Reviewerly/exHarmony huggingface dataset:
A knowledge distillation-based deep interaction compressed network for CTR prediction

Click-through rate (CTR) prediction is an essential task in online advertising recommender systems. As a hot research frontier driven by academic and industrial needs, CTR prediction has observed remarkable progress in recent years. How to effectively learn sophisticated feature interactions hidden behind user behaviors is critical in maximizing CTR. Targeted at the above issue, this paper first constructs a deep interaction compressed network model (abbreviated as DICN) that aims to generate both low- and high-order feature interactions with different weights in both explicit and implicit fashions. Then, a KD-DICN model is created using a knowledge distillation framework to reduce the complexity of DICN. Finally, comprehensive experiments are conducted on two public datasets to demonstrate the effectiveness and efficiency of our models, which outperform the existing state-of-the-art methods. Therefore, the research presented in this paper can not only enrich and develop the theories related to CTR prediction, but it can also assist some advertising industries in better defining their service audience and recommending more popular contents to consumers more accurately.
Autonomous Flight Control for Drones using Instrumented Gloves Technology

An instrumented glove is used to operate drones in military, commercial, and medical sectors in this research. Traditional drone control techniques employ remote control devices, which might be difficult to operate. The instrumented glove lets operators control drones with natural hand motions. Sensors in the glove translate the operator's hand gestures into drone motions. The instrumented glove is versatile. Military reconnaissance and surveillance operations may benefit from the glove. Commercial aerial photography, surveying, and inspection may benefit from it. It can operate medical drones to transfer supplies and equipment to distant regions. Overall, the instrumented glove is an intriguing drone technology discovery that might change how drones are used in numerous sectors. Drone operations may be more efficient and accurate with the glove's simplified control technique. The study examines this technology's potential across several sectors, noting its pros and cons. Thus, it lays the groundwork for drone control research using instrumented gloves.
Combinatorial online high‐order interactive feature selection based on dynamic graph convolution network

Traditional feature selection algorithms assume that the sample and feature space is known before learning, while most of the data is feature streams or data streams in reality. Currently, streaming feature selection algorithms can retain relevant features by removing redundant and irrelevant features based on the interaction between features, but they ignore the specific number of features that have interaction. Most of the existing studies only consider the case of interaction between two features, which is not quite in line with most realistic scenarios, i.e., the number of features with interaction is unknown. This paper concentrates on the high-order interactions between stream feature, and proposes a Combinatorial High-Order Interactive Feature Selection based on Dynamic GCN and Sparse learning (CHOIFS-DGS). Based on previous definitions of feature interaction, this paper proposes some new metrics to measure the degree of interaction between a newly arrived feature and an already selected feature. CHOIFS-DGS consists of three main parts, namely: low-order online feature selection based on interaction measure, high-order online feature selection based on dynamic GCN, and Intra-group sparse feature selection. In the experimental analysis section, this study employs two different classifiers and eleven publicly released data sets, including gene data related to diseases and data from two classification challenges (NeurIPS 2003 feature selection challenge and WCCI 2006 Performance Prediction Challenge). The experimental results demonstrate that the proposed CHOIFS-DGS model significantly improves classification accuracy on all eleven data sets, while using a relatively smaller number of features, thus fulfilling the role of key feature selection. Furthermore, the CHOIFS-DGS algorithm consists of three components: LO-OIFS, HO-OIFS, and Group-Sparse. By applying these three sub-modules separately for extracting data features and comparing the results with CHOIFS-DGS, it is found that the performance of CHOIFS-DGS is lower than that of the individual sub-modules only in three data sets, while significantly better in the remaining eight data sets. This indicates that the integrated use of the three sub-modules can enhance model accuracy. Finally, in the ablation experiment, to verify the necessity of considering higher-order interactions among features, the results of the HO-OIFS module were compared with those of the other modules. The results show that the model’s accuracy significantly improves after incorporating the HO-OIFS module, thereby demonstrating that considering higher-order interactions between features is essential.
Pengaruh Model Pembelajaran Contextual Teaching And Learning (CTL) terhadap Hasil Belajar Siswa pada Subtema III Pentingnya Menjaga Asupan Makanan Sehat Kelas V SD Negeri 124388 Siantar Martoba

This research is motivated by the low learning outcomes of fifth grade students at SD Negeri 124388 Siantar Martoba. The contextual teaching learning (ctl) learning model on student learning outcomes in subtheme 3 The Importance of Maintaining Healthy Food Intake. The purpose of this study was to determine whether or not there was an influence of the contextual teaching learning (ctl) learning model on student learning outcomes in subtheme 3 of the importance of maintaining healthy food intake. The approach in this research is a quantitative approach. The results of testing the hypothesis using SPSS 22 assistance analysis. It is known that the results of tcount are 11,256. The ttable value in the statistical table with a significance of 0.05 with df n-2 is 325. Because tcount (11.256) &gt; (325) the contextual teaching learning (CTL) model influences student learning outcomes. In conclusion, on student learning outcomes in subtheme 3 The Importance of Maintaining Healthy Food Intake.
Increasing the Students’ Ability in Mastering Vocabulary Through Guessing Game at XI-MIPA 1 of SMA Negeri 3 Gunungsitoli in 2021/2022

Vocabulary is a core component of languages and the basic for how the learners speak, listen, read and write. Vocabulary mastery is the ability to use the words in conducting the communication and understandable the meaning of the words or phrases in English. 36 students of class XI-MIPA1 of SMA Negeri 3 Gunungsitoli faced problem in learning vocabulary particularly to obtain and absorb the meaning of new words. To solve the problem, the researcher implemented Guessing Game which the main procedure was asked the students to guess the new word from the explanation of the word itself. To achieve the purpose, the researcher conducted CAR Method and it was applied into two cycles. Each cycle consisted of some phases such as planning, action, observation, and reflection. The instruments used by the researcher to collect the data were observation sheet, field notes and evaluation sheet. In Cycle I, it showed that some of the students were still unable to comprehend the text. However, the students’ ability in mastering vocabulary was increased in Cycle II where the average of the students’ value was 90.33. Therefore, based on the average for the students’ value in the two cycles, it can be categorized in very good level. Hence, Guessing Game can increase the students’ ability in mastering vocabulary.
WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text

Short texts are a common source of knowledge, and the extraction of such valuable information is beneficial for several purposes. Traditional topic models are incapable of analyzing the internal structural information of topics. They are mostly based on the co-occurrence of words at the document level and are often unable to extract semantically relevant topics from short text datasets due to their limited length. Although some traditional topic models are sensitive to word order due to the strong sparsity of data, they do not perform well on short texts. In this paper, we propose a novel word embedding-based topic model (WETM) for short text documents to discover the structural information of topics and words and eliminate the sparsity problem. Moreover, a modified collapsed Gibbs sampling algorithm is proposed to strengthen the semantic coherence of topics in short texts. WETM extracts semantically coherent topics from short texts and finds relationships between words. Extensive experimental results on two real-world datasets show that WETM achieves better topic quality, topic coherence, classification, and clustering results. WETM also requires less execution time compared to traditional topic models.
Evaluating Deep Learning Methods For Classifying Bugs

Software maintenance is a crucial part of software development, especially now more than ever. Without proper maintenance, software can become outdated and unreliable. and vulnerable to security threats, which can have serious consequences for users and organisations that rely on it. But as the software projects become larger, it becomes It is the responsibility of the managers to assign the bugs to the developers so that the developers can use their time efficiently in resolving those bugs. But the capacity of Managers’ ability to analyse each and every bug report and assign it to the appropriate developer is being out- paced by the shear number of bug reports, leading to slow progress. Its impossible for developers or managers to be able to understand hundreds of reports a week, let alone being able to have a good idea of each and every developer in the team to be able to appropriately assign the bugs to them.
Pengembangan Lembar Kerja Peserta Didik Pada Pembelajaran Tematik Subtema Pentingnya Udara Bersih Bagi Pernapasan Kelas V

The purpose of this study was to produce student worksheets in accordance with the needs of students in understanding the material presented in thematic learning. This study aims to identify the feasibility of student worksheet products with the method used, namely the ADDIE method with the stages of Analysis, Design, Development, Implementation, and Evaluation. The feasibility of this product is assessed from the aspects of content, design and language. The data source for this research was 30 students in class V A of Elementary School 26 Pontianak Tenggara, totaling 30 people with two stages of research implementation. The difference in assessment at the two stages of the research is that students like questions or assignments that vary with illustrated pictures that attract attention so that it makes it easier to understand the material for each subject contained in each learning that takes place. Thus the use of illustrated pictures and varied forms of questions can attract students' interest in learning along with the subject matter of learning in each activity on the worksheet helping students understand the material presented by the teacher.
From stone to silicon: technical advances in epigraphy

Abstract Through the annals of time, writing has slowly scrawled its way from the painted surfaces of stone walls to the grooves of inscriptions to the strokes of quill, pen, and ink. While we still inscribe stone (tombstones, monuments) and we continue to write on skin (tattoos abound), our quotidian method of writing on paper is increasingly abandoned in favor of the quick-to-generate digital text. And even though the stone-inscribed text of epigraphy offers demonstrably better permanence than that of writing on skin and paper—even better than that of the memory system of the modern computer (Bollacker in Am Sci 98:106, 2010)—this field of study has also made the digital leap. Today’s scholarly analyses of epigraphic content increasingly rely on high-tech approaches involving data science and computer models. This essay discusses how advances in a number of exciting technologies are enabling the digital analysis of epigraphic texts and accelerating the ability of scholars to preserve, renew, and reinvigorate the study of the inscriptions that remain from throughout history.
A Virtual Community Healthcare Framework in Metaverse Enabled by Digital Twins

Many developed economic bodies are facing an unprecedented increase in the aging population, which brings high demand for healthcare services and it is a specific challenge to ensure the health and safety of seniors living alone in a residential community or in large-scale nursing houses. Thanks to the proliferation of the Internet of Things (IoT), Artificial Intelligence (AI)/Machine Learning (ML) algorithms, and the fifth-generation and beyond (B5G) communication technologies, digital healthcare services (DHS) provide a promising solution. This paper proposes a virtual community healthcare framework (VirCom) leveraging the Internet of Medical Things (IoMT), Digital Twins (DT), and Metaverse, which envisions a seamless interwoven of the physical world and cyberspace. In the context of VirCom, each individual senior is considered a physical object (PO) no matter whether living alone at home or in a room of a nursing house, and their activities and health status are mirrored to corresponding logical objects (LO) in a virtual community in the Metaverse, where activity recognition, potential risk prediction, and alert generation are realized. Specifically, a Multi-sensor Action Recognition-based Senior falling detection (MARS) system is presented as a case study of the VirCom framework, aiming at protecting seniors' safety without compromising their privacy but allowing an instant alerting system when anomalies are detected. An extensive experimental study validated the feasibility of the VirCom framework.
Paper bending stiffness and web tension measurement from a running web using a vacuum and computer imaging

A novel method for measuring the bending stiffness of paper online during manufacturing is intro-duced. The method uses photometric stereo imaging to detect the shape of the deflection surface caused by a controllable pressure difference over the paper’s surfaces. The hardware for the measurement is based on a combination of two existing sensors, which has accelerated and facilitated the development of the implementation. The deflection and loads are tied together by the governing differential equation for the bending of an orthotropic elastic plate with selected simplifications. An approach to resolve material parameters and in-plane loads without knowledge of traditional boundary conditions is suggested. The presented method was tested in a paper mill during manufacturing. For bending stiffnesses measured online, correlation coefficients 0.88 and 0.92 were obtained compared to state-of-the-art laboratory measurements. However, the results gained from a moving web are noisy and the data requires correction of the slope and an offset. Although several issues remain to be resolved before the method can be considered as an accurate measurement for industrial use, the theoretical background, the performance of online implementation, and the results are promising. Possible causes for the observed discrepancies and the future development of the method are discussed.
Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Abstract Book recommender systems provide personalized recommendations of books to users based on their previous searches or purchases. As online trading of books has become increasingly important in recent years, artificial intelligence (AI) algorithms are needed to recommend suitable books to users and encourage them to make purchasing decisions in the short and the long run. In this paper, we consider AI algorithms for so called collaborative book recommender systems, especially the matrix factorization algorithm using the stochastic gradient descent method and the book-based k -nearest-neighbor algorithm. We perform a comprehensive case study based on the Book-Crossing benchmark data set, and implement various variants of both AI algorithms to predict unknown book ratings and to recommend books to individual users based on the highest predicted ratings. This study aims to evaluate the quality of the implemented methods in recommending books by using selected evaluation metrics for AI algorithms.
4-D multiframe co-encoded spectral imaging system

Spectral information can effectively characterise the intrinsic features of a target, while the surface morphology of the object being probed is also a major concern. This paper provides an undersampled spectral 3-D co-imaging scheme. The spectral imaging module of this system shares the optical path with the 3-D imaging module and does not contain a mechanical motion structure, so the scheme is simple in design and stable in structure. A series of experiments have shown that the depth error of this system is within 0.1 mm, the SAM (Spectral Angle Mapper) can reach 4.28 and it has a wide range of applications. It can be applied to non-invasive inspection scenarios such as vegetation monitoring, food safety and heritage conservation.
Research on the Precision Measurement Method of Flat Screen Gap Based on Mobile Vision

In view of the problem that the measurement accuracy of screen gaps is affected by the R region of the transition rounded corner of the large-size flat back cover, a mobile vision system with dual linear motor synchronous drive is designed to realize precise positioning of monocular vision in 16 local image acquisition positions set in the R region. Full-size measurement of the R region is achieved using local information fusion to accurately measure the screen gap. First, to solve the problem of edge breaking and the blurring of local images, a region extraction and segmentation method based on block statistics is proposed to quickly obtain accurate outer edges. Then, an adaptive Hough line detection method based on dichotomy is proposed to accurately locate the outer edges. The experimental results show that the accuracy of region extraction and segmentation is up to 99.68% within 60 ms; the angle error and position error of line detection are less than 0.01° and 0.2 pixels, respectively. With optimal anti-interference performance, the proposed algorithm realizes the adaptive and accurate positioning of the outer edge within 200 ms; the measurement time and the measurement error of the measurement system are less than 6.0 s and 0.03 mm, respectively.
Dynamic Transaction Confirmation Sharding Protocol for Alliance Chain

Alliance chain has gained widespread popularity in industrial and commercial fields due to its multi-centralization and node manageability. Current implementations of the alliance chain suffer from scalability obstacles, such as communication congestion and throughput drop, when the number of nodes increases. In this paper, a novel dynamic transaction confirmation sharding protocol is proposed, which improves transaction processing efficiency by partitioning nodes and assigning different transactions to different shards. It utilizes dynamic transaction confirmation consensus as a sharding intra-consensus mechanism to minimize message size and package transactions into microblocks, which modifies communication content during transaction propagation among shards and reduces network congestion and shard reconfigure cost. The protocol leverages a review system and reputation model to identify and punish malicious nodes and also incorporates a verifiable random function for node configuration, which ensures a sufficient number of honest nodes within the shard and prevents repeated consensus processes. Simulation results show that the proposed protocol outperforms mainstream used permissioned chain sharding protocols Attested HyperLedger and Sharper, achieving a throughput improvement of at least 20%. This protocol is suitable for scenarios requiring high throughput and reliability in industrial and commercial fields such as finance, logistics, and supply chain management. Even if the number of alliance chain nodes increases to the usual maximum, or there are some faulty nodes, the protocol can still maintain stable performance.
Stellar representation of extremal Wigner-negative spin states

The Majorana stellar representation is used to characterize spin states that have a maximally negative Wigner quasiprobability distribution on a spherical phase space. These maximally Wigner-negative spin states generally exhibit a partial but not high degree of symmetry within their star configurations. In particular, for spin $j > 2$, maximal constellations do not correspond to a Platonic solid when available and do not follow an obvious geometric pattern as dimension increases. In addition, they are generally different from spin states that maximize other measures of nonclassicality such as anticoherence or geometric entanglement. Random states ($j \leq 6$) display on average a relatively high amount of negativity, but the extremal states and those with similar negativity are statistically rare in Hilbert space. We also prove that all spin coherent states of arbitrary dimension have non-zero Wigner negativity. This offers evidence that all pure spin states also have non-zero Wigner negativity. The results can be applied to qubit ensembles exhibiting permutation invariance.
On a wider class of prior distributions for graphical models

Abstract Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of $\mathcal{G}_n$ , the set of all graphs in n vertices. One approach that has been proposed to tackle this problem is to limit search to subsets of $\mathcal{G}_n$ . In this paper we study subsets that are vector subspaces with the cycle space $\mathcal{C}_n$ as the main example. We propose a novel prior on $\mathcal{C}_n$ based on linear combinations of cycle basis elements and present its theoretical properties. Using this prior, we implement a Markov chain Monte Carlo algorithm, and show that (i) posterior edge inclusion estimates computed with our technique are comparable to estimates from the standard technique despite searching a smaller graph space, and (ii) the vector space perspective enables straightforward implementation of MCMC algorithms.
The metaverse: updating the Internet (Web 3.0) or just a new development for immersive videogames?

Are we witnessing the return of virtual worlds such as Deuxième Monde (1997) or Second Life (2003), boosted and enhanced by technologies? Or is it the coming of the next generation of the Internet (Web 3.0)? Or is it just a marketing repackaging of virtual reality markets that up to now did not deliver as expected? This paper attempts to provide answers to these questions. It introduces the notion of the metaverse, looks at its definition(s), and describes its key elements, thereby outlining the metaverse ecosystem. The article also attempts to draw lessons from the pioneering experiences of former virtual worlds, and thus examines some case studies from the video game industry. In conclusion, we investigate the metaverse’s potential constraints (energy/environment, cost of hardware and bandwidth, lack of business models, regulation) and opportunities, and reveal the challenges ahead for its widespread adoption.