Reviewerly/exHarmony huggingface dataset:
Mining high-utility sequences with positive and negative values

Sequence pattern discovery is a fundamental topic in the domain of data mining. It has been widely used to solve various problems (e.g., behavior pattern discovery, gene pattern discovery in bioinformatics, user click pattern mining, etc.). High-utility sequence mining as a novel hot issue is more challenging and has generally attracted plenty of attention. Our paper focuses on mining high-utility sequences in a more complicated environment with high efficiency. Most of the previous methods for utility mining aim to find high-utility sequences suitable for items with positive values, but most real-world situations contain items with both positive and negative values. Several algorithms have been applied to the above sophisticated situation and can be used as our comparing algorithms. In this paper, we introduce the FHUSN (Fast mining High Utility Sequences with Negative item) algorithm to mine high-utility sequences in situations with or without negative utility values. FHUSN utilizes the new utility array to store data. Several new pruning strategies that apply to situations with or without negative values have been used to reduce search space. Experiments are carried out on several benchmark datasets, and experimental results illustrate that our method has better performance.
Designing integrated physical–digital systems for children–nature interaction

Studies in the last decades have highlighted how children spend less time outdoors, while they are increasingly attracted by screens and indoor digital activities. Based on the assumption that technology and open-air activities are not necessarily mutually exclusive, this article illustrates the design of integrated physical–digital systems motivating children to connect with nature and learn from it. A key ingredient is providing unobtrusive technology supporting playful, creative, and educational outdoor experiences without distracting children from their experience with nature. Based on the insights gathered through design-based research, the article also outlines design considerations that aim to refine the theory and practice of technology for children–nature interaction.
Space-based quantum networking in the presence of a nuclear disturbed environment

Space-based quantum networks provide a means for near-term long-distance transmission of quantum information. This article analyzed the performance of a downlink quantum network between a low-Earth-orbit satellite and an observatory operating in less-than-ideal atmospheric conditions. The effects from fog, haze, and a nuclear disturbed environment on the long-range distribution of quantum states were investigated. A density matrix that estimates the quantum state by capturing the effects from increased signal loss and elevated background noise to estimate the state fidelity of the transmitted quantum state was developed. It was found that the nuclear disturbed environment and other atmospheric effects have a degrading effect on the quantum state. These environments impede the ability to perform quantum communications for the duration of the effects. In the case of the nuclear disturbed environment, the nuclear effects subside quickly, and network performance should return to normal by the next satellite pass.
Including critical Approaches in HCI Curricula: A provocation

This is not a paper [1]; it is more of a collection and reflection of tangled ideas and discussions on the politics of engaging multiple worlds in design. Discussions brought on by three African HCI researchers on their journey to define an identity for an African HCI curriculum. We invite the readership to engage with our stories and ponder with us these questions: 1) How might we help future designers engage and navigate multiple worldviews, some of which are less dominant? 2) How might we help them navigate uncontested politics and ethics of design encounters? 3) What are the challenges that we educators might face as we introduce critical approaches in the classroom?
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences.
Assessing and validating tourism business model in hospitality industry: role of blockchain platform

The research aims to investigate the potential of blockchain technology to address the challenges facing traditional tourism businesses in the hospitality industry. By assessing and validating tourism business models, the research explores how blockchain can enhance transparency, efficiency, and cost reduction. This research utilizes the ARDL technique to examine the role of blockchain in the tourism in reducing environmental deterioration in China for the period of 2010–2020. The empirical analysis was used in this study. The study presents findings that support the effectiveness of blockchain in validating tourism business models. The authors conclude by discussing the implications of their research for the hospitality industry and suggest future research directions.
Application of 5G Communication Technology Based on Intelligent Sensor Network in Coal Mining

Intelligent mining provides an indispensable, strong technical support for the construction of intelligent mines. Intelligent tunneling technology is an inevitable requirement for safe and efficient production of coal and a fundamental way to solve the imbalance of mining in order to improve the deficiency of data transmission in light of intelligent coal tunneling technology. Based on intelligent sensor and 5G technology, this paper puts forward a kind of intelligent tunneling support transportation integrated technology. According to the geological conditions of the surrounding rock in different coal seams, the anchor road header and 5G continuous intelligent tunneling mode are adopted. At the same time, the data communication network coverage suitable for underground unmanned driving is expounded, and the performance indexes that need to be met for accessing WLAN network are given based on the actual engineering experience. The breakthrough of underground unmanned driving application that 5G new communication network will bring is analyzed. And the construction mode of 5G platform of underground coal mine sensor network is given.
Machine-readable data and metadata of international key comparisons in radionuclide metrology

Abstract The digitalization of key comparisons in radionuclide metrology is being studied at the BIPM. Results of comparisons are made available on the BIPM KCDB platform where a PDF/A document can be downloaded. Access to the metadata contained in the PDF is not straightforward. Therefore, the XML and JSON versions of the report are now embedded in the PDF/A document, allowing direct access to the metadata. This achievement is a first step towards the implementation of FAIR (findable, accessible, interoperable, reusable) principles and does not affect the current policy of publishing comparison results. The combination of PDF/A and XML technologies seems to be an effective way to carry out digitalization. The strengths of both technologies can be brought together to meet the FAIR principles and ISO/IEC 17025 compliance. This approach benefits from robust data exchange features such as XML Schema and XML namespaces, as well as secure digital signatures and long-term archiving of PDF/A-3 documents.
HLA-HOD: Joint High-Low Adaptation for Object Detection in Hazy Weather Conditions

Object detection remains challenging in hazy weather conditions due to the poor visibility of captured images. There are currently two types of detectors capable of adapting to varying weather conditions: (i) low-level adaptation methods that combine one detector with an additional dehazing network and (ii) high-level adaptation methods that explore various kinds of domain adaptation knowledge. However, neither of these approaches can achieve desirable performance due to their inherent limitations. We raise an intriguing question—if combining both low-level adaptation and high-level adaptation, can improve the generalization ability of a detector in hazy weather conditions? To answer it, we propose a Joint High-Low Adaptation Object Detection paradigm (HLA-HOD) in hazy weather conditions. By combining both low-level adaptation and high-level adaptation, HLA-HOD achieves superior performance on hazy images without requiring ground-truth bounding boxes or clean images. Extensive experiments demonstrate that our method outperforms state-of-the-art low-level and high-level adaptation methods by a large margin both quantitatively and qualitatively.
Most complicated lock pattern-based seismological signal framework for automated earthquake detection

Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selector-based feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.
Translational argument technology: Engineering a step change in the argument web

Following the establishment in 2006 of a representational standard for the computational handling of structures of argumentation, the Argument Interchange Format, it became possible to develop a vision for the coherent integration of multifarious services, components and tools that create, consume, navigate, analyse, evaluate and manipulate arguments and debates. This vision was the Argument Web with theoretical foundations laid by Rahwan et al. (2007), and practical engineering work described by Bex et al. (2013). Over the intervening period, the key challenge has been to demonstrate the practical and societal value of the Argument Web by taking its tools and applications to larger audiences. This paper lays out three approaches by which the Argument Web has been scaled up in this way, each in partnership with the BBC, and each with different kinds of evaluation and impact. Transitioning these technologies to large user groups paves the way for broader-scale uptake of the Argument Web and heralds the translation from lab to real-world application for a substantial research community working in argument technology.
SLDF: A semantic line detection framework for robot guidance

Semantic line is a high-level feature in image segmentation and is of great significance in image understanding. This paper proposes a semantic line detection framework shorten as SLDF in addressing robot guidance or navigation tasks. First of all, this paper cast semantic line detection task as a special object detection task with oriented bounding boxes, and then discusses the diversity between several kinds of label coding categories. There are two alternative backbones provided by SLDF: A two-stage model in pursuit of higher precision, or a single stage model that reaches the best trade-off between speed and accuracy. Besides, a geometry-based loss function is designed for SLDF to exploit the benefits of localization accuracy and brings significant improvement. Finally, considering the lack of a comprehensive line evaluation standard including angle, offset, length and other factors, this paper respectively designs corresponding evaluation function based on the characteristics of frame-independent, frame-dependent and streaming perception model, and then evaluate the performance of our framework on SEL-Dataset, NKL-Dataset. Finally, we migrate the designed model to the agricultural robot and realize the robot guidance task in the farmland.
A multi-channel multi-tower GNN model for job transfer prediction based on academic social network

The study of scholar job transfer is beneficial to both individual career success and talent recruitment of institutions. For such a complicated problem, the diversity and objectivity of the obtained information are essentially significant. However, previous works focus on the general job-hopping problems and utilize the information from the recruitment or social platform, which is somehow limited by data privacy. In this paper, we define the scholar job transfer prediction task by introducing more diversified information (e.g., career sequence, collaboration graph), which is obtained from their publications, for more comprehensive modeling without privacy data. Moreover, we design a Multi-channel Multi-tower Enhanced Framework (MMEF) to integrate the heterogeneous inputs in a complementary manner, which can capture the temporal pattern from career trajectories, leverage the academic collaboration information considering the influence from co-authors, and deal with extra descriptions and estimate the relevance scores between the scholars and institutions. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed framework, which outperforms the state-of-the-art approaches by about 20% overall for making better use of the potential patterns in heterogeneous data. More intensive studies explore how and the degree to different collaborators impact scholar job transfer.
Memory Errors and Memory Safety: C as a Case Study

We provide an accessible, self-contained explanation of memory errors as they pertain to programming languages, and the related idea of memory safety. We use the C language as a concrete example.
Several families of binary cyclic codes with good parameters

Binary odd-like duadic codes have parameters [n,(n+1)/2], where n is odd. It is well known that the minimum odd weight of odd-like duadic codes has the lower bound n. The binary quadratic-residue codes and the punctured binary Reed-Muller codes of certain order are two families of binary cyclic codes with parameters [n,(n+1)/2,d≥n]. It is very hard to construct an infinite family of binary cyclic codes with length n and dimension near (n+1)/2 whose minimum distances have a square-root bound. Recently, Tang and Ding constructed an infinite family of binary cyclic codes with parameters [2m−1,2m−1,d] whose minimum distances have lower bounds close to the square-root bound. The objective of this paper is to construct several infinite families of binary cyclic codes of length 2m−1 and dimension near 2m−1 whose minimum distance d much exceeds the square-root bound. For m≥9 being odd, an infinite family of binary cyclic codes with parameters [2m,2m−1,d≥3⋅2m−12−1] is presented. For m≥8 being even, an infinite family of binary cyclic codes with parameters [2m,2m−1−1+m2,d≥3⋅2m−22+1] is presented.
An improved <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si78.svg" display="inline" id="d1e789"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:math> path-planning algorithm for nuclear spill evacuation and radioactive source retrieval in complex terrain

Path-planning technology is an essential radiation protection measure to reduce radiation exposure to workers in nuclear emergencies. The terrain in the actual radiation environment is often diverse and also impact personnel movement speed differently. It is a multi-objective optimization problem for distance, dose, and terrain. However, there are only path-planning methods considering some of these factors. We creatively quantify the difference in terrain to movement speed and propose a path-planning method for Complex Terrain based on the A∗ algorithm (CTA) in the radiational environments. The CTA algorithm divides the influence of terrain on movement into accessible and inaccessible, and the reachable part quantifies the influence of terrain as velocity, which can give the minimum dose path in complex nuclear environments. Three simulation experiments confirm the method's superiority. The simulation experiments show that the CTA algorithm works better in complex terrain than the traditional algorithm.
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Let $q$ be an odd prime and $B = \{b_{j}\}_{j=1}^{l}$ be a finite set of nonzero integers that does not contain a perfect $q^{th}$ power. We show that $B$ has a $q^{th}$ power modulo every prime $p \neq q$ and not dividing $\prod_{b\in B} b$ if and only if $B$ corrresponds to a linear hyperplane covering of $\mathbb{F}_{q}^{k}$. Here, $k$ is the number of distinct prime factors of the $q$-free part of elements of $B$. Consequently: $(i)$ a set $B \subset\mathbb{Z}\setminus\{0\}$ with cardinality less than $q+1$ cannot have a $q^{th}$ power modulo almost every prime unless it contains a perfect $q^{th}$ power and $(ii)$ For every set $B = \{b_{j}\}_{j=1}^{l} \subset\mathbb{Z}\setminus\{0\}$ and for every $\big(c_{j}\big)_{j=1}^{l} \in\Big(\mathbb{F}_{q}\setminus\{0\}\Big)^{l}$ the set $B$ contains a $q^{th}$ power modulo every prime $p \neq q$ and not dividing $\prod_{j=1}^{l}$ if and only if the set $\{b_{j}^{c_{j}}\}_{j=1}^{l}$ does so.
Generalized Lucas polynomials over finite fields

In this paper, we give necessary and sufficient conditions on n for the unsigned and signed generalized Lucas polynomials fn(x) and gn(x) being permutations over the prime field. Moreover, the permutation behaviours of these polynomials over non-prime fields are also discussed.
Building “Design Empathy” for People with Disabilities: an Unsolved Challenge in HCI Education

Human-Computer Interaction (HCI) education research strives to integrate inclusion and diversity in classroom teaching. We focus on a key demographic that has been historically underrepresented in design: people with multiple disabilities and/or severe motor disabilities. Since engaging directly with these populations is rarely feasible, we need other ways of teaching students how to consider the challenges, emotions, and lived experiences of these target users. While current approaches in HCI education do engage students in thinking about accessibility through curated disabled experiences (e.g., simulation or personas), these methods do not fully reflect the holistic experience of people with disabilities. We propose a new way of teaching design empathy: immersing students in the real-world experiences of people with disabilities through a curated set of their public social media posts.
Challenges in Teaching More-Than-Human Perspectives in Human-Computer Interaction Education

In this paper, we discuss challenges emerging in connection to teaching for and with more-than-human values and stakeholder perspectives in human-computer interaction (HCI) curriculum. Recently, we have experienced a rise in interest in more-than-human perspectives in various HCI venues. However, there is still a lack of published work on how to teach such perspectives, as well as practical educational resources for supporting the more-than-human HCI in education.
Multiplicative Noise Removal and Contrast Enhancement for SAR Images Based on a Total Fractional-Order Variation Model

In this paper, we propose a total fractional-order variation model for multiplicative noise removal and contrast enhancement of real SAR images. Inspired by the high dynamic intensity range of SAR images, the full content of the SAR images is preserved by normalizing the original data in this model. Then, we propose a degradation model based on the nonlinear transformation to adjust the intensity of image pixel values. With MAP estimator, a corresponding fidelity term is introduced into the model, which is beneficial for contrast enhancement and bias correction in the denoising process. For the regularization term, a gray level indicator is used as a weighted matrix to make the model adaptive. We first apply the scalar auxiliary variable algorithm to solve the proposed model and prove the convergence of the algorithm. By virtue of the discrete Fourier transform (DFT), the model is solved by an iterative scheme in the frequency domain. Experimental results show that the proposed model can enhance the contrast of natural and SAR images while removing multiplicative noise.
Real-Time Detection of Nickel Plated Punched Steel Strip Parameters Based on Improved Circle Fitting Algorithm

Nickel-plated punched steel strip is a product obtained by punching holes on the surface of cold-rolled white sheet steel strip and then electrochemical nickel plating. It is necessary to make accurate and fast detection of punching circle parameters, since it is of crucial importance to ensuring the quality of nickel-plated punched steel strips. Accordingly, in this article, an improved circle fitting algorithm of nickel-plated punched steel strip is proposed. Firstly, the least squares fitting is performed to obtain the circle center and radius dataset by iterative algorithm with different values for the initial point positions and intervals. Then, the mean shift algorithm is used to optimize the results after iteration, and the segmented fitted circle centers are all concentrated around the true circle center to obtain the best radius and center coordinates. Finally, comparison experiments with different numbers of circular holes and verification experiments with nickel-plated punched steel strips are carried out. As the results show, the algorithm proposed in this article is more robust than the least squares algorithm in detecting multiple circles and has better real-time performance than the Hough transform. Therefore, it can meet the industrial production needs with high accuracy and real-time requirements, such as nickel-plated punched steel strips.