes avoiding false negatives and when it prioritizes avoiding false positives. Finally, we show that computing degree of belief with this model is #P-complete in general.
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax

The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness. In the context of multi-objective optimization this constraint can require solutions to be Pareto-optimal. In this paper we study how the GSEMO algorithm with additional diversity-enhancing heuristic optimizes a diversity of its population on a bi-objective benchmark problem OneMinMax, for which all solutions are Pareto-optimal. We provide a rigorous runtime analysis of the last step of the optimization, when the algorithm starts with a population with a second-best diversity, and prove that it finds a population with optimal diversity in expected time O(n2), when the problem size n is odd. For reaching our goal, we analyse the random walk of the population, which reflects the frequency of changes in the population and their outcomes.
Fixed-Parameter Tractability of the (1 + 1) Evolutionary Algorithm on Random Planted Vertex Covers

We present the first parameterized analysis of a standard (1+1) Evolutionary Algorithm on a distribution of vertex cover problems. We show that if the planted cover is at most logarithmic, restarting the (1+1) EA every O(n log n) steps will find a cover at least as small as the planted cover in polynomial time for sufficiently dense random graphs p > 0.71. For superlogarithmic planted covers, we prove that the (1+1) EA finds a solution in fixed-parameter tractable time in expectation.
Teacher Educator’s Qualities for Successful Teaching: Students’ Perspectives on Teacher’s Personal Traits in Nepalese Context

The nature of teacher education in Nepal is being questioned by various critics because they argue that it only focuses on the formal or theoretical knowledge of the teachers rather than considering practical and behavioral aspects of teachers’ professional development. On this ground, this phenomenological research primarily aims at identifying students’ perspectives on personal qualities/characteristics of teacher educators and investigating how such personal qualities/characteristics contribute to successful teaching in the context of Nepal. The data has been collected through personal and structured interview taken with 10 students (who are prospective teachers as well) purposively selected from the 5 campuses of Kailali district that run B. Ed. and M. Ed. Programs. Through the interpretation and analysis of the data, the study reveals that personal characteristics of teacher educators are essential for successful teaching and learning. Such qualities should be recognized as the formal parameters of assessing teacher’s qualification for improving the entire course of teacher education.
On Simple Expectations and Observations of Intelligent Agents: A Complexity Study

Public observation logic (POL) reasons about agent expectations and agent observations in various real world situations. The expectations of agents take shape based on certain protocols about the world around and they remove those possible scenarios where their exceptions and observations do not match. This in turn influences the epistemic reasoning of these agents. In this work, we study the computational complexity of the satisfaction problems of various fragments of POL. In the process, we also highlight the inevitable link that these fragments have with the well-studied Public announcement logic.
Argumentation Frameworks Induced by Assumption-based Argumentation: Relating Size and Complexity

A key ingredient of computational argumentation in AI is the generation of arguments in favor of or against claims under scrutiny. In this paper we look at the complexity of argument construction and reasoning in the prominent structured formalism of assumption-based argumentation (ABA). We point out that reasoning in ABA by means of constructing an abstract argumentation framework (AF) gives rise to two main sources of complexity: (i) constructing the AF and (ii) reasoning within the constructed graph. Since both steps are intractable in general, it is no surprise that the best performing state-of-the-art ABA reasoners skip the instantiation procedure entirely and perform tasks directly on the input knowledge base. Driven by this observation, we identify and study atomic and symmetric ABA, two ABA fragments that preserve the expressive power of general ABA, and that can be utilized to have milder complexity in the first or second step. We show that using atomic ABA allows for an instantiation procedure for general ABA leading to polynomially-bounded AFs and that symmetric ABA can be used to create AFs that have mild complexity to reason on. By an experimental evaluation, we show that using the former approach with modern AF solvers can be competitive with state-of-the-art ABA solvers, improving on previous AF instantiation approaches that are hindered by intractable argument construction.
Explainable Representations for Relation Prediction in Knowledge Graphs

Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representations of entities that preserve structural and local graph neighbourhood properties, but sacrifice explainability. However, in tasks such as link or relation prediction, understanding which specific features better explain a relation is crucial to support complex or critical applications. We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects (i.e., subgraphs) between entities and learning representations for each subgraph, producing a multi-faceted and explainable representation. We evaluate SEEK on two real-world highly complex relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction. Our extensive analysis using established benchmarks demonstrates that SEEK achieves comparable or even superior performance to standard learning representation methods while identifying both sufficient and necessary explanations based on shared semantic aspects.