Reviewerly/exHarmony huggingface dataset:
Representing Hierarchical Structured Data Using Cone Embedding

Extracting hierarchical structure in graph data is becoming an important problem in fields such as natural language processing and developmental biology. Hierarchical structures can be extracted by embedding methods in non-Euclidean spaces, such as Poincaré embedding and Lorentz embedding, and it is now possible to learn efficient embedding by taking advantage of the structure of these spaces. In this study, we propose embedding into another type of metric space called a metric cone by learning an only one-dimensional coordinate variable added to the original vector space or a pre-trained embedding space. This allows for the extraction of hierarchical information while maintaining the properties of the pre-trained embedding. The metric cone is a one-dimensional extension of the original metric space and has the advantage that the curvature of the space can be easily adjusted by a parameter even when the coordinates of the original space are fixed. Through an extensive empirical evaluation we have corroborated the effectiveness of the proposed cone embedding model. In the case of randomly generated trees, cone embedding demonstrated superior performance in extracting hierarchical structures compared to existing techniques, particularly in high-dimensional settings. For WordNet embeddings, cone embedding exhibited a noteworthy correlation between the extracted hierarchical structures and human evaluation outcomes.
The Quantum States of a Graph

Quantum codes are crucial building blocks of quantum computers. With a self-dual quantum code is attached, canonically, a unique stabilised quantum state. Improving on a previous publication, we show how to determine the coefficients on the basis of kets in these states. Two important ingredients of the proof are algebraic graph theory and quadratic forms. The Arf invariant, in particular, plays a significant role.
Probabilistic causal bipolar abstract argumentation: an approach based on credal networks

Abstract The Bipolar Argumentation Framework approach is an extension of the Abstract Argumentation Framework. A Bipolar Argumentation Framework considers a support interaction between arguments, besides the attack interaction. As in the Abstract Argumentation Framework, some researches consider that arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained from a Bipolar Argumentation Framework under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real application domains there is a need for aggregating probability values from different sources so it is not suitable to aggregate such probability values in a unique probability distribution. To tackle this challenge, we use credal networks theory for modelling the uncertainty of the degree of belief of arguments in a BAF. We also propose an algorithm for calculating the degree of uncertainty of the extensions inferred by a given argumentation semantics. Moreover, we introduce the idea of modelling the support relation as a causal relation. We formally show that the introduced approach is sound and complete w.r.t the credal networks theory.
Environment Perception with Chameleon-Inspired Active Vision Based on Shifty Behavior for WMRs

To improve the environment perception ability of wheeled mobile robots (WMRs), the visual behavior mechanism of the negative-correlation motion of chameleons is introduced into the binocular vision system of WMRs, and a shifty-behavior-based environment perception model with chameleon-inspired active vision for WMRs is established, where vision–motor coordination is achieved. First, a target search sub-model with chameleon-inspired binocular negative-correlation motion is built. The relationship between the rotation angles of two cameras and the neck and the camera’s field of view (FOV), overlapping angle, region of interest, etc., is analyzed to highlight the binocular negative-correlation motion compared with binocular synchronous motion. The search efficiency of the negative-correlation motion is doubled compared with binocular synchronous motion, and the search range is also greatly improved. Second, the FOV model of chameleon-inspired vision perception based on a shifty-behavior mode is set up. According to the different functional requirements of target searching and tracking stages, the shift of the robot visual behavior is analyzed from two aspects, measuring range and accuracy. Finally, a chameleon-inspired active-vision-based environment perception strategy for mobile robots is constructed based on the shifty-behavior mode, and experimental verification is deployed, which achieves the reproduction of the visual behavior of chameleons in the vision system of mobile robots with satisfactory results.
Well-Separated Pair Decompositions for High-Dimensional Datasets

Well-separated pair decomposition (WSPD) is a well known geometric decomposition used for encoding distances, introduced in a seminal paper by Paul B. Callahan and S. Rao Kosaraju in 1995. WSPD compresses O(n2) pairwise distances of n given points from Rd in O(n) space for a fixed dimension d. However, the main problem with this remarkable decomposition is the “hidden” dependence on the dimension d, which in practice does not allow for the computation of a WSPD for any dimension d&gt;2 or d&gt;3 at best. In this work, I will show how to compute a WSPD for points in Rd and for any dimension d. Instead of computing a WSPD directly in Rd, I propose to learn nonlinear mapping and transform the data to a lower-dimensional space Rd′, d′=2 or d′=3, since only in such low-dimensional spaces can a WSPD be efficiently computed. Furthermore, I estimate the quality of the computed WSPD in the original Rd space. My experiments show that for different synthetic and real-world datasets my approach allows that a WSPD of size O(n) can still be computed for points in Rd for dimensions d much larger than two or three in practice.
Watermarking in Medical Image

Medical image security is possible using digital watermarking techniques. Important information is included in a host medical image in order to provide integrity, consistency, and authentication in the healthcare information system. This paper introduces a proposed method for embedding invisible watermarking in the 3D medical image. The cover medical image used is DICOM which consists of a number of slices, each one representing a sense, firstly must separate the ROI (Region of Interest) and NROI (Not Region Of Interest) for each slice, the separation process performed by the particular person who selected by hand the ROI. The embedding process is based on a key generated from Arnold's chaotic map used as the position of a pixel in the slices with the highest saturation for embedding a secret message in the NROI because the ROI contains the information of the ill, so it cannot be modified. The evaluation of the proposed method using PSNR and MSE shows good results according to both requirements of the watermark system, the invisibility and quality of the watermarked medical image where the PSNR value is up to 43.3936 and MSE is up to 0.000041.
Higher Educational Institutions of the Musical Profile in the Volga Region in the Second half of the 1930s – 1950s: Problems of Personnel Support

Relevance . The state policy of most countries of the world is aimed at increasing the qualitative level and competitiveness of domestic education systems, the development of individual industries in the field of education. Positive changes in this area can help studying the historical experience of building national education models, the use of domestic educational traditions of the past in modern practice. The purpose of the study is to cover and popularize approaches to ensure the staff of the superior musical and educational institutions in the Volga region in the second half of the 1930s-the 1950s. The objectives of the study: to study factual material reflecting the ways of solving personnel problems in domestic conservatories in the second half of the 1930s – 1950s; identify the degree of effectiveness of this activity. Methodology . In the course of the study, the following sources were used: scientific articles, dissertations, collections of documents, archive materials. The methodological basis of the work was the principles of objectivity and comprehensiveness, as well as a number of scientific methods: system analysis, historical, typological, historical and comprehensive. Results. As a result of this study, approaches to the personnel support of the musical higher educational institutions of the Volga region in the second half of the 1930s-1950s were specified. Conclusion. The study showed that in the conditions of modernization development of the second half of the 1930s and 1950s, due to migration processes, the problem of personnel recruitment of musical universities (conservatives) was effectively solved in the Volga region. The decisive role in this issue belonged to the state system of distribution of specialists. Capital musicians (Moscow, Leningrad, etc.), evacuated to the region during the Great Patriotic War, played key importance for replenishing and strengthening the states of the Volga conservatory. Thanks to the shots involved during this period, musical and cultural traditions were formed, pedagogical and performing regional schools were created, several generations of professional musicians were brought up.
Model adaptation via credible local context representation

Abstract Conventional model transfer techniques, requiring the labelled source data, are not applicable in the privacy‐protected medical fields. For the challenging scenarios, recent source data‐free domain adaptation (SFDA) has become a mainstream solution but losing focus on the inter‐sample class information. This paper proposes a new Credible Local Context Representation approach for SFDA. Our main idea is to exploit the credible local context for more discriminative representation. Specifically, we enhance the source model's discrimination by information regulating. To capture the context, a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context. In the epoch‐wise adaptation, deep clustering‐like training is conducted with two major updates. First, the context for all target data is constructed and then the context‐fused pseudo‐labels providing semantic guidance are generated. Second, for each target data, a weighting fusion on its context forms the anchored neighbourhood structure; thus, the deep clustering is switched from individual‐based to coarse‐grained. Also, a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse‐grained learning. Experiments on three benchmarks indicate that the proposed method can achieve state‐of‐the‐art results.
Cybersecurity effectiveness in UK construction firms: an extended McKinsey 7S model approach

Purpose This study aims to assess the essential elements of internal organisational capability that influence the cybersecurity effectiveness of a construction firm. An extended McKinsey 7S model is used to analyse the relationship between a construction firm's cybersecurity effectiveness and nine internal capability elements: shared values, strategy, structure, systems, staff, style, skills, relationships with third parties and regulatory compliance. Design/methodology/approach Based on a quantitative research strategy, this study collected data through a cross-sectional survey of professionals working in the construction sector in the United Kingdom (UK). The collected data was analysed using descriptive and inferential statistical methods. Findings The findings underlined systems, regulatory compliance, staff and third-party relationships as the most significant elements of internal organisational capability influencing a construction firm's cybersecurity effectiveness, organised in order of importance. Research limitations/implications Future research possibilities are proposed including the extension of the proposed diagnostic model to consider additional external factors, examining it under varying industrial relationship conditions and developing a dynamic framework that helps improve cybersecurity capability levels while overseeing execution outcomes to ensure success. Practical implications The extended McKinsey 7S model can be used as a diagnostic tool to assess the organisation's internal capabilities and evaluate the effectiveness of implemented changes. This can provide specific ways for construction firms to enhance their cybersecurity effectiveness. Originality/value This study contributes to the field of cybersecurity in the construction industry by empirically assessing the effectiveness of cybersecurity in UK construction firms using an extended McKinsey 7S model. The study highlights the importance of two additional elements, third-party relationships and construction firm regulatory compliance, which were overlooked in the original McKinsey 7S model. By utilising this model, the study develops a concise research model of essential elements of internal organisational capability that influence cybersecurity effectiveness in construction firms.
Implications of computer code translation for translation studies

This article explores the intersection of the translation of machine code and translation of natural languages. Starting with the deep-seated metaphor the brain is a computer, this study demonstrates the extent to which computer science, cognitive science, linguistics and translation are intertwined. The parallels between difficulties of translating computer code and natural languages point to the failure to find a workable interlingua and the importance of complexity studies and emergent properties for both fields. Thus translation studies would do well to examine more carefully the extent to which a computational understanding of the brain has shaped basic concepts and approaches to translation. At the same time, homologies between the market for translation of computer languages and natural languages and the need in programming to be more sensitive to the needs of non-English speakers suggests that translation studies has much to offer to computer science.
Complementary Coarse-to-Fine Matching for Video Object Segmentation

Semi-supervised Video Object Segmentation (VOS) needs to establish pixel-level correspondences between a video frame and preceding segmented frames to leverage their segmentation clues. Most works rely on features at a single scale to establish those correspondences, e.g., perform dense matching with Convolutional Neural Network (CNN) features from a deep layer. Differently, this work explores complementary features at different scales to pursue more robust feature matching. A coarse feature from a deep layer is first adopted to get coarse pixel-level correspondences. We hence evaluate the quality of those correspondences, and select pixels with low-quality correspondences for fine-scale feature matching. Segmentation clues of previous frames are propagated by both coarse and fine-scale correspondences, which are fused with appearance features for object segmentation. Compared with previous works, this coarse-to-fine matching scheme is more robust to distractions by similar objects and better preserves object details. The sparse fine-scale matching also ensures a fast inference speed. On popular VOS datasets including DAVIS and YouTube-VOS, the proposed method shows promising performance compared with recent works.
Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous Resources

Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in the face of variable workloads, auto-scaling techniques have been studied to adapt operator parallelism at run-time. However, most of the effort has been spent under the assumption of homogeneous computing infrastructures, neglecting the complexity of modern environments. We consider the problem of deciding both how many operator replicas should be executed and which types of computing nodes should be acquired. We devise heterogeneity-aware policies by means of a two-layered hierarchy of controllers. While application-level components steer the adaptation process for whole applications, aiming to guarantee user-specified requirements, lower-layer components control auto-scaling of single operators. We tackle the fundamental challenge of performance and workload uncertainty, exploiting Bayesian optimization (BO) and reinforcement learning (RL) to devise policies. The evaluation shows that our approach is able to meet users’ requirements in terms of response time and adaptation overhead, while minimizing the cost due to resource usage, outperforming state-of-the-art baselines. We also demonstrate how partial model information is exploited to reduce training time for learning-based controllers.
How to verify identity in the continuous variable quantum system?

Continuous variable quantum cryptography has developed rapidly in recent decades, but how to verify identity in the continuous variable quantum system is still an urgent issue. To solve this problem, we propose a continuous variable quantum identification (CV-QI) protocol based on the correlation of the two-mode squeezed vacuum state and the continuous variable teleportation. The bidirectional identity verification between two participants of the communication can be achieved by the proposed CV-QI protocol. To guarantee the security, we make full use of the decoy state sequences during the whole process of the proposed CV-QI protocol. Besides, we provide the security analyses of the proposed CV-QI protocol, and analyses indicate that the security of the proposed CV-QI protocol is guaranteed.
Importance-Based Ray Strategies for Dynamic Diffuse Global Illumination

In this paper, we propose a first and efficient ray allocation technique for Dynamic Diffuse Global Illumination (DDGI) using Multiple Importance Sampling (MIS). Our technique, IS-DDGI, extends DDGI by incorporating a set of importance-based ray strategies that analyze, allocate, and manage ray resources on the GPU. We combine these strategies with an adaptive historical and temporal frame-to-frame analysis for an effective reuse of information and a set of GPU-based optimizations for speeding up ray allocation and reducing memory bandwidth. Our IS-DDGI achieves similar visual quality to DDGI with a speedup of 1.27x to 2.47x in total DDGI time and 3.29x to 6.64x in probes ray tracing time over previous technique [Majercik et al. 2021]. Most speedup of IS-DDGI comes from probes ray tracing speedup.
Lightweight and privacy-preserving multi-server authentication scheme based on PUF and biometrics

With the development of wireless communication technology and the rapid increase of user data, multi-server key agreement authentication scheme has been widely used. In order to protect users’ privacy and legitimate rights, a two-factor multi-server authentication scheme based on device PUF and users’ biometrics is proposed. The users’ biometrics are combined with the physical characteristics of the Physically Unclonable Functions (PUF) as authentication factors, which not only ensures the security of the scheme, but it also is user-friendly without a password. The proposed scheme can be applied to telemedicine, smart home, Internet of Vehicles and other fields to achieve mutual authentication and key agreement between users and servers. In order to prove the security of the proposed scheme, the widely accepted ROR model and BAN logic are used for formal security analysis. The scheme can effectively resist various security attacks, and the comparison with existing schemes shows that it has better performance in terms of communication cost and computational complexity.
Efficient Spatial Resampling Using the PDF Similarity

In real-time rendering, spatiotemporal reservoir resampling (ReSTIR) is a powerful technique to increase the number of candidate samples for resampled importance sampling. However, reusing spatiotemporal samples is not always efficient when target PDFs for the reused samples are dissimilar to the integrand. Target PDFs are often spatially different for highly detailed scenes due to geometry edges, normal maps, spatially varying materials, and shadow edges. This paper introduces a new method of rejecting spatial reuse based on the similarity of PDF shapes for single-bounce path connections (e.g., direct illumination). While existing rejection methods for ReSTIR do not support arbitrary materials and shadow edges, our PDF similarity takes them into account because target PDFs include BSDFs and shadows. In this paper, we present a rough estimation of PDF shapes using von Mises--Fisher distributions and temporal resampling. We also present a stable combination of our rejection method and the existing rejection method, considering estimation errors due to temporal disocclusions and moving light sources. This combination efficiently reduces the error around shadow edges with temporal continuities. By using our method for a ReSTIR variant that reuses shadow ray visibility for the integrand, we can reduce the number of shadow rays while preserving shadow edges.
Hardware-efficient algorithm and architecture design with memory and complexity reduction for semi-global matching

Among the stereo matching algorithms, the semi-global matching (SGM) is an efficient and high-accuracy method. However, its huge demand for memory access and high computational complexity makes it difficult to achieve a real-time and efficient processing on hardware. Based on the spatial redundancy found in the matching cost, we propose some effective techniques to reduce the requirement of on-chip and off-chip memory, while simultaneously greatly lower the computational complexity. Experimental results present that the proposed SGM algorithm reduces the computational complexity by 71%–74% and has almost the same quality of disparity map compared with the original 8-path SGM. The proposed 3-path fully-pipelined architecture is implemented on the Xilinx VCU-106 with a throughput of 1920 × 1080/54 fps. We also synthesize and layout it with TSMC 40 nm standard library, leading to an area of 8.1 mm2 with throughput of 1920 × 1080/192 fps. The million disparity estimation per second (MDE/s) of the proposed design reaches up to 50,960, which outperforms conventional ASIC implementations.
Tutoring support of the educational process of disabled students in developing competences

This article examines the issue of tutoring in the educational process for students with disabilities. In recent years, the number of students with disabilities has tended to increase, so it is important to develop tutoring support for mastering not only professional, but also supraprofessional (general cultural) competences for this category of students. The role of a tutor in an educational institution can be either a teacher, a group supervisor or a specially trained specialist working in an inclusive education department. The authors consider the topical issue related to the support of the educational process for students with disabilities and it is not only the construction of an individual learning path, but also the formation of universal culture, spiritual and moral relationships, the development of cultural space and in general the socialization of the student with disabilities. These determinants will be optimally absorbed and anchored by the study population in the HEI’s educational space, accompanied by a tutor.