Reviewerly/exHarmony huggingface dataset:
A tampering detection algorithm based on multi-scrambling coding

In order to authenticate the content of the image and find out the tampered location, semi-fragile watermark technology usually extracts feature information from image blocks as watermark, which will be embedded into irrelevant blocks by block mapping algorithm. When a block is tampered, its original feature hidden in other blocks will not be changed. However, it must be pointed out that this situation only considers that the original feature information of the tampered block has been tampered, but ignores that the feature information of other blocks embedded in the tampered position is also destroyed. It is impossible to recognize that whether the tampered area itself or the image block whose feature information embedded in the tampered area is tampered, resulting in increasing of false detection rate. In order to improve the accuracy of tamper detection of semi-fragile watermark algorithm, this paper proposes a novel tampering detection algorithm, which performs multi-scrambling encoding on the extracted feature information, and establishing a tampering equation related to each block according to the multi-scrambling encoding. According to the sum of tampering equation to determine whether the image block has been tampered. Experimental results show that after using the tampering detection algorithm proposed by this paper, both the false rejection probability and false acceptance probability are lower than the previous algorithms. In addition, the false acceptance probability is still very low under JPEG compression.
Entity Disambiguation Based on UCL Knowledge Space

Named entity disambiguation is an important task in the process of knowledge space construction and improvement, which builds a bridge between entities in text and entities in knowledge space. However, the existing entity disambiguation methods mainly rely on the similar or related entities in the context. For the short text widely appeared in the practical application scenarios, the performance of the existing methods drops sharply. In this paper, we propose an entity disambiguation method based on Uniform Content Label knowledge space (UCLKS-ED). This method uses UCL knowledge space to provide additional knowledge information for the matching task of candidate entities and the source entity, and extends the representation of the source entity by conceptualizing. The experimental results on a large manually annotated short-text dataset and another standard dataset demonstrate that the proposed method achieves significant improvement over the state-of-the-art baselines.
Pemetaan untuk Strategi Dakwah di Kota Semarang Menggunakan Pendekatan Data Mining (Mapping for Da'wah Strategy in Semarang City Using Data Mining Approach)

This paper aims to explore the potential of da'wah in the city of Semarang with a data mining approach. The data mining approach is carried out by implementing the fuzzy c-means (FCM) algorithm in order to obtain the optimum number of clusters in the potential clustering of da'wah in the city of Semarang. The data used in this study from the Ministry of Religion of the Republic of Indonesia and the Central Statistics Agency (BPS) of Semarang City. The results of the FCM analysis show that the optimum number of clusters is two clusters, where the sub-district in the second cluster is an area with a high potential for da'wah. This study provides information that in effective da'wah activities, certainty and clarity is needed regarding the targets of da'wah through mapping of da'wah in the form of clustering potential da'wah. This can be a consideration of dakwah strategies for the successful implementation of da'wah studies so that an increase in the target behavior of da'wah can be achieved. The application of FCM to get the optimum cluster of potential da'wah in order to produce da'wah mapping is novelty in the field of Islamic studies, especially the science of da'wah.
Efficient String Matching Based on a Two-Step Simulation of the Suffix Automaton

Automata play a very important role in the design of string matching algorithms as their use has always led to elegant and very efficient solutions in practice. In this paper, we present a new general approach to the exact string matching algorithm based on a non-standard efficient simulation of the suffix automaton of the pattern and give a specific efficient implementation of it. To show the effectiveness of our algorithm, we perform an extensive comparison against the most effective alternatives known in literature in terms of search speed and shift advancements. From our experimental results the new algorithm turns out to be very efficient in practical cases scaling much better when the length of the pattern increases, improving the search speed by nearly 10 times under suitable conditions.
Improving Safety and Efficiency for Navigation in Multiagent Systems

Multiagent system concept can be deployed in many real world applications. A good example is unmanned aerial vehicle (UAV). While UAV deployment in real world has gained more popularity, many areas of research are still needed for more efficient, more useful, safer and more environment-friendly deployment of UAVs. The first and foremost issue is the navigation problem. Here, we investigate how to navigate UAVs through congested 3D sphere towards their individual destinations efficiently and safely. Our approach is based on 3D reciprocal velocity obstacle (3DRVO) and intelligent agent’s Belief-Desire-Intention (BDI) architecture. We represent UAVs by intelligent agents using 3DRVO at low level navigation for safety, and BDI at high level planning for efficiency. The simulation results show that agents can travel safely to their destinations. The results also show that the agents can travel efficiently, i.e., while the number of agents increases, the elapsed time for simulation and the simulation rounds increase in much lower rates.
Software Process Improvement Diagnostic: A Snowballing Systematic Literature Review

Software Process Improvement (SPI) consists of a set of changes in a software development company, introducing new and improved methods, techniques, and tools. We call SPI Diagnostic, the process to know the organization current status. Typically, these SPI Diagnostic processes are manually performed, thus demanding consultant practitioner and a high effort from the organization under analysis. Based on this we propose the following question: “What solutions have been proposed to SPI Diagnostic?” We looked for other Systematic Literature Reviews with the same goal and the found studies do not answer our question. Hence, we performed a Systematic Literature Review (SLR) to investigate solutions to the SPI Diagnostic process. We executed an SLR, based on snowballing technique. This SLR characterizes 14 solutions aiming at systematizing the SPI Diagnostic process through a method, model, or framework. As a result, we advocate that Artificial Intelligence (AI) based solutions should be more explored in research to deal with SPI Diagnostic complexity and, therefore helping the SPI Diagnostic.
CrossNet: Computing-Friendly Lightweight Anchor-Free Detector

Many scenarios such as edge and mobile scenarios are very sensitive to computing complexity and parameter size. Most object detection models cannot be directly deployed without specific modifications. In this paper, we introduce CrossNet, a compute-friendly lightweight anchor-free detector based on CenterNet. We abandon DCN module and design the network structure according to lightweight design principles. A new sample matching strategy based on neighbor points is adopted for improving its performance greatly. Weighted Focal Loss and Balanced L1 Loss further reduce the performance gap between CrossNet and CenterNet. CrossNet possesses a model size of 4.3MB (12.8× smaller than original CenterNet, respectively) and requires 1.51B operations for inference (11.7× smaller than original CenterNet, respectively) while still achieving mAP of 74.2% on VOC dataset. We hope our work could provide some inspiration for lightweight detector design.
Data Analysis for the Hogg and Max Weber Models

By adopting predictive maintenance mode and using EMS Software to analyze and summarize big data, confirm the normal performance range of system parameters, and take maintenance measures in advance to restore the degraded aircraft system to a reasonable range, so as to avoid the impact on flight safety from the aircraft with “disease” to the complete failure of the system. Predictive maintenance greatly reduces the probability of system failure and effectively ensures the safety of flights.
Crossing cuts polygonal puzzles: Models and Solvers

Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping un-ordered fragments, is fundamental to numerous applications, and yet most of the literature has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical sys-tem endowed with hierarchical loop constraints and a layered reconstruction process that is guided by the pictorial content of the pieces. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to indicate that they are solvable completely automatically.
Speech Recognition: A

Abstract Speech recognition can be formulated as the problem of guessing a sequence of words that produces a sequence of sounds. The human brain is remarkably good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that the listeners hear a corrupted version of the speech. Computers are getting much better at speech recognition and voice command systems are now common for smartphones (Siri), automobiles (GPS, music, and climate control), call centers, and dictation systems. In this chapter, we explain the main ideas behind the algorithms for speech recognition and for related applications. The starting point is a model of the random sequence (e.g., words) to be recognized and of how this sequence is related to the observation (e.g., voice). The main model is called a hidden Markov chain . The idea is that the successive parts of speech form a Markov chain and that each word maps randomly to some sounds. The same model is used to decode strings of symbols in communication systems. Section 11.1 is a general discussion of learning. The hidden Markov chain model used in speech recognition and in error decoding is introduced in Sect. 11.2. That section explains the Viterbi algorithm. Section 11.3 discusses expectation maximization and clustering algorithms. Section 11.4 covers learning for hidden Markov chains.
Research on Grammar Checking System Using Computer Big Data and Convolutional Neural Network Constructing Classification Model

Abstract This article proposes an automatic grammatical correction method for typos and word order errors that may occur in Chinese writing by beginners. The thesis first constructs heuristic rules and expands the corpus by analyzing the characteristics of different grammatical error types in the data set. Secondly, when the paper uses classification methods to detect grammatical errors, it extracts sentence-level binary and ternary part-of-speech combinations, n-gram models based on part-of-speech statistics and other three types of features to construct single classification and ensemble classification models, and then use convolutional neural the network constructs classification models from different angles. Finally, when the paper adopts the method based on sequence labeling for grammatical error detection, it mainly uses dependency syntax tree features, and realizes grammatical error detection by constructing a conditional random field model. This method can automatically detect grammatical errors while also identifying the sentence the location of the error. On this basis, the paper implements a simple Chinese grammatical error automatic detection system, which can provide help for the optimization of questions and answers in the question-and-answer system
Precision Inspection of Secondary Cell Electrode Alignment with Camera Image Error Correction Algorithm using Dot Grid

Industrial cameras used in traditional machine vision are subject to errors because of the distortions induced in the images due to the camera lenses and various external factors. To calibrate these errors, a picture of the dot grid was taken and the center of the circle was calculated using the least squares circle-fitting method and the centroid method. In addition, an error map was generated using these methods, which can be used for generating a first-order equation with the least squares method. It is possible to calibrate the object point. In this work, an equation that can correct this error and perform an accurate measurement. It was applied to the stacking equipment of secondary batteries for automobiles to an algorithm that accurately measures the alignment of the cathode and the anode.
Mathematics of the future

Mathematics has entered a crisis of complexity. The main reason is the use of immutable concepts according to the law of identity of Aristotle's logic. The evidence has become super-long, unverifiable. Overcoming the crisis is possible in the transition from immutable concepts to "mobile" ones, in the generalization of the law of identity. In modern mathematics, there are prerequisites for such a transition – in qualitative theory, probability theory, algorithm theory, and foundations. The future of mathematics lies in the development of categories as transformative concepts.
Generative adversarial networks and their variants

Nowadays, generative adversarial networks (GANs) are one of the most exciting approaches in computer science. It is an algorithmic designs that utilize two neural networks, i.e., generator and discriminator, setting one in opposition to the next that's why adversarial to produce new content, synthetic instances of information that can pass for genuine data. This ability of GANs looks a little bit of magic, at least at first sight. The primary objective of GANs is to produce information from the beginning, consider mostly pictures but from other areas which also include music. Most commonly, they are utilized in voice generation, image generation, video generation, and many more. By and large, GANs are model design for preparing a generative model and it is generally necessary to utilize deep-learning models in this design. Some variants of GAN are Vari GAN, Temporal GAN (TGAN), Laplacian GAN (LAPGAN), VGAN, SRGAN, and FCGAN. This chapter provides an introduction to GANs, deep-learning methods with an overview of some variants and applications that have benefited from them.
Usage of Blockchain for Edge Computing

Edge computing introduces a novel computing illustration that enables cloud computing accumulators to be accessible to both the end-users and/or devices. Currently, in edge computing, cloud services and resources are following distributed architecture whereby decentralized service management and preserving security are the main challenges for edge computing. On the other side, Blockchain has made an appearance of decentralized data management framework that maintains an immutable public ledger used to store records of large amounts of transactions securely and efficiently among the involved parties. This facilitates a publicly accessible transaction history that allows participants such as devices and/or humans to access or append the immutable records. Blockchain technology can address the limitations of edge computing because it would ensure greater security and privacy of endpoint edge devices and will make the system more reliable given the threats of cybersecurity. The security, managing resources and scalability issues will be addressed from the perspective of integrating Blockchain with edge computing. This chapter would focus on the capabilities of edge computing and how it can be enhanced when integrated with Blockchain. The chapter covers the introduction, applications and benefits of edge computing, issues in edge computing, integrating Blockchain in edge computing: the missing piece of the puzzle?, challenges of using Blockchain in edge computing, what are the potential solutions and future research direction related to edge computing integrated with Blockchain.
Large-Scale Agile Implementation in Large Financial Institutions: A Systematic Literature Review

Organizations must constantly evolve and adapt to meet customer demands in an ever-changing business environment. The most notable differences have been in the way fintech companies use agile practices to speed up software development and their time to market for new products. This has impacted the way how large financial institutions operate. Large Scale Agile Development (LSAD) is popular, and many studies are being conducted in that domain. However, there is a need to understand the research landscape and the level of theorization on all the studies that where conducted, so that future research opportunities and needs can be identified in this domain. This study aims to synthesize and analyze the research trends and provide a synopsis on the levels of theorization in LSAD research while at the same time also recommending areas for future research in the field of LSAD in the financial sector. Using a systematic literature review protocol, we identified 39 primary studies that focused on LSAD in the financial services sector between 2015 and 2020. The studies were limited to English language only and were sourced from the Google Scholar, IEEE Xplore and ScienceDirect electronic databases. Of all papers reviewed, 14 produced Analysis theories, 9 produced Explanation theories, 3 produced Explanation & Prediction theories, and 7 produced Design & Action theories. Interestingly, none produced Prediction theories. 6 of the papers were systematic literature reviews. The findings indicate that LSAD research in the financial sector has not yet matured in the explanation & prediction theories, especially in the prediction-type theories. This provides an avenue for future research to provide the building blocks for theory development. The focus of future studies should be on building a predictive theory around LSAD Teams in the financial Sector.
The 10th International Workshop on News Recommendation and Analytics (INRA 2022)

A rapidly changing news ecosystem presents new challenges to research, media organizations, consumers, and societies. The 10th edition of the International Workshop on News Recommendation and Analytics (INRA) serves to exchange ideas and discuss recent trends, technological advancements, and open problems concerning news. We welcome contributions in scientific articles, demonstrations, and ideas. We strive to bring together researchers, practitioners, and decision-makers to address crucial challenges. The workshop provides an opportunity to learn about recent research and interactively discuss technical and interdisciplinary aspects related to news. Topics of interest include information access systems for news, advances in natural language processing, multi-modality, mis- and disinformation, trust and user experiences, and personalization.
Transformation in Russian adult additional education

The present work considers the development of adult education in Russia from the elimination of illiteracy from the late 19th -early 20th centuries to the use of information and communication technologies at the beginning of the 21st century. The aim of the article is to consider the stages of development of adult education and to determine at the stages of development the most sought-after educational areas and technologies in additional adult education. In this work, differences in the process of teaching children and adults are considered, an excursion is made into the history of the formation of adult education in Russia. A modern picture of additional adult education is given. Based on the studies, the authors conclude that for adults the most sought-after educational field is the study of a foreign language using remote technologies.
Evaluating the Impact of Cybertheft Through Social Engineering and Network Intrusions

This chapter takes an in-depth look into the research literature to analyze and evaluate the role that social engineering plays in network intrusion and cybertheft. It will also discuss preventive measures and solutions to the threats and vulnerabilities that present themselves in the process of social engineering attacks. Social engineering is a means of stealing private data through tactics that make the victim feel comfortable to give their data. This kind of attack can cost individuals and organizations millions of dollars and block their access to data. The articles present multiple statistics that prove that the risk of social engineering attacks on individuals or organizations has increased tremendously. This new wave of communication has given hackers many opportunities to threaten security by tracking your email, phone, social networks, etc. Information detailing how users can be more aware of ways to protect their private information from attackers will also be presented.
Similarity and Ranking Preserving Deep Hashing for image Retrieval

Hash codes based on deep learning can effectively learn image features. For supervised deep learning methods, the label information of the image can be used to further learn the semantic information of the image. However, the current supervised deep learning methods often use 1 and 0 (or -1) to represent the similarity of two images. In fact, these two extreme values do not fully reflect the similarity between images. Thus, we proposed a novel similarity and ranking preserving deep hashing method (SRPDH). In order to enrich and more comprehensively reflect the semantic information between images, we refine the single-label information into multi-label information, and use Jaccard coefficient model to calculate the similarity between label information. In the loss function model, we use the cross entropy model and consider the loss caused by the binary quantization of the network output. The experimental results show that our method can further improve the mean average precision (MAP) of image retrieval compared with the existing methods.
Oracle Data Privacy Protection System of Virtual Database

With the growth of information explosion, the trend of data sharing and information exchange is gradually becoming obvious, and more and more attention has been paid to privacy. One of the main objectives of database privacy protection research is to protect sensitive information stored in databases from inference by ordinary database-users. In this paper, a framework based on Oracle database is proposed to aid the formal analysis of the database inference problem. The framework is based on association networks, consisting of similarity metrics and Bayesian network models (BNM), and aims to address the database privacy protection problem. First, the similarity analysis of the data is used to distinguish and check similar attributes. Second, the probability dependence of attributes is analyzed. Blocking and aggregation are used to prevent the association of data, and the associated network is used for analysis. The results show that the constructed database privacy protection system can be realized in many aspects such as scope reduction, blocking, aggregation and so on, and can finally ensure the effective protection of database privacy.