Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Implementation in SAP

People, both in business and in public life always want to secure their freedom - freedom of expression, communication, and above all, freedom of choice. However, this concept of freedom is rarely extended to the consumer knowledge perspective. After all, customers are human beings and freedom is at the heart of the concept of shopping and food. Welcome to the world of Artificial intelligence (AI). When AI is integrated with customer experience, it can create a customer journey that allows customers to have their own experience, ultimately leading to more agency and more satisfied customers. What matters the most in business efficiency today is customer relation management and the time it takes to provide business solution to them. The profit/loss graph is heavily dependent on users’ trust and experience with the company or organization. Understanding the importance of using ERP systems in various sectors like Education, Bank, Telecom, Software solution company is well known in the IT industry today. From cafeteria income and outcome to company’s revenue and turnover generation to debit and credits; from students’ detail to performance of the student in academics in particular subject, everything can be achieved through Open-source software SAP. Implementing the vast powers of SAP integrated with AI gives boosting efficiency to the company. According to SAP, Gartner's latest report stated, "By 2023, organizations in the advanced stages of digital transformation will find that poor customer experience is their biggest obstacle to continued success. "Client misunderstandings can be the result of inability to effectively manage complaints, misunderstanding of customer feedback, and ignorance of customer preferences. There is a need to close the gap between freedom and technology. If artificial intelligence is integrated with SAP, it can close that gap.
OOXML Document Data Recovery Algorithm Based on Confidence Subset

This Aiming at the problem of text content data corruption in OOXML documents caused by noise errors during network transmission, this paper proposes a confidence-based document recovery algorithm. Firstly, the algorithm determines the size of the low confidence interval to be recovered by the signal noise ratio, and determines the error subset according to the confidence information provided by the physical layer. All possible combinations of the send sequence are obtained by iterating through the error pattern and extracting the character data through the inflate decompression. Finally, the least number of illegal characters is taken as the criterion to obtain the recovered document content. Simulation results show that the algorithm can effectively improve the data recovery rate of damaged documents by using the underlying confidence information.
Research on Mathematical Statistics Model Algorithm under Improved Algorithm

The current evolutionary algorithms for solving multi-objective optimization mainly consider how to deal with the optimization of multiple conflicting objectives, and rarely consider the problem of processing constraints. Therefore, we propose a differential evolution algorithm for elite retention and non-dominated solution set migration operations in the evolution process to ensure the diversity of Pareto optimal solutions for multi-objective optimization problems. The performance of the basic differential evolution algorithm is improved through group chaotic initialization, adaptive crossover and mutation operations. The simulation results of three classic test functions show that the algorithm in this paper is better than the non-dominated sorting genetic algorithm in terms of uniformity, approximation and convergence speed, and the convergence speed is also better than the other two improved evolutionary algorithms.
Comparative Performance Evaluation of Web-Based Book Recommender Systems

In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential.
Design and Implementation of Automatic Selection of the Most Efficient Itemset Algorithm Based on Spark

The combination of Spark distributed platform and High-Utility Itemset Mining can solve the problem of long running time issue of High-Utility Itemset Mining. In the experiment, we conclude that Spark-based parallel D2HUP and EFIM algorithms have a greater improvement in running time efficiency than serial algorithms. The existing research has shown that the EFIM and D2HUP algorithms are the two best algorithms for High-Utility Itemset Mining. This paper generates 118 datasets by generating and collecting the running time of the two algorithms in the real and simulated datasets, taking into account the characteristics of each dataset’s length, sparse degree, and dataset size as characteristics with running time as the prediction target and then establishing a model. The accuracy of the prediction was evaluated through experiments, and a set of rules based on decision trees was generated. According to the rules, the fastest algorithm between EFIM and D2HUP can be predicted very well.
Chinese rubbing document image binarization image based on automatic seed region growth

Rubbings of China is a great treasure, of which the recorded information is later generations research was one of the important ways in politics, economy and culture. But the rubbings are vulnerable to damage. However, the partition of rubbings can make this information better preserved, which is of great significance to the protection of cultural relics, historical research and cultural inheritance. This paper combined region growing segmentation and connected domain labeling to segment the historical document image. Firstly, the OTSU algorithm has been employed for document image binarization. Second, the binarized image has been corroded to remove isolated noise points, and then the effective connected area is extracted by the connectivity domain marking algorithm to find the initial value seed point. Finally, the seed point segmented the area growth to obtain a clear binarized document image. Experiments showed that the automatic seed region growth leads to performance improvements over the K-means cluster and OSTU method. We tested our method using the DIBCO 2010 dataset, which showed segmentation accuracy of up to 98%.
Tool-Assisted Componentization of Java Applications

Many popular object-oriented (OO) programming languages, such as Java, do not provide explicit support for architecture-based development, i.e., do not provide programming-language constructs that are at the granularity of architectural constructs, such as components and ports. The gap between how engineers design their systems and how they implement them has been one of the leading causes of architectural drift—a situation in which the prescriptive architecture (the designed architecture) does not match the descriptive architecture (the implemented architecture). To mitigate this challenge, in its ninth iteration, Java introduced the concept of Java Platform Module System (JPMS), which for the first time provides explicit implementation-level support for well-known architectural constructs, such as components (called modules) and ports (called module directives). Despite this, the majority of existing Java applications (apps) are still purely OO programs that do not make use of the new constructs, because converting them to well-structured component-based (CB) programs is a tedious and error-prone task. In fact, prior research has shown that when engineers convert OO apps to CB apps, they tend to be highly over-privileged, i.e., components are granted more access privileges than they actually need. To mitigate these challenges, we have developed OO2CB, an approach for conversion of an OO Java app to a least-privilege CB Java app. OO2CB employs component recovery techniques to assist the developer in determining a given OO app’s components. It then statically analyzes the source code of the app to determine the dependencies among its recovered components and the required port types for facilitating their interaction. Finally, OO2CB generates a functionally equivalent CB app that satisfies the least-privilege security principle. Our experiments on several large real-world OO Java apps corroborate the effectiveness of OO2CB.
Analysis and Research of Generative Adversarial Network in Anomaly Detection

In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected.
Evaluation and Optimization on Virtualization Performance Cost under Semantic Gap

Virtualization is a key enabling technology in modern data centers. While it provides numerous benefits, it also creates new problems. Virtualization requires the hypervisor to treat the virtual machine as a black box, limiting the ability of information exchange between the hypervisor and the virtual machine, bringing a problem known as the semantic gap. Currently, much research on the semantic gap mainly focuses on bridging the semantic gap. The evaluation of the semantic gap, on the other hand, is a neglected but crucial problem, and relevant research is currently lacking. Therefore, this paper proposes a corresponding virtualization performance cost model to better evaluate the semantic gap. Based on this cost model, we summarize solutions that can be used to alleviate the semantic gap. Furthermore, we propose a novel evaluation method for the CPU double scheduling semantic gap. Finally, we propose an effective virtio-balloon based dynamic memory tuning strategy to alleviate the memory semantic gap. The experiments show that for 400.perlbench, our strategy saves 551MB of memory on average during running and reclaims 990MB of memory after running, with a performance cost of only 1.1%. For 429.mcf, our strategy saves 815MB of memory on average during running and reclaims 2130MB of memory after running, although with the performance cost of 27.6%, it prevents performance cliff-like drop caused by memory shortage.
Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising

In order to improve the transmission effect of low-power communication signal of Internet of Things and compress the enhancement time of low-power communication signal, this paper designs a low-power communication signal enhancement method of Internet of Things based on nonlocal mean denoising. Firstly, the residual of one-dimensional communication layer is preprocessed by convolution core to obtain the residual of one-dimensional communication layer. Then, according to the two classification recognition methods, the noise reduction signal feature recognition of the low-power communication signal of the Internet of Things is realized, the nonlocal mean noise reduction algorithm is used to remove the low-power communication signal of the Internet of Things, and the weight value between similar blocks is calculated according to the European distance method. Finally, the low-power communication signal enhancement of the Internet of Things is realized by the nonlocal mean value denoising method. The experimental results show that the communication signal enhancement time overhead of this method is low, which is always less than 2.6 s. The lowest bit error rate after signal enhancement is about 1%, and the signal-to-noise ratio is up to 18 dB, which shows that this method can achieve signal enhancement.
GSDM: A Gated Semantic Discriminating Model for Knowledge Graph Completion

Knowledge representation learning is an automatic learning technique that can embeds a knowledge graph into a low-dimensional vector space. With use of this, knowledge becomes computable and various intelligent applications can be realized. Traditional semantic discriminating models suggest that the embeddings of entities should depend on the specific semantic environment. We find that the multiple latent information of relations has not been put to use by these models. In this paper, a gated semantic discriminating model (GSDM) is proposed to select useful latent information and neglect useless information according to the specific semantic environment for both entities and relations. Experiments show that GSDM achieves better performance than related state-of-the-art baselines on most indicators. The better trade-off between the discriminate parameter pressure and the model performance has proved the correctness and feasibility of semantic discriminating mechanism to some extent.
Evaluating Robustness of Sequence-based Deepfake Detector Models by Adversarial Perturbation

Deepfake videos are getting better in quality and can be used for dangerous disinformation campaigns. The pressing need to detect these videos has motivated researchers to develop different types of detection models. Among them, the models that utilize temporal information (i.e., sequence-based models) are more effective at detection than the ones that only detect intra-frame discrepancies. Recent work has shown that the latter detection models can be fooled with adversarial examples, leveraging the rich literature on crafting adversarial (still) images. It is less clear, however, how well these attacks will work on sequence-based models that operate on information taken over multiple frames. In this paper, we explore the effectiveness of the Fast Gradient Sign Method (FGSM) and the Carlini-Wagner L2-norm attack to fool sequence-based deepfake detector models in both the white-box and black-box settings. The experimental results show that the attacks are effective with a maximum success rate of 99.72% and 67.14% in the white-box and black-box attack scenarios, respectively. This highlights the importance of developing more robust sequence-based deepfake detectors and opens up directions for future research.
A Comprehensive Learning Moth-Flame Optimization with Low Discrepancy Sequence

The moth-flame optimization (MFO) algorithm is extensively employed to attain the global optimization of a problem. The original MFO algorithm has drawbacks of low population diversity, slow convergence speed, and falling into local optimum easily. An improved moth-flame optimization algorithm (CLMFOLDS) based on comprehensive learning (CL) mechanism and low discrepancy sequence (LDS) is presented in this paper to solve the problem. A random population with uniform distribution is generated in the search space by using LDS. The information of the entire population is learned to gain the position of the new moth, which is called the CL strategy. External storage is designed to save the suboptimal solution throughout the iteration. The elimination mechanism is employed to strike out the poor solution in the population to enhance the global search capability of the algorithm. The CLMFOLDS is assessed in the CEC 2017 benchmark problem. Experimental results illustrate that the CLMFOLDS algorithm is superior to state-of-the-art algorithms.
A Trusted Notifier Reporting Framework for Incentive DNS Abuse Mitigation Based on Blockchain

Serving as the global Internet’s phonebook, the Domain Name Service (DNS) is utilized for translating human-friendly domain names into machine-readable IP addresses, which makes DNS essential to the operation of the Internet and heavily relied on by today’s almost all various online services. Note that in some cases, DNS could be exploited maliciously and involved into some unexpected scenarios such as phishing, spamming, and harmful content distributing. We refer to these kinds of exploitation as DNS Abuse. Over the past years, DNS Abuse has been suffering an explosive growth, and thus DNS Abuse mitigation has been drawing more and more researchers’ attentions. In this paper, we reexamine some current major efforts made in this area, and find out some potential concerns or limitations which could bring negative impact to their usability and reliability for worldwide adoption or deployment. In this context, we further propose an incentive and decentralized procedure by introducing a trusted notifier reporting framework based on blockchain, which could guarantee the usability and reliability of DNS Abuse mitigation more significantly for worldwide deployment.
Unscented Kalman filter with improved forward-backward prediction for target tracking

In order to improve the accuracy of active sonar underwater target tracking, an improved algorithm is proposed based on the existing forward-backward prediction algorithm of unscented Kalman filter. The algorithm can adaptively perform forward-backward prediction operation, and replace the measured value with the state estimate value of the past moment to avoid nonlinear problem in reverse prediction. The simulation results show that when the total calculation amount is almost the same, the algorithm can improve the accuracy of state estimation better than the standard algorithm.
Phygital customer experience strategy enabled by extended reality technology (ERT)

Extended reality technology (ERT) includes various visualization tools such as avatars, virtual models in 3D, or virtual and augmented reality devices that companies can incorporate in their digital strategies to design immersive and realistic customer experiences. Thus, using ERT allows companies to offer their customers the possibility of visualizing and personalizing products online, and thus they are more inclined to buy the product, as their perception of the displayed image is positive. This chapter aims to answer the following questions: what does ERT mean? What are its devices? And how can companies use ERT to create immersive shopping experiences that connect digital and physical settings, leading customers to experience their online purchase as if the experience is occurring in physical stores?
Analisis Spektra UV-Visible Senyawa Bahan Alam Tersensitasi Zat Warna: Pengembangan Modul Praktikum Kimia Komputasi

The research aims to examine the validity, practicality, and effectiveness of the computation chemistry laboratory works module to analyze spectra UV-Visible of natural compounds in dye-sensitized solar cells. The research design is 4D (Define, Design, Development, and Dissemination). The population is 80 students in the Chemistry Education Study Program, The Faculty of Teacher Training and Education Studi, the University of Mataram Indonesia, who have completed the computation chemistry course. The sample in this research is 20 students taking to be respondents for the computation chemistry laboratory practical works module with a random sampling technique. The validity value studied by Aiken V of the laboratory module is V= 0,90 with the reliability R=0,98. It means the laboratory module has a highly valid category to be applied. The value of the practicality module gained from the students' responses is with average practicality of 91%. It means the module has a highly practical category. The module's effectiveness could be seen from the students' point with an average of 83.5, which shows that the module has a high effectiveness category. In conclusion, the computation chemistry laboratory works module that is being developed is highly valid, practical, and effective to be applied.
Competitive Decentralized Randomness Beacon Protocols

A distributed and reliable source of randomness is always a critical element in cryptography, both in the construction and application of cryptographic primitives. Modern cryptography such as blockchain, cryptocurrencies, decentralized finance is heavily dependent on a trusted randomness source. In particular, Decentralized Randomness Beacon (DRB) protocols can be a reliable source of randomness. A DRB protocol generates a continuous stream of publicly verifiable random values. Almost all the available DRB protocols are collaborative in nature where participants of a DRB protocol collaborate their local entropy to generate global randomness.
Improved Global and Local Graph Neural Network for Session-based Recommendation

The session-based recommendation(SBR) aims to predict user actions based on anonymous sessions. SBR focuses on recent sessions to make more real-time and reliable recommendations and does not need to extract user IDs, thereby alleviating privacy issues. Recent research mainly models user preferences based on the target session while ignoring other sessions, which may contain items transitions related to and unrelated to the target session. This paper proposes a novel method, namely Improved Global and Local Graph Neural Network (IGL-GNN), to models item transitions within not only the target session but also the other sessions. Specifically, we learn the session-level item representation from the local graph, learns the global-level item representation from the global graph. We propose a new method to effectively aggregate global and local representations, which not only retains more useful information but also removes useless information. In addition, we innovatively introduced Transformer, incorporated it into the model as a general deformation function, enhanced the ability to obtain complex transformations, and solved the limitations of the model in learning complex representations. We have done a lot of experiments on three benchmark datasets, and the results show that the performance of IGL-GNN is better than the most advanced methods.
Beyond backpropagate through time: Efficient model‐based training through time‐splitting

Model-based policy gradient (MBPG) has been employed to seek an approximate solution to the optimal control problem. However, there is coupling between adjacent states due to temporal dependencies, making the training time grow linearly with the time horizon. This paper reshapes the training process of MBPG with the time-splitting technique to establish a time-independent algorithm called Training Through Time-Splitting (T3S). First, copy the coupled variables to obtain two independent variables. Meanwhile, an extra variable together with an equivalence constraint is introduced for problem consistency. Then, the transformed problem divides into subproblems with carefully derived loss functions. Subproblems own decoupled variables and shared policy networks, which means they can be optimized concurrently. Guided by the algorithm design, this paper further proposes an asynchronous parallel training scheme to accelerate training efficiency. Numerical simulation shows that the T3S algorithm outperforms the MBPG algorithm by 83.6% in wall-clock time with a trajectory tracking task.