Reviewerly/exHarmony huggingface dataset:
Assessing the Moderating Effect of Innovation on the Relationship between Information Technology and Supply Chain Management: An Empirical Examination

This study examines how innovation (INN) influences the relationship between supply chain management and information technology in Jordan. 211 employees of Jordanian industrial enterprises who work in the Operations Department provided information for the study, which examines this subject. The findings indicate a close connection between information technology and supply chain management. Innovation also dramatically modifies the interaction between supply chain management and information technology. Management help may be the subject of future research.
A General Approach for Constrained Robotic Coverage Path Planning on 3D Freeform Surfaces

There are many industrial robotic applications which require a robot manipulator’s end-effector to fully cover a 3D surface region in a constrained motion. Constrained surface coverage in this context is focused on placing commonly used coverage patterns (such as raster, spiral, or dual-spiral) onto the surface for the manipulator to follow. The manipulator must continuously satisfy surface task constraints imposed on the end-effector while maintaining manipulator joint constraints. While there is substantial research for coverage on planar surfaces, methods for constrained coverage of 3D (spatial) surfaces are limited to certain (parametric or spline) surfaces and do not consider feasibility systematically given manipulator and task constraints. There is a lack of fundamental research to address the general problem: given a manipulator, a 3D freeform surface, and task constraints, whether there exists a feasible continuous motion plan to cover the surface, and if so, how to produce a uniform coverage path that best satisfies task constraints. In this paper, we introduce a general approach to address this fundamental but largely open coverage problem. We have applied our approach to example 3D freeform surface coverage tasks in simulation and real world environments with a 7-DOF robotic manipulator to demonstrate its effectiveness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the constrained coverage path planning problem on 3D freeform surfaces for many industrial applications, such as painting, spray coating, abrasive blasting, polishing, shotcreting, etc. It provides a principled and general approach that includes an automatic robotic system to find feasible robotic end-effector paths for covering a 3D freeform surface with some interaction from a human worker who provides key parameters related to the specific task without being an expert in robotics. Therefore, the approach enables a human worker who only has the domain knowledge of a specific coverage task to operate the general and automatic robotic system effectively for completing the task.
Musical movement as a means of comprehending the inner form of a musical work

Background. The issue of the methods in aesthetic education, including music, is of particular importance in connection with the task of personal development and formation of the moral sphere. The question of organizing the work that the listener does when perceiving a piece of music arises. Practical solution to this problem requires considering and identifying the content and structure of musical perception. Objectives. The article aims (1) to research the mechanisms and orientation of musical perception; (2) to analyze approaches to the problem of musical content in musicology and music psychology; (3) to reveal the possibilities of rhythmoplasty in the formation of musical perception as the ability to understand and experience and to respond to sounding music. Methods. The work is a theoretical and analytical study summarizing the experience of practice, musical movement, and other methods of rhythmoplasty. The methods of logical and meaningful analysis of theoretical approaches to the description of musical perception are used. The study of musical-motor forms created in practice is carried out from the point of view of reflecting the formal structures of music, as well as from the point of view of revealing the musical content. Sample. Musical-motor forms and compositions created in the practice of musical movement, as well as written reviews of participants of classes and members of the “Heptahor” studio are used as a material for analysis. Results. The conducted research allows us to confirm the assumption that the expressive movements carried out in response to sounding music reflect rather the internal, meaningful and semantic side of the music than its external, formal-structural aspects. Conclusion. The results obtained allow us to define the process of musical perception as a complex activity of “listening” to the inner semantic side of a musical work, which is not reducible to purely analytical work. It is also shown that using the means of rhythmoplasty makes it possible to reflect the inner semantic side of a musical work and translate a musical image into spatio-temporal forms.
TranSpeckle: An edge‐protected transformer for medical ultrasound image despeckling

Abstract The transformer, a type of neural architecture, has demonstrated exceptional performance improvements in vision and natural language tasks. While overcoming the disadvantages of limited perceptual field and non‐adaptive input content exhibited in CNNs, the computational complexity of the Transformer model increases quadratically with spatial resolution. As such, this model is not frequently employed in image processing tasks such as image denoising, and there is a shortage of studies that investigate ultrasonic image multiplication speckle removal. In light of this, we present TranSpeckle, an effective and efficient despeckle architecture that employs Multi‐Dconv Head Transposed Attention and Dconv Feed‐Forward Network as the core components of its Transformer block. Multiple Transformer blocks are then utilized to implement a hierarchical encoder‐decoder network. TranSpeckle architecture considerably reduces the computational complexity of feature maps while also effectively capturing long‐range pixel interactions and local context information. In this study, an edge protection module is combined to augment the edges of ultrasound images. The module incorporates extracted image edge features into the TranSpeckle architecture, which ameliorates the issue of edge information loss engendered by the image despeckling process. Extensive experimental results clearly show that our proposed network outperforms state‐of‐the‐art methods in terms of quantitative metrics and visual quality.
Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Crude oil price fluctuations significantly affect commodity market price fluctuations, so a sudden drop in oil prices will cause a slowdown in the economy and other commodities. This is very important for Indonesia, one of the world's oil-producing countries, to gain multiple benefits from oil exports when world oil prices increase and increase economic growth. Therefore, a system is needed to predict world crude oil prices. In this case, the Particle Swarm Optimization (PSO) algorithm is applied as the optimization of the weight parameters in the Artificial Neural Network-Backpropagation (ANN-BP) method. We compared the ANN-BP–PSO and ANN-BP methods to obtain the method with the best causation value based on the MAPE and MSE results. PSO aims to find the best weight value by iterating the process of finding and increasing position, speed, Pbest, and Gbest until the iteration is complete. The results showed that the ANN-BP-PSO process was classified as very good and had a lower predictive error rate than the ANN-BP method based on the MAPE and MSE values, which is 5.02007% and 7.15827% compared to 6.28323% and 13.86345.
FEM Investigation of the Air Resonance in a Cretan Lyra

Cretan lyra is a stringed instrument very popular on the island of Crete, Greece and an important part of its musical tradition. For stringed musical instruments, the air mode resonance plays a vital part in their sound, especially in the low frequency range. For this study, the air mode resonance of a Cretan lyra is investigated with the use of finite element method (FEM). Two different FEM acoustic models were utilized: first, a pressure acoustics model with the Cretan lyra body treated as rigid was used to provide an approximate result; secondly, an acoustic-structure interaction model was applied for a more accurate representation. In addition, acoustic measurements were performed to identify the air mode resonance frequency. Results of this study reveal that the acoustic-structure interaction model has a 3.7% difference regarding the actual measurements of the resonance frequency. In contrast, the pressure acoustics solution is approximately 13.8% too high compared with the actual measurements. Taken together, findings of this study support the idea that utilizing FEM acoustic-structure interaction models could possibly predict more accurately the vibroacoustic behavior of musical instruments, which in turn can enable the determination of key aspects that can be used to control the instrument’s tone and sound quality.
Research on the Improvement of Information Teaching Ability of College Teachers in era of Intelligent Education

As an endogenous variable of the systematic reform of higher education, education information promotes the innovation of higher education concept, the reform of teaching mode and the system reconstruction. Traditional teaching and learning has been transformed to online learning and real-time intera
Streamlining the archiving of science blog posts

The Rogue Scholar science blog archive is adding important functionality to existing science blogs. The first step after a blog has signed up with Rogue Scholar is archiving the content. This is not only needed for long-term preservation but also enables full-text search and DOI registration with meaningful metadata.
Sublinear Time Lempel-Ziv (LZ77) Factorization

The Lempel-Ziv (LZ77) factorization of a string is a widely-used algorithmic tool that plays a central role in data compression and indexing. For a length-n string over integer alphabet $$[0, \sigma )$$ with $${\sigma = n^{\mathcal O(1)}}$$ , and on a word RAM of width $$w = \varTheta (\log n)$$ , it can be computed in $$\mathcal O(n)$$ time. However, the packed representation of the string occupies only $$\varTheta (n \log \sigma )$$ bits or equivalently $$\varTheta (n / \log _\sigma n)$$ words of space, and hence we can hope for algorithms that run in $$\mathcal O(n / \log _\sigma n)$$ time and words of space. Kempa showed how to compute the LZ77 factorization with overlaps in $$\mathcal O(n / \log _\sigma n + z \log ^{11} n)$$ time and $$\mathcal O(n / \log _\sigma n + z \log ^{10} n)$$ words of space, where z is the number of phrases in the LZ77 factorization (SODA 2019). We significantly improve this result by achieving $$\mathcal O(n / \log _\sigma n + z \log ^{3+\epsilon } z)$$ time with overlaps, and $$\mathcal O(n / \log _\sigma n + z \log ^{23/5+\epsilon } z)$$ without overlaps (for any constant $$\epsilon \in \mathbb R^+$$ ). In both cases, we require only $$\mathcal O(n / \log _\sigma n)$$ words of space. One ingredient of the solution is a novel approximation algorithm that computes an LZ-like parsing of at most 3z phrases in $$\mathcal O(n / \log _\sigma n)$$ time and words of space. All algorithms are deterministic.
New Advances in Rightmost Lempel-Ziv

The Lempel-Ziv (LZ) 77 factorization of a string is a widely-used algorithmic tool that plays a central role in compression and indexing. For a length-n string over a linearly-sortable alphabet, e.g., $$\varSigma = \{1, \dots , \sigma \}$$ with $${\sigma = n^{\mathcal O(1)}}$$ , it can be computed in $$\mathcal O(n)$$ time. It is unknown whether this time can be achieved for the rightmost LZ parsing, where each referencing phrase points to its rightmost previous occurrence. The currently best solution takes $${\mathcal O(n (1 + {\log \sigma }/{\sqrt{\log n})})}$$ time (Belazzougui & Puglisi SODA2016). We show that this problem is much easier to solve for the LZ-End factorization (Kreft & Navarro DCC2010), where the rightmost factorization can be obtained in $$\mathcal O(n)$$ time for the greedy parsing (with phrases of maximal length), and in $$\mathcal O(n + z \sqrt{\log z})$$ time for any LZ-End parsing of z phrases. We also make advances towards a linear time solution for the general case. We show how to solve multiple non-trivial subsets of the phrases of any LZ-like parsing in $$\mathcal O(n)$$ time. As a prime example, we can find the rightmost occurrence of all phrases of length $$\varOmega (\log ^{6.66} n / \log ^2 \sigma )$$ in $$\mathcal O(n / \log _\sigma n)$$ time and space.
Diabatic quantum annealing for the frustrated ring model

Abstract Quantum annealing (QA) is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with an easy-to-prepare ground state and a problem Hamiltonian whose ground state encodes solutions to an optimization problem. The standard implementation relies on the evolution being adiabatic: keeping the system in the instantaneous ground state with high probability and requiring a time scale inversely related to the minimum energy gap between the instantaneous ground and excited states. However, adiabatic evolution can lead to evolution times that scale exponentially with the system size, even for computationally simple problems. Here, we study whether non-adiabatic evolutions with optimized annealing schedules can bypass this exponential slowdown for one such class of problems called the frustrated ring model. For sufficiently optimized annealing schedules and system sizes of up to 39 qubits, we provide numerical evidence that we can avoid the exponential slowdown. Our work highlights the potential of highly-controllable QA to circumvent bottlenecks associated with the standard implementation of QA.
Co-occurrence spatial–temporal model for adaptive background initialization in high-dynamic complex scenes

Background information is an important aspect of pre-processing for advanced applications in computer vision. The literature has made rapid progress in background initialization. However, background initialization still suffers from high-dynamic complex scenes, such as illumination change, background motion, or camera jitter. Therefore, this study presents a novel Co-occurrence Spatial–Temporal (CoST) model for background initialization in high-dynamic complex scenes. CoST achieves a spatial–temporal model through a co-occurrence pixel-block structure. The proposed approach extracts the spatial–temporal information of pixels to self-adaptively generate the background without the influence of high-dynamic complex scenes. The efficiency of CoST is verified through experimental results compared with state-of-the-art algorithms. The source code of CoST is available online at: https://github.com/HelloMrDeng/CoST.git.
Yauti: A Tool for Morphosyntactic Analysis of Nheengatu within the Universal Dependencies Framework

This paper reports on Yauti, a rule-based morphosyntactic analyzer for the endangered Brazilian indigenous language Nheengatu. Its goal is to generate analyses in the UD framework’s CoNLL-U format. It has been developed on par with the construction of the Nheengatu treebank of the UD collection. In sentences only consisting of known and unambiguous words, the tool generally delivers good results. It obtained a LAS score of 73.2% in a version of the Nheengatu UD treebank with all 1022 sentences automatically provided with XPOS tags and a special annotation to handle non-lexicalized words.
Multimodal Cross-Attention Graph Network for Desire Detection

The study of human desire is significant in gaining a better understanding of human behavior and emotions, as desire is one of the most fundamental instincts of human beings. With the development of multimodal algorithms, there have been many recent research tasks using both text and visual information for sentiment classification. However, human desires underlying emotions are still relatively underdeveloped. In this paper, we propose a novel multi-modal approach that combines both visual and textual information to provide a more comprehensive and accurate representation of human desires. Specifically, we first use text and visual encoders that can contain more information to extract features. To better connect the contextual relationships of the same modality and the associations of objects between different modalities, we construct a cross-modal graph and use the cross-attention method for graph representation learning. We evaluate our model on a public multimodal dataset of human desires. Extensive experimental results and in-depth analysis demonstrate that our model achieves state-of-the-art performance in desire detection tasks (Our code can be found at https://github.com/guruitong/-MCAGN.git ).
Ensemble-based Solar Power Prediction System Using Missing Value Interpolation Algorithm

Environmental problems such as global warming due to excessive use of fossil fuels are becoming serious. In order to solve this problem, the supply of new and renewable energy is being activated, and the new and renewable energy market is also expanding. In particular, the share of solar and wind energy among new and renewable energies is rapidly increasing. However, uncertainty and volatility are inherent in renewable energy due to the characteristics of power generation that depend on natural conditions. This leads to a problem in which errors occur in the prediction of the amount of reserve energy required to secure the amount and cost of renewable energy generation. In this paper, we propose an ensemble-based solar power generation prediction system applying missing value interpolation algorithm. It predicts the amount of solar power generation by using weather forecast data from the Korea Meteorological Administration, and provides visualization and scheduling functions for the amount of power generation and predicted amount through a web page.
PACE: Point Annotation-Based Cell Segmentation for Efficient Microscopic Image Analysis

Cells are essential to life because they provide the functional, genetic, and communication mechanisms essential for the proper functioning of living organisms. Cell segmentation is pivotal for any biological hypothesis validation/analysis i.e., to get valuable insights into cell behavior, function, diagnosis, and treatment. Deep learning-based segmentation methods have high segmentation precision, however, need fully annotated segmentation masks for each cell annotated manually by the experts, which is very laborious and costly. Many approaches have been developed in the past to reduce the effort required to annotate the data manually and even though these approaches produce good results, there is still a noticeable difference in performance when compared to fully supervised methods. To fill that gap, a weakly supervised approach, PACE, is presented, which uses only the point annotations and the bounding box for each cell to perform cell instance segmentation. The proposed approach not only achieves 99.8% of the fully supervised performance, but it also surpasses the previous state-of-the-art by a margin of more than 4%.
Federated quanvolutional neural network: a new paradigm for collaborative quantum learning

Abstract In recent years, the concept of federated machine learning has been actively driven by scientists to ease the privacy concerns of data owners. Currently, the combination of machine learning and quantum computing technologies is a hot industry topic and is positioned to be a major disruptor. It has become an effective new tool for reshaping several industries ranging from healthcare to finance. Data sharing poses a significant hurdle for large-scale machine learning in numerous industries. It is a natural goal to study the advanced quantum computing ecosystem, which will be comprised of heterogeneous federated resources. In this work, the problem of data governance and privacy is handled by developing a quantum federated learning approach, that can be efficiently executed on quantum hardware in the noisy intermediate-scale quantum era. We present the federated hybrid quantum–classical algorithm called a quanvolutional neural network with distributed training on different sites without exchanging data. The hybrid algorithm requires small quantum circuits to produce meaningful features for image classification tasks, which makes it ideal for near-term quantum computing. The primary goal of this work is to evaluate the potential benefits of hybrid quantum–classical and classical-quantum convolutional neural networks on non-independently and non-identically partitioned (Non-IID) and real-world data partitioned datasets among several healthcare institutions/clients. We investigated the performance of a collaborative quanvolutional neural network on two medical machine learning datasets, COVID-19 and MedNIST. Extensive experiments are carried out to validate the robustness and feasibility of the proposed quantum federated learning framework. Our findings demonstrate a decrease of 2%–39% times in necessary communication rounds compared to the federated stochastic gradient descent approach. The hybrid federated framework maintained a high classification testing accuracy and generalizability, even in scenarios where the medical data is unevenly distributed among clients.
SFP: Providing System Call Flow Protection against Software and Fault Attacks

With the improvements in computing technologies, edge devices in the Internet-of-Things or the automotive area have become more complex. The enabler technology for these complex systems are powerful application core processors with operating system support, such as Linux, replacing simpler bare-metal systems. While the isolation of applications through the operating system increases the security, the interface to the kernel poses a new threat. Different attack vectors, including fault attacks and memory vulnerabilities, exploit the kernel interface to escalate privileges and take over the system.
The DistilBERT Model: A Promising Approach to Improve Machine Reading Comprehension Models

Machine Reading Comprehension (MRC) is a challenging task in the field of Natural Language Processing (NLP), where a machine is required to read a given text passage and answer a set of questions based on it. This paper provides an overview of recent advances in MRC and highlights some of the key challenges and future directions of this research area. It also evaluates the performance of several baseline models on the dataset, evaluates the challenges that the dataset poses for existing MRC models, and introduces the DistilBERT model to improve the accuracy of the answer extraction process. The supervised paradigm for training machine reading and comprehension models represents a practical path forward for creating comprehensive natural language understanding systems. To enhance the DistilBERT basic model's functionality, we have experimented with a variety of question heads that differ in the number of layers, activation function, and general structure. DistilBERT is a model for question-resolution tasks that is successful and delivers state-of-the-art performance while requiring less computational resources than large models like BERT, according to the presented technique. We could enhance the model's functionality and obtain a better understanding of how the model functions by investigating other question head architectures. These findings could serve as a foundation for future study on how to make question-and-answer systems and other tasks connected to the processing of natural languages.