Reviewerly/exHarmony huggingface dataset:
Marker Identification of Disc Workpiece Based on HOG-SVM Classifier

With the less humanized trend of manufacturing production line gradually progressing, real-time recording and tracking of workpieces information have become an essential way to implement efficient management. To collect and identify the workpiece marker is of great significance for product information statistics and quality traceability. Aiming at the problems of inaccurately positioning, difficultly segmenting, and slowly recognizing for capturing the marker of disc workpieces distributed circularly, this study proposes a projection segmentation algorithm based on polar coordinate inverse transformation to locate and separate the circularly arranged marker and extract the total separated characters. The features of the directional gradient histogram (HOG) of characters are used as the input of the support vector machine (SVM) model, and after training, a workpiece marker recognition classifier is obtained. The experiment results show that the recognition accuracy of markers composed of letters and numbers is over 97% by the proposed method. Our proposed method outperformed state-of-the-art approaches in achieving higher recognition accuracy rate with the SVM classifier.
Long-term Data Sharing under Exclusivity Attacks

The quality of learning generally improves with the scale and diversity of data. Companies and institutions can therefore benefit from building models over shared data. Many cloud and blockchain platforms, as well as government initiatives, are interested in providing this type of service.
Privacy preserving mobile forensic framework using role‐based access control and cryptography

Summary The rise of social media‐related crimes has led to the rise of mobile forensics. Since mobile forensics and privacy preservation are conflicting fields, it is important to find a middle ground where forensics can be performed on any device without compromising the confidentiality of an individual. This paper presents a framework called “role‐based mobile forensics framework with cryptography (RBMF2C)” that can be easily implemented and protects users' privacy and does not interfere with the forensic process. A mobile forensic platform called Sher‐locked phones developed using C# is also presented in this paper that is developed following the aforementioned RBMF2C framework. This platform consists of five layers: access control, evidence gathering, data analysis, privacy, and reporting layer. The developed platform implements the RBMF2C framework on the evidence gathering, analysis, and reporting layer to protect the evidential image, evidential findings, and final report from being accessed by unauthorized users. The implementation of privacy preservation techniques as proposed by the proposed framework such as role‐based access control, keyword search, and encryption/ decryption did not hinder the performance of the developed toolkit, and suspects data privacy is also preserved to a substantial extent.
Nurturing Spirituality: In Conjunction with Integral Education

Spirituality facilitates a deeper contemplation of reality, and it provides a better understanding of the self and the daily struggles of life. Spirituality develops the divine potential of learners and prepares them for life by giving them the tools they need to keep on learning through their experiences. It enables them to develop more completely and comprehensively. In a way, it is training for life. This research paper explicates the meaning, importance, and understanding of spirituality as a part of Integral Education. The researcher in this paper has tried to study the different practices at home to ensure the development of spirituality among learners. In this study, the researcher tried to explore the dimensions and components of spirituality and establish its relevance in the present day. The study also explored how spirituality can be integrated into the daily lives of learners to help them grow as better human beings and lead a value oriented life.
Oral English Auxiliary Teaching System Based on Deep Learning

In order to solve the problem of the oral English auxiliary teaching system, a research based on Deep Learning was proposed. Based on the theory of Deep Learning, the teaching mode of Deep Learning for college students built on information technology was investigated in the research. As for Shallow Strategy, the item “I think that the best way to pass the English examination is to keep high frequency words in mind” changed most significantly from 3.3 to 2.68. As for Deep Strategy, the most significant change was the item “It can be very interesting for almost all the oral English topics as long as you engage in it actively.” The value increased from 3.31 to 3.97, which was almost close to 4. From the comparison, it could be found that the students changed from memorizing English words mechanically and passively in order to pass the exam to engaging in the oral English situation actively to solve problems so as to obtain self-satisfaction. As a language, English can reflect a unique cultural heritage. It is different from Chinese culture, which can improve learners’ spiritual and cultural accomplishments subtly. In addition, we should try to solve problems with English logical thinking, which can train our critical thinking and oral expression ability, enhance our self-confidence, and improve our sense of self-efficacy. A design-based research paradigm was used in the research. Through the integrated use of information technology, it aimed at building out a deeply mixed teaching mode based on “Cloud Class and Offline Class.” A combination of quantitative and qualitative data collection methods was adopted to evaluate the practical effect. According to the experimental results, two rounds of mixed-mode iterative loop design were performed for the mixed teaching mode. In order to make it more operable, it was improved and perfected continuously.
Skew‐cyclic codes based public‐key cryptosystem approach

Abstract This is a survey on the theory of skew‐cyclic codes over the ring , where v 2 = v . Coding theory plays an important role in the constructing cryptosystems. In this survey, we propose a public‐key cryptosystem using skew‐cyclic codes. The encryption is done by the Gray map ψ and the projection map φ is used for decryption. This approach is different from the others. More importantly, it is a secure system.
An efficient hardware implementation of CNN-based object trackers for real-time applications

Abstract The object tracking field continues to evolve as an important application of computer vision. Real-time performance is typically required in most applications of object tracking. The recent introduction of Convolutional Neural network (CNN) techniques to the object tracking field enabled the attainment of significant performance gains. However, the heavy computational load required for CNNs conflicts with the real-time requirements required for object tracking. In this paper, we address these computational limitations on the algorithm-side and the circuit-side. On the algorithm side, we adopt interpolation schemes which can significantly reduce the processing time and the memory storage requirements. We also evaluate the approximation of the hardware-expensive computations to attain an efficient hardware design. Moreover, we modify the online-training scheme in order to achieve a constant processing time across all video frames. On the circuit side, we developed a hardware accelerator of the online training stage. We avoid transposed reading from the external memory to speed-up the data movement with no performance degradation. Our proposed hardware accelerator achieves 44 frames-per-second in training the fully connected layers.
The Main Influencing Factors of Artificial Intelligence Big Data Sports News Coverage

Through systematic analysis and research on the reporting methods of various sports events and the status quo of data news under the background of big data, it is found that sports news reports under the background of big data have presented new reporting forms and contents. Combining the application of open source data with sports news can make good use of relevant technical forces to improve the reading efficiency of sports news. This can consolidate old audiences, attract new audiences, and maximize the promotion of various sports events, allowing more audiences to participate. After distributing 336 questionnaires, 303 were recovered. A total of 303 answer sheets met the requirements. The successful recovery rate of the questionnaire was 90.18%. Among them, 172 were male, accounting for 56.77% of the valid questionnaires; 131 were female, accounting for 43.23% of the valid questionnaires. Full-time students accounted for a relatively high proportion of the survey respondents, with a total of 130 people, accounting for 42.90% of the valid questionnaires. It can be seen that most of the people who pay attention to sports news reports are young and middle-aged groups.
Exact algorithms for the product configuration problem

Abstract A software product line (SPL) is a software engineering reusability approach used to create different applications from a shared set of common software features. The product configuration problem (PCP) consists in finding the product, among all the products allowed by the SPL, that best fits the customer's preferences and requirements. PCP is a generalization of the tree knapsack problem (TKP) with additional AND, XOR, and OR constraints. Therefore, this problem is also NP‐hard. Previous work in the literature could solve only instances with up to 500 features. In this paper, we propose a pseudo‐polynomial time dynamic programming algorithm that solves PCP in , where n is the number of features and H is the customer's budget. We also propose an integer linear programming formulation for PCP, which is solved by the CPLEX's branch‐and‐bound algorithm. Computational experiments, performed on realistic instances from the literature, showed that the proposed algorithms were able to find optimal solutions for all the instances with up to 10,000 features.
Improvement of English Classroom Teaching Effect Based on Real-Time Supervision of Student Status

For English courses, the interaction between teachers and students in the classroom teaching process has a crucial impact on the improvement of classroom teaching effects, and through real-time supervision of students’ classroom learning status, students’ learning dynamics and learning effects can be grasped in time. For this reason, this paper proposes a learning state semisupervised learning method and real-time state monitoring system based on a clustering algorithm and a self-training SVM classification algorithm. At the same time, combined with the reform of English classroom teaching, the teaching effect can be improved to the greatest extent through the supervision of students’ learning state. The experimental results show that monitoring the primary school English classroom teaching quality subject and providing skill training can help the primary school English classroom teaching quality subject predict the possible situation in the monitoring and prepare in advance in terms of thinking and skill teaching. Schools can urge primary school English classroom teaching quality monitoring subjects to have basic monitoring knowledge literacy, monitoring ability literacy, and monitoring moral literacy through primary school English classroom teaching quality monitoring knowledge training, skill training, and moral training. On the basis, the main body of primary school English classroom teaching quality monitoring can give full play to the role of the main body of monitoring when it organizes and implements monitoring. It is proved that real-time supervision of students’ status can effectively improve the effect of English classroom teaching.
The Humble Programmer

introduction Share on The Humble Programmer Author: Edsger W. Dijkstra View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 5–18https://doi.org/10.1145/3544585.3544588Published:13 July 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Unifying attentive sparse autoencoder with neural collaborative filtering for recommendation

The autoencoder network has been proven to be one of the powerful techniques for recommender systems. Currently, the ways of utilizing autoencoder in recommender systems can be divided into two categories: modeling user-item interaction rely solely on autoencoder and integrating autoencoder with other models. Most existing methods based on autoencoder assume that all features of model’s input are equally the same contributing to the final prediction, which can be regarded as attention weight vectors; however, this hypothesis is not reliable, especially when exploring users’ interaction frequency with different items. Moreover, combining autoencoder with traditional methods, the usual strategy is to leverage a linear kernel of the inner product of user and item vectors to predict user preferences, which will lead to insufficient expression power and hurt the performance of recommendation when facing data sparsity and cold start problems. To tackle the above two problems, we propose a novel hybrid deep learning model for top-n recommendation, called attentive stacked sparse autoencoder (A-SAERec), which can capture attention weights vector of a user for items, and then combined with the neural matrix factorization to improve the performance of recommender model. Extensive experiments on four real-world datasets show that our A-SAERec algorithm has significant improvements over state-of-the-art algorithms.
Hagrid: using Hilbert and Gosper curves to gridify scatterplots

Abstract A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid , a technique that uses space-filling curves (SFCs) to “gridify” a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid , Small multiples with gaps SMWG , and CorrelatedMultiples CMDS , in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot. Graphical abstract
DAMGNN: Deep adaptive multi-channel graph neural networks

Recently, several studies have reported that Graph Convolutional Networks (GCN) exhibit defects in integrating node features and topological structures in graphs. Although the proposal of AMGCN compensates for the drawbacks of GCN to some extent, it still cannot solve GCN’s insufficient fusion abilities fundamentally. Thus it is essential to find a network component with stronger fusion abilities to substitute GCN. Meanwhile, a Deep Adaptive Graph Neural Network (DAGNN) proposed by Liu et al. can adaptively aggregate information from different hops of neighborhoods, which remarkably benefits its fusion abilities. To replace GCN with DAGNN network in AMGCN model and further strengthen the fusion abilities of DAGNN network itself, we make further improvements based on DAGNN model to obtain DAGNN variant. Moreover, experimentally the fusion abilities of the DAGNN variant are verified to be far stronger than GCN. And then build on that, we propose a Deep Adaptive Multi-channel Graph Neural Network (DAMGNN). The results of lots of comparative experiments on multiple benchmark datasets show that the DAMGNN model can extract relevant information from node features and topological structures to the maximum extent for fusion, thus significantly improving the accuracy of node classification.
An exact inversion method for multi-input-single-output decomposable TS fuzzy systems

Almost all exact inversion methods provide inverse solutions for only one input variable of fuzzy systems. These methods have certain limitations on the fuzzy system structure such as monotonic rule bases, singleton rule consequents, and invertibility check. These requirements limit the modeling capabilities of the fuzzy systems and also may result in poor application performances. In this study, an exact analytical inversion method is presented for multi-input-single-output decomposable TS fuzzy systems with either singleton or linear consequents. In the proposed method, fuzzy system structures do not need to have monotonic rule bases, singleton rule consequents, or any invertibility conditions. Thus, more flexible fuzzy systems can be used in inverse model based applications. The proposed method provides a simple and systematic way to obtain unique inverse solutions of all input variables simultaneously with respect to any desired system output value. For this purpose, an inversion trajectory approach that guarantees the existence and uniqueness of the inverse solutions is introduced. The inversion trajectory consists of a set of paths defined on the specific edges of universe of discourses of the decomposed fuzzy subsystems. Using this approach, the inverse definition of the overall fuzzy system can easily be derived only by inverting the related decomposed fuzzy subsystems on this inversion trajectory and then combining their inverse definitions. In this way, the inverse definition of the overall fuzzy system is obtained as consisting of analytical solutions of linear and quadratic equations for singleton and linear consequent cases, respectively. Simulation studies are given for the inversion of two and three-input-single-output fuzzy systems, and the exactness and effectiveness of the proposed method are demonstrated.
Sequential tree recognition method of sensitive data in energy big data center based on rule matching

In this paper, the sequential tree recognition method of sensitive data in energy big data center based on rule matching is studied, to accurately identify sensitive data in energy big data center, and improve the operation security of energy big data center. The RETE rule matching algorithm is used to match the sensitive data rules of the energy big data center. The algorithm automatically finds the optimal rete topology, reduces the join intermediate node data, and realizes rule matching. The data cut points after rule matching are divided into balanced cutting points and unbalanced cutting points. The maximum sorting mutual information only exists at the unbalanced cut points. The ordered decision tree can be constructed by traversing the unbalanced cutting points. The data to be identified can be retrieved in the form of data flow to obtain the word frequency, regional information and sensitivity of sensitive words, and the sensitive data can be identified according to the sensitivity calculation results. The experimental results show that the proposed method can effectively identify the sensitive data of energy big data center with high recognition accuracy, and can be applied to the practical application of energy big data center.
OhioLINK’s Formative Milestones: Part II, 2003–2022

OhioLINK, one of the leading academic library consortia in the US, marks its 30th anniversary in 2022. This paper, the second of two parts, reviews the organization’s history through selected formative milestones and provides observations about the evolution of the consortium from its current executive director, revealing the importance of expanding cost-effective and easy access to scholarly resources, economies of scale, pushing technology, and a focus on the user.
An Image Inpainting Method Based on Whale-Integrated Monarch Butterfly Optimization-Based DCNN

This paper proposes an image inpainting method based on Whale integrated Monarch Butterfly Optimization-based Deep Convolutional Neural network (Whale-MBO-DCNN) model. Initially, the patch extraction and mapping are applied to the input image to extract the patches of the image followed by image reconstruction in order to map the patches. The patch with minimum distance is selected using the concept of Bhattacharya distance in patch extraction. On the other hand, the construction of the residual image form the input image is done using Deep CNN, which is trained with the proposed Whale-MBO algorithm. The proposed Whale-MBO algorithm is developed from the integration of Monarch Butterfly Optimization (MBO) and (WOA. Finally, the residual image and the reconstructed image are fused using Holoentropy to obtain the reconstructed image. The experimentation is performed using the evaluation metrics, such as PSNR, SDME, and SSIM. The effectiveness of the proposed image inpainting method is revealed through a higher PSNR, SDME, and SSIM of 33.0585, 74.4249, and 0.9479, respectively.
Introduction to Special Issue on French Variation in Digital Media

Digital discourse, often referred to as computer-mediated communication, has revolutionized communication practices since the advent of the personal computer (Crystal, 2011), with ever-growing effects, as smart devices proliferate in every aspect of twenty-first-century human existence. Linguists in the francophone world first took note of these new digital interactional practices in the 1980s with the onset of conversation via Minitel (Levy, 1993), a French-born service that consisted of a computer terminal that connected via telephone lines to remote services like chatrooms, interactive games, and purchasing platforms, years before most Americans had ever heard of the world wide web (Mailland, 2017). Minitel terminals remained functional some 30 years later; the service was ultimately decommissioned in 2012 on account of outdated modems, an inability to support advancements in graphics and the earlier mass migration (in France and beyond) to the present-day internet (Mailland, 2017). But the introduction of virtual interactional spaces – in the francophone world and beyond – had marked the beginning of a new linguistic trend in which users found themselves regularly engaged in the production of written language infused with vernacular tendencies.