Reviewerly/exHarmony huggingface dataset:
RETHINKING THE INTERPLAY BETWEEN TRANSFORMATIONAL LEADERSHIP, ORGANIZATIONAL CULTURE, AND EMPLOYEE CREATIVITY

In the present period of rapid global changes, insurance firms are competing fiercely to gain a competitive edge by promoting effective leadership, high employee performance, and innovative behavior. This research targets the Jordanian insurance industry to investigate the mediating effect of organizational culture on the relationship between transformational leadership and employee creativity. The research sheds light on the contribution of these variables to the performance of the insurance industry. A total of 275 participants from 22 distinct insurance companies were included in the research. An online questionnaire was administered via department heads using a Google link, and the data collected was analyzed using Smart-PLS4. The research revealed that a favorable organizational culture has a significant mediating impact on enhancing employee creativity, which is initially influenced by transformational leadership. Keywords : Transformational Leadership, Employee Creativity, Organizational Culture, Insurance Companies DOI： https://doi.org/10.35741/issn.0258-2724.58.2.14
On the Group of Universal Gates for a Two-Qubit Quantum System

The goal of finding a commutative group of gates from the set of unitary matrices is to simplify the process of quantum computing. If a set of gates is commutative, then we can apply the gates in any order without affecting the final outcome. This is important because quantum systems are very sensitive to errors and decoherence, and any disruption to the system can cause errors in the computation.By having a commutative group of gates, we can simplify the process of designing quantum algorithms and reduce the risk of errors. This is particularly important for solving complex computational problems that cannot be solved by classical computers.
Let's report our rivals: how Chinese fandoms game content moderation to restrain opposing voices

While crowdsourcing approaches in content moderation systems increase the governance capacity of social media, they also offer a loophole for malicious users to massively report and restrict disliked content. To fill the knowledge gap about large-scale, bottom-up attempts at restraining online expressions, we focus on a type of public and institutionalized mass reporting: anti-smear (反黑) campaigns within Chinese online fandom communities, where fans coordinate together and collectively report content they perceive as inappropriate. Based on detailed data of more than two hundred anti-smear groups collected from Weibo and interviews with active participants, our paper examines the motives and dynamics of anti-smear campaigns, the coordination strategies used to game the content moderation system, and the diffusion of anti-smear culture among fandom networks. We argue that anti-smear is essentially a practice of information control and reflects an intolerant mindset of social media users towards dissidents. This paper also points out the vulnerability of community-based content moderation systems to be weaponized in a polarized age, which brings great challenges to platform governance.
Correction: Alkhaleefah et al. Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images. Cancers 2022, 14, 4030

In the original publication [...].
Artificial Intelligence and Digital Technologies in the Future Education

Artificial Intelligence (AI) is the branch of Computer Science that focuses on the theory and practice of creating “smart” devices mimicking human reasoning and behavior. The introduction of the techniques of AI to Education has brought significant benefits to the teaching and learning, to student and teacher assessment and training and to several other educational processes. The present paper focuses on the role that AI and the digital technologies play for Education in the forthcoming era of the Fourth Industrial Revolution, which, under normal conditions, is expected to lead humanity to an era of nearly free energy, goods and services. Thepaper discusses the benefits and limitations of the introduction of techniques of AI in Education. Namely, the role of computers isinvestigated and the expediency of teaching principles of Soft Computingin Education is studied. The advantages and disadvantages of e-learningwith respect to the traditional learning theories and teaching methods are examined, with a special focus on connectivism, a new learning theory in the digital era. The possible role of social robots and the new role of the teacher in future education are also studied and the paper closes with the final conclusions.
A Doppler Location Method Based on Virtual Path Difference

This paper presents a Doppler passive location method for moving targets with fixed single station using the Doppler frequency shift and time difference information. First, based on the relationship between frequency shift and path difference, the virtual path difference is calculated from the measured value of Doppler frequency shift by means of mean value correction. Then, under the assumption that the target is moving at an average speed along a straight line, two coaxial virtual double base arrays are constructed by using the moving track of the moving target based on the method of fixed period time difference. On this basis, the moving distance of the moving target can be calculated by using the ratio relationship between the frequency difference and the radial distance between the two adjacent detection points in the middle of the array, and the linear solution of the two double base path difference positioning equations. At this point, the relative coordinate position of the moving target can be obtained by directly using the linear solution of the double base path difference positioning equation again.
Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions

Video captioning aims to convey dynamic scenes from videos using natural language, facilitating the understanding of spatiotemporal information within our environment. Although there have been recent advances, generating detailed and enriched video descriptions continues to be a substantial challenge. In this work, we introduce Video ChatCaptioner, an innovative approach for creating more comprehensive spatiotemporal video descriptions. Our method employs a ChatGPT model as a controller, specifically designed to select frames for posing video content-driven questions. Subsequently, a robust algorithm is utilized to answer these visual queries. This question-answer framework effectively uncovers intricate video details and shows promise as a method for enhancing video content. Following multiple conversational rounds, ChatGPT can summarize enriched video content based on previous conversations. We qualitatively demonstrate that our Video ChatCaptioner can generate captions containing more visual details about the videos. The code is publicly available at https://github.com/Vision-CAIR/ChatCaptioner
Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?

The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems. In practice, however, we have encountered several key issues when using pretrained embedding in a real-world production system, many of which cannot be fully explained by current knowledge. Unfortunately, we find that there is a lack of a thorough understanding of how pre-trained embeddings work, especially their intrinsic properties and interactions with downstream tasks. Consequently, it becomes challenging to make interactive and scalable decisions regarding the use of pre-trained embeddings in practice. Our investigation leads to two significant discoveries about using pretrained embeddings in e-commerce applications. Firstly, we find that the design of the pretraining and downstream models, particularly how they encode and decode information via embedding vectors, can have a profound impact. Secondly, we establish a principled perspective of pre-trained embeddings via the lens of kernel analysis, which can be used to evaluate their predictability, interactively and scalably. These findings help to address the practical challenges we faced and offer valuable guidance for successful adoption of pretrained embeddings in real-world production. Our conclusions are backed by solid theoretical reasoning, benchmark experiments, as well as online testings.
LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search

Queries with similar information needs tend to have similar document clicks, especially in biomedical literature search engines where queries are generally short and top documents account for most of the total clicks. Motivated by this, we present a novel architecture for biomedical literature search, namely Log-Augmented DEnse Retrieval (LADER), which is a simple plug-in module that augments a dense retriever with the click logs retrieved from similar training queries. Specifically, LADER finds both similar documents and queries to the given query by a dense retriever. Then, LADER scores relevant (clicked) documents of similar queries weighted by their similarity to the input query. The final document scores by LADER are the average of (1) the document similarity scores from the dense retriever and (2) the aggregated document scores from the click logs of similar queries. Despite its simplicity, LADER achieves new state-of-the-art (SOTA) performance on TripClick, a recently released benchmark for biomedical literature retrieval. On the frequent ("HEAD") queries, LADER largely outperforms the best retrieval model by 39% relative NDCG@10 (0.338 v.s. 0.243). LADER also achieves better performance on the less frequent ("TORSO") queries with 11% relative NDCG@10 improvement over the previous SOTA (0.303 v.s. 0.272). On the rare ("TAIL") queries where similar queries are scarce, LADER still compares favorably to the previous SOTA method (NDCG@10: 0.310 v.s. 0.295). On all queries, LADER can improve the performance of a dense retriever by 24%-37% relative NDCG@10 while not requiring additional training, and further performance improvement is expected from more logs. Our regression analysis has shown that queries that are more frequent, have higher entropy of query similarity and lower entropy of document similarity, tend to benefit more from log augmentation.
Inferring the Age and Sex of Ancient Potters from Fingerprint Ridge Densities: A Data-Driven, Bayesian Mixture Modelling Approach

The density of epidermal ridges in a fingerprint varies predictably by age and sex. Archaeologists are therefore interested in using recovered fingerprints to learn about the ancient people who produced them. Recent studies focus on estimating the age and sex of individuals by measuring their fingerprints with one of two similar metrics: mean ridge breadth (MRB) or ridge density (RD). Yet these attempts face several critical problems: expected values for adult females and adolescent males are inherently indistinguishable, and inter-assemblage variation caused by biological and technological differences cannot be easily estimated. Each of these factors greatly decreases the accuracy of predictions based on individual prints, and together they condemn this strategy to relative uselessness. However, information in fingerprints from across an assemblage can be pooled to generate a more accurate depiction of potter demographics. We present a new approach to epidermal ridge density analysis using Bayesian mixture models with the following key benefits: Age and sex are estimated more accurately than existing methods by incorporating a data-driven understanding of how demographics and ridge density covary. Uncertainty in demographic estimates is automatically quantified and included in output. The Bayesian framework can be easily adapted to fit the unique needs of different researchers.
Certificateless Anonymous Signcryption Scheme With Provable Security in the Standard Model Suitable for Healthcare Wireless Sensor Networks

In healthcare wireless sensor networks (HWSNs), wireless sensors are placed on the patient, which collect the patient’s vital signs parameters and various environmental information, and send these data to the doctor, so that the doctor is able to perform real-time remote monitoring for the patient. The patient’s vital signs parameters contain a lot of private information. It is very likely to cause the leakage of patient’s private information if these parameters are transmitted through the public channel. In addition, many patients do not want doctors to know their true identity. We designed a certificateless anonymous signcryption (CL-ASC) scheme for HWSNs in this article. It encrypts the patient’s vital signs parameters, authenticates the legitimacy of the sensor, and realizes the anonymity of the sensor. We then showed the security proofs of new scheme in the standard model. Finally, we compared the performance of the six schemes. Since only three pairing operations are used, the new scheme enjoys higher computation efficiency and is suitable for HWSNs.
Analisis Persepsi Siswa terhadap Penggunaan Aplikasi Quizizz pada Materi Hidrokarbon

Quizizz online learning application is one of the most widely used learning evaluation applications, especially during the covid-19 pandemic. This study aims to analyze students' perceptions of the use of Quizizz in chemistry subjects with Hydrocarbons Material. This study uses a quantitative method with a descriptive approach at Madrasah Aliyah Nurul Wathan Pasar Kembang. 36 students were selected by purposive sampling, the data were collected using a questionnaire technique, while the data were analyzed using descriptive analysis techniques. The results of the data analysis showed that students (89%) had a very good perception of the use of Quizizz in chemistry subjects with Hydrocarbon Materials.
Learning Analytics: An Opportunity for Education

How analytics is transforming the future for millions of learners.
LOCC: a novel visualization and scoring of cutoffs for continuous variables

There is a need for new methods to select and analyze cutoffs employed to define genes that are most prognostic significant and impactful. We designed LOCC (Luo's Optimization Categorization Curve), a novel tool to visualize and score continuous variables for a dichotomous outcome.To demonstrate LOCC with real world data, we analyzed TCGA hepatocellular carcinoma gene expression and patient data using LOCC. We compared LOCC visualization to receiver operating characteristic (ROC) curve for prognostic modeling to showcase its utility in understanding predictors in various TCGA datasets.Analysis of E2F1 expression in hepatocellular carcinoma using LOCC demonstrated appropriate cutoff selection and validation. In addition, we compared LOCC visualization and scoring to ROC curves and c-statistics, demonstrating that LOCC better described predictors. Analysis of a previously published gene signature showed large differences in LOCC scoring, and removing the lowest scoring genes did not affect prognostic modeling of the gene signature demonstrating LOCC scoring could distinguish which predictors were most critical.Overall, LOCC is a novel visualization tool for understanding and selecting cutoffs, particularly for gene expression analysis in cancer. The LOCC score can be used to rank genes for prognostic potential and is more suitable than ROC curves for prognostic modeling.
An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder

Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time. The storage and transmission of the obtained data is a crucial link, and a good compression method can improve the efficiency of this process. In this paper, a lightning convolutional stack autoencoder (LCSAE) model for compressing LEMP data was designed, which converts the data into low-dimensional feature vectors through the encoder part and reconstructs the waveform through the decoder part. Finally, we investigated the compression performance of the LCSAE model for LEMP waveform data under different compression ratios. The results show that the compression performance is positively correlated with the minimum feature of the neural network extraction model. When the compressed minimum feature is 64, the average coefficient of determination R2 of the reconstructed waveform and the original waveform can reach 96.7%. It can effectively solve the problem regarding the compression of LEMP signals collected by the lightning sensor and improve the efficiency of remote data transmission.
Deterministic all-optical quantum state sharing

Quantum state sharing, an important protocol in quantum information, can enable secure state distribution and reconstruction when part of the information is lost. In (k, n) threshold quantum state sharing, the secret state is encoded into n shares and then distributed to n players. The secret state can be reconstructed by any k players (k > n / 2), while the rest of the players get nothing. In the continuous variable regime, the implementation of quantum state sharing needs the feedforward technique, which involves optic-electro and electro-optic conversions. These conversions limit the bandwidth of the quantum state sharing. Here, to avoid the optic-electro and electro-optic conversions, we experimentally demonstrate (2, 3) threshold deterministic all-optical quantum state sharing. A low-noise phase-insensitive amplifier based on the four-wave mixing process is utilized to replace the feedforward technique. We experimentally demonstrate that any two of three players can cooperate to implement the reconstruction of the secret state, while the rest of the players cannot get any information. Our results provide an all-optical platform to implement arbitrary (k, n) threshold deterministic all-optical quantum state sharing and pave the way to construct the all-optical broadband quantum network.
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes. Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks.
Agglomeration of Polygonal Grids Using Graph Neural Networks with Applications to Multigrid Solvers

Agglomeration-based strategies are important both within adaptive refinement algorithms and to construct scalable multilevel algebraic solvers. In order to automatically perform agglomeration of polygonal grids, we propose the use of Machine Learning (ML) strategies, that can naturally exploit geometrical information about the mesh in order to preserve the grid quality, enhancing performance of numerical methods and reducing the overall computational cost. In particular, we employ the k-means clustering algorithm and Graph Neural Networks (GNNs) to partition the connectivity graph of a computational mesh. Moreover, GNNs have high online inference speed and the advantage to process naturally and simultaneously both the graph structure of mesh and the geometrical information, such as the areas of the elements or their barycentric coordinates. These techniques are compared with METIS, a standard algorithm for graph partitioning, which is meant to process only the graph information of the mesh. We demonstrate that performance in terms of quality metrics is enhanced for ML strategies. Such models also show a good degree of generalization when applied to more complex geometries, such as brain MRI scans, and the capability of preserving the quality of the grid. The effectiveness of these strategies is demonstrated also when applied to MultiGrid (MG) solvers in a Polygonal Discontinuous Galerkin (PolyDG) framework. In the considered experiments, GNNs show overall the best performance in terms of inference speed, accuracy and flexibility of the approach.
DISPARITAS MEDIA PEMBELAJARAN PADA ERA DIGITALISASI PENDIDIKAN DI WILAYAH PERBATASAN RI-RDTL (REFLEKSI PEMBELAJARAN ONLINE DAERAH PERBATASAN)

The purpose of the study was to determine the disparity in the use of learning media and the barriers to the digitalization of learning media programs in the border areas of Indonesia and RDTL. The research approach is qualitative with descriptive method. Research informants are principals, teachers and students. Data collection techniques using observation, interviews, document studies, questionnaires. The data analysis technique consists of 3 flow of activities carried out simultaneously, namely data reduction, data presentation and conclusion drawing/verification. The result of the research is that from a total of 17 border area schools with a total of 143 teachers, 122 teachers (85%) did not use digital-based learning media, while 21 teachers (15%) used digital-based learning media. The inhibiting factors for the use of digital-based learning media in border areas are, First, the development of technology-based infrastructure that has not been evenly distributed in the RI-RDTL border area. Second, the absence of free internet access for border area communities has an impact on the availability of internet quotas for teachers and students in carrying out online learning activities. Third, the low knowledge of teachers in managing online learning activities such as the use of digital-based learning media.
Sand-dust image enhancement using RGB color balance method

Abstract： This paper proposes a dusty image enhancement algorithm based on the RGB color balance method to address the problem of color difference in low-contrast and low-definition outdoor images in dusty environments.The method mainly includes two tasks： color correction and contrast enhancement.First， in view of the particularity of the color distribution of dust images and the illumination mechanism as• sumed by the gray world algorithm， an RGB color balance method (RGBCbm) that maintains the mean value of the color component is proposed such that the RGB three-channel component is stretched accord• ing to the mean value of the color component， which effectively removes the color curtain problem caused by dust in images.The multi-scale retinal enhancement algorithm with color restoration (MSRCR) is fur• ther used to improve the color correction results.Subsequently， the relative global histogram stretching (RGHS) method combined with the Lab color model is used to enhance and correct the contrast， color， and brightness of the image.Test results and verification of the proposed algorithm on experimental data show that the algorithm can effectively solve the color difference problem in various dust-degraded images