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Organizational politics can lead to stigmatization among employees, creating division within an organization. As a result, researchers are interested in studying this topic. This study aims to identify trends and developments in scientific publications related to organizational politics, using 828 international journals in the Web of Science database. The study employs various factorial analysis visualizations, including correspondence analysis, multiple correspondence analysis, and multidimensional scaling analysis. The present study shows that research on organizational politics has increased over time, with a significant focus on perception in the most recent years. Further analysis reveals that perception and performance are the most frequently associated topics with organizational politics. Besides, the three-factor analysis approach highlights the keyword “perception” as having the largest cluster among the three approaches. However, bibliometric analysis of this topic is limited, particularly regarding the use of biblioshiny software as an analytical tool. The findings suggest potential areas for future research, including creativity and employee personality, using the bibliometric method with a time evolution approach.

Book arrangement problems in Indonesia mainly utilize traditional systems. However, it contributes to user frustration and dissatisfaction, leading to difficulties in the retrieval process and hindering book monitoring. Therefore, an efficient arrangement is needed to make it easier for librarians to organize collections at the library. Hence, this study proposes and evaluates Optical Character Recognition (OCR) approaches such as Tesseract-OCR and Efficient and Accurate Scene Text (EAST) by considering various light factors and device applications. An evaluation was performed to determine the effectiveness of the methods for detecting classification numbers using the Character Error Rate (CER) and Word Error Rate (WER). The results obtained can be seen from the lighting and the use of image capture tools that can affect the text detection results. The higher the lighting, the better the text detection results obtained. However, in the extraction tool using a security camera yields a better output than the others. In conclusion, the modernization of book arrangement systems by implementing OCR through security cameras could enhance user experience in the retrieval process.

When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. In this study, microscopic images are used for printed source identification due to their high magnification properties resulting in detailed texture and structure information. Prior research implemented a scanner as a digitizing technique to resolve very fine printed identification, but this technique provided limited information on the resolution and magnification of the sample. In contrast, the performance of microscopy techniques can retrieve the shape and surface texture of a printed document with differing micro structures among printer sources. To explore the relationship between source printers and images obtained by the microscope, the proposed approach utilizes image processing techniques and data exploration methods to calculate many important statistical features, including: Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, the Wiener filter, the Gabor filter, Haralick, and SFTA features. Among the different set of features, the LBP approach achieves the highest identification rate and is significantly superior to other methods. As a result, the proposed technique using microscopic images achieves a high classification accuracy rate, which shows promising applications for real world digital forensics research.

Ancient documents that have undergone physical and visual degradation pose significant challenges in the digital recognition and preservation of information. This research aims to evaluate the effectiveness of ten classic binarization methods, including Otsu, Niblack, Sauvola, and ISODATA, as well as other adaptive methods, in comparison to the U-Net ResNet34 model trained on 256 × 256 image blocks for extracting textual content and separating it from the degraded parts and background of palm leaf manuscripts. We focused on two significant collections, Lontar Terumbalan, with a total of 19 images of Balinese manuscripts from the National Library of Indonesia Collection, and AMADI Lontarset, with a total of 100 images from ICHFR 2016. Results show that the deep learning approach outperforms classical methods in terms of overall evaluation metrics. The U-Net ResNet34 model reached the highest Dice score of 0.986, accuracy of 0.983, SSIM of 0.938, RMSE of 0.143, and PSNR of 17.059. Among the classical methods, ISODATA achieved the best results, with a Dice score of 0.957 and accuracy of 0.933, but still fell short of the deep learning model across most evaluation metrics.

When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. Over the past several years, digital forensics for printed document source identification has begun to be increasingly important which can be related to the investigation and prosecution of many types of crimes. Unlike invasive forensic approach which requires a fraction of the printed document as the specimen for verification, noninvasive forensic technique uses the optical mechanism to explore the relationship between the scanned images and the source printer. To explore the relationship between source printers and images obtained by the scanner, the proposed decision-theoretical approach utilizes image processing techniques and data exploration methods to calculate many important statistical features, including: Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, the Wiener filter, the Gabor filter, Haralick, and SFTA features. Consequently, the proposed aggregation method intensively applies the extracted features and decision-fusion model of feature selections for classification. In addition, the impact of different paper texture or paper color for printed sources identification is also investigated. In the meantime, the up-to-date techniques based on deep learning system is developed by Convolutional Neural Networks (CNNs) which can learn the features automatically to solve the complex image classification problem. Both systems have been compared and the experimental results indicate that the proposed system achieve the overall best accuracy prediction for image and text input and is superior to the existing approaches. In brief, the proposed decision-theoretical model can be very efficiently implemented for real world digital forensic applications.

Recently, digital forensics, which involves the collection and analysis of the origin digital device, has become an important issue. Digital content can play a crucial role in identifying the source device, such as serve as evidence in court. To achieve this goal, we use different texture feature extraction methods such as graylevel co-occurrence matrix (GLCM) and discrete wavelet transform (DWT), to analyze the Chinese printed source in order to find the impact of different output devices. Furthermore, we also explore the optimum feature subset by using feature selection techniques and use support vector machine (SVM) to identify the source model of the documents. The average experimental results attain a 98.64 % identification rate which is significantly superior to the existing known method of GLCM by 1.27 %. The superior testing performance demonstrates that the proposed identification method is very useful for source laser printer identification.

Evaluating information quality within these applications becomes paramount to ensure student satisfaction, involving assessments of completeness, relevance, and other factors. To address this concern, the study employs the quality of information system (QIS) model, focusing on characteristics that enhance the value of information. An analysis, grounded in time, content, and form dimensions, offers a comprehensive and quantitative understanding of the current state of information quality in e-learning applications such as Brainly. Consequently, many students in various regions, particularly Madiun, expressed satisfaction with the time, content, and form of the information provided by Brainly, resulting in an information quality score of 81%. This study contributes to the dimensions of time and form within the Brainly application. Emphasizing these dimensions yields valuable insights into the application's advantages from the users' perspective and provides recommendations for enhancing the Brainly application's capacity to deliver timely information and offer a user-friendly experience.

The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features. Each underlying facial bone exhibits unique characteristics essential to the face's physical structure that could be exploited for identification. Therefore, we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches. Using our proposed approach, we were able to achieve an accuracy of 92.3–99.5% in the classification of human skulls with mandibles and an accuracy of 91.4–99.9% in the classification of human skills without mandibles. Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

Wavelet tree based watermarking algorithms are generally using the energy difference among grouped wavelet coefficients for invisible watermark embedding and extraction. According to cryptanalysis of wavelet tree quantization (WTQ) scheme, the robustness of watermarking is weak if the wavelet tree group coefficients are only unilaterally modulated. Therefore, bilaterally modulated techniques like modified wavelet tree quantization (MWTQ) and wavelet tree group modulation (WTGM) improve the security since the attackers can not decipher how tree coefficients are modulated. However, MWTQ needs the wavelet tree group information as the extra information which results the method is not purely blind for watermark extraction. For that matter, a novel wavelet tree group based watermarking using energy modulation and consistency check (WW-EMCC) is proposed in this study which not only resists the cryptanalysis attacks but also provides the dual function of choices for blind (WW-EMCC B ) and non-blind (WW-EMCC N ) watermark embedding. The essence of WW-EMCC design is to embed the watermark in the tree group coefficients as well as the relationship between the tree groups. Such approach extends the bilateral modulation into higher dimension of modulation and increase the robustness of security. In addition, WW-EMCC can even be modified as a captioning watermarking with lossless image quality which integrates watermarking and cryptography for copyright protection. This study has performed intensive comparison for the proposed scheme with WTQ, MWTQ and WTGM under various geometric and nongeometric attacks. The experimental results demonstrate that the proposed technique yields better performance with higher degree of robustness.

With the widespread use of the Internet and the rapid development of digital technologies, copyright protection of multimedia content has become an important issue. Among the available technologies, digital watermarking techniques are regarded as a solution to the property right protection for multimedia resources. To evaluate the performance of a visible watermarking technique, robustness and perceptual translucence are two essential criteria for the watermark applications. In order to get the best trade-off between the embedding energy of a watermark and perceptual translucence, this study presents a technique named ICOCOA (innovated content and contrast aware) by exploiting the contrast sensitivity function (CSF) and noise reduction of human vision system in the wavelet domain. Another novel idea of this work is to propose the innovated CSF masking (I-CSF) curve which provides better weight perception where a game-theoretic architecture can be leveraged to determine the best I-CSF masking for the watermarked image. The experimental results demonstrate that the proposed approach not only provides a good translucent quality of the watermark but also achieves the robustness against the common image processing operations.