Reviewerly/exHarmony huggingface dataset:
Facilitating the discovery of diverse content: some notes from Sotheby's Institute of Art

When the diversification of collections is a priority for a library, making diverse content in existing holdings more discoverable is a useful complement to the acquisition of new resources. Artists and authors from diverse groups, and material written from diverse perspectives, can be made more visible to patrons, either through enhancements to catalogue records or the creation of standalone indexes focussed on those groups. With the conscious, ethical diversification of collections a relatively new enterprise for many libraries, certain aspects of how best to proceed are not yet clear, meaning librarians need to make time to reflect on, and review, the methods they are using to improve discoverability.
The Alternative Scenarios for the Digital Future

The aim of the article is to study the impact of digitalization on economic, sociocultural and political processes in society, as well as to search for alternative scenarios for the development of digital society. Digitalization today is an effective indicator of the development of society, which provides many people with ease in carrying out their daily activities. The usefulness of digitalization for society today is confirmed in almost all aspects of human life – from production and doing business to communication and mobility. However, in today's society there is no clear understanding of the further development of humanity in the context of rapid technological advances, and there is even more lack of an idea of what the role of a person in the digital world will be. Analyzing this issue, we have worked out a number of versions and views – from scientifically progressive to fantastic-utopian – in order to understand the optimal scenario for the development of digital society. Today, the world has entered the era of Industry 4.0, that is, the era that is based on new technologies. Technologies are increasingly becoming able to replace humans in every field of industry, education, science, and everyday life issues of society. The study showed that today there is no consensus in society about what the future world will look like under the influence of digitalization and the spread of artificial intelligence (AI). This raises the question of the role of man in the light of technological change. In order to find the optimal scenario for the development of humanity in the era of digitalization and the formation of AI, we analyzed the main views that prevail among scientists and in society in general, and singled out our own vision of the role of humans in the era of digitalization. The fundamentals of the theory of technological singularity and transhumanism are analyzed. It is found that the negative consequences of technological progress can be generalized as two major problems. The first is that humans will lose control over technology, because AI will begin to develop on its own so quickly that it will become incomprehensible to humans. The second is a change in global humanistic concepts, at the center of which are a person and the appropriate moral values.
Perancangan UI/UX Website International Office Universitas Muhammadiyah Sukabumi Dengan Mengunakan Pendekatan User Persona

Information is a result of processing data and facts that can be understood and conveyed to describe a situation or condition. At this time the information is important and much needed in various fields. Information is very useful for describing a condition, such as in the world of cooperation, information is needed to give an idea of the partners who will collaborate. The International Office is an information website managed by the Institute for International Cooperation and Relations (LKHI) to provide various information about the Muhammadiyah University of Sukabumi (UMMI) to all collaborating partners. In addition to providing information to partners, besides that the International Office is also useful for providing information on international activities that can be followed by students and making it easier for lecturers to find information on activity data. After several years of operation, the International Office has experienced a decline in users as a result of which the website does not match the interests and needs of current users. This study aims to increase user interest and interest in the website by understanding the characteristics and needs of users in more depth using the User Persona approach. Where from understanding with the User Persona approach, researchers can understand more deeply the needs and desires of users for websites which are then used as a basis for redesigning the User Interface (UI) and User Experience (UX) to provide an overview of the implementation of the characteristics and needs of users. After the UI/UX redesign in accordance with the understanding of the interests that users want, this research results in a more attractive website appearance resulting in an increase of 21% interest and 18% preference for appearance and provides an overview of implementation to make it easier for developers when making changes to the site web.
Implementation of C4.5 and K-Nearest Neighbor to Predict Palm Oil Fruit Production on Local Plantations

The agricultural sector, especially oil palm plantations, has an important role in the Indonesian economy. Oil palm farming has increased the welfare of farmers and provided employment. In this study, researchers used the C4.5 and K-Nearest Neighbor algorithms in the RapidMiner application to analyze oil palm fruit production data after going through the data preprocessing process. The C4.5 algorithm produces a decision tree with an accuracy of 94.12%, while the KNN algorithm achieves an accuracy of 82.53%. Based on these results, it can be concluded that the C4.5 algorithm has a higher accuracy in classifying oil palm fruit production based on existing attributes.
Use of Instagram to Increase the Outcome in Learning the Reaction Rate in Chemistry Subject

The reaction rate is one of the abstract chemical materials so it requires visualization that can describe the reaction rate events in a more realistic way so that it is more easily understood by students. Visualization can help students be able to observe the symptoms that occur, collect data and draw conclusions from the videos or images presented and improve student learning outcomes. Social media such as Instagram is very possible to be applied in the chemistry learning process which requires visualization to explain some chemistry concepts like reaction rate. The use of Instagram can help students observe the symptoms that occur in the reaction rate through the videos or images presented. Instagram is considered very appropriate to be used to convey learning material that requires visualization to increase students' understanding. The research method used was quasi-experimental with the use of experimental class and control class and shaped Posttest-Only Design. Based on the results of the study, the learning outcomes in the cognitive domain average value of the experimental class was 80.83 and the average value of the control class was 73.83 with increased learning outcomes N-gain of 0.26. Assessment of learning outcomes in the affective and psychomotor domains with the use of final score on a scale of 1 to 4. Calculation of the final score uses the score comparison formula obtained with the maximum score multiplied by the maximum score. In the present study, a final score with an average affective and psychomotor score of 3.21 and 3.04 was obtained respectively.
Extended Reality in End of Arm Tooling Development and Assembly

The emerging technologies presented by the fourth industrial revolution present new opportunities across the entire industrial sector. Among those paradigms that have seen rapid and significant growth in recent years are Virtual Reality (VR) and Augmented Reality (AR), collectively known as Extended Reality (XR). These technologies provide a wide range of unexplored solutions across the manufacturing industry, that promise to improve the effectiveness and speed of various existing processes. One such activity that could potentially stand to benefit from XR is the project and development of End of Arm Tooling (EOAT) for robot applications.The initial purpose of this work is to first study the XR technologies, their current applications across various industries, and the hardware and software involved in their use and development. Then, the main goal is to develop XR solutions to aid in the project, development, and assembly of EOAT systems for extracting injected parts from a mould. Checking in with industry professionals throughout the development of a VR application, the functionalities were tailored to their needs.
Target Search for Joint Local and High-Level Semantic Information Based on Image Preprocessing Enhancement in Indoor Low-Light Environments

In indoor low-light environments, the lack of light makes the captured images often suffer from quality degradation problems, including missing features in dark areas, noise interference, low brightness, and low contrast. Therefore, the feature extraction algorithms are unable to extract the feature information contained in the images accurately, thereby hindering the subsequent target search task in this environment and making it difficult to determine the location information of the target. Aiming at this problem, a joint local and high-level semantic information (JLHS) target search method is proposed based on joint bilateral filtering and camera response model (JBCRM) image preprocessing enhancement. The JBCRM method improves the image quality by highlighting the dark region features and removing the noise interference in order to solve the problem of the difficult extraction of feature points in low-light images, thus providing better visual data for subsequent target search tasks. The JLHS method increases the feature matching accuracy between the target image and the offline database image by combining local and high-level semantic information to characterize the image content, thereby boosting the accuracy of the target search. Experiments show that, compared with the existing image-enhancement methods, the PSNR of the JBCRM method is increased by 34.24% at the highest and 2.61% at the lowest. The SSIM increased by 63.64% at most and increased by 12.50% at least. The Laplacian operator increased by 54.47% at most and 3.49% at least. When the mainstream feature extraction techniques, SIFT, ORB, AKAZE, and BRISK, are utilized, the number of feature points in the JBCRM-enhanced images are improved by a minimum of 20.51% and a maximum of 303.44% over the original low-light images. Compared with other target search methods, the average search error of the JLHS method is only 9.8 cm, which is 91.90% lower than the histogram-based search method. Meanwhile, the average search error is reduced by 18.33% compared to the VGG16-based target search method. As a result, the method proposed in this paper significantly improves the accuracy of the target search in low-light environments, thus broadening the application scenarios of target search in indoor environments, and providing an effective solution for accurately determining the location of the target in geospatial space.
The Preservation of Digital Evidences Through Blockchain Technology

Blockchain technology uses a decentralized approach for the preservation of the data as evidence. The authenticity and integrity of an evidence has always been considered as a burden over the functioning of judicial system worldwide. Blockchain with its introduction into the field other than crypto currency management as a technically developed and widespread application can help in the quick settlement of suits and may range from establishing a storage facility for the preservation of important documents as well as can also supersede judges and experts. This paper discusses how Blockchain differs from the present evidentiary system in terms of protecting the integrity and validity of electronic evidence. It also emphasizes possible issues that can arise as a result of Blockchain-induced changes to the laws governing electronic evidence. While blockchain technology may modify the conventions of proof, it also comes with essential disadvantages. Furthermore, its handling of electronic evidence may suggest a combined centralized judicial mechanism between traditional and decentralized technology.
MENGIDENTIFIKASI PERSEPSI MASYARAKAT PEMULUNG LUBUK MINTURUN KECAMATAN KOTO TANGAH TERHADAP PENDIDIKAN FORMAL WAJIB BELAJAR 12 TAHUN

This study aims to determine the perceptions of scavengers towards formal 12-year compulsory education. This case study is at the Lubuk Minturun TPA. This research belongs to the type of qualitative descriptive research. Data collection techniques in this study are observation, interviews, and documentation. As well as data processing using the stages of problem formulation, data collection, data analysis, and report writing. The results of this study indicate that the Lubuk Minturun TPA scavenger community has a negative perception of the 12-year compulsory formal education. It has been found through the results of research that many scavengers do not send their children to school because there is no money. They prefer their children to work to make money. The obstacle faced by scavengers is limited income, which makes it difficult to support education for their children who are 12 years of school.
Educational Innovation Using Augmented Reality: Systematic Literature Review

Technology is becoming increasingly prevalent in the creation of educational innovations. The objective of this study is to review the existing literature and research on educational innovation using augmented reality. Systematic literature evaluations were performed in accordance with PRISMA's guidelines for conducting and reporting systematic reviews. A total of 25 peer-reviewed articles were included after a systematic search of the Scopus database. Included in the research agendas are annual publications, multilevel, publication sources, disciplines of study, and geographical context. The categorization of educational innovation using augmented reality is performed in order to comprehend the direction and emphasis of research in this field.
Machine Learning-Based Object Detection Using Raspberry Pi

The power system is operating steadily which is impacted by the dependability of electrical components. Historically, electrical components have a crucial concern in the intelligent power systems. Low spotting accuracy and subpar real-time performance, which make it difficult to extract critical information from the scrutiny images, are the fundamental issues with traditional techniques of electrical component recognition. This article suggests a method for detecting electrical components based on a parallel Raspberry Pi CPU (ARM) and a machine learning algorithm. The available dataset was decreased as a result of several of the inspection photographs being unclear. In order to perform reconstruction with high resolution of the hazy image and expand the dataset, we use convolutional neural network with super resolution (SRCNN). We evaluate the performance of the suggested strategy in comparison with other well-liked recognition techniques. The experiment’s findings support the usefulness of the suggested approach and the systems. It is possible to attain good resilience, high recognition accuracy, and powerful real-time performance.
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction

Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robust prediction, this problem can be decomposed into three sub-tasks: depth-based image warping, panorama inpainting and high-dynamic-range (HDR) reconstruction, among which the success of panorama inpainting plays a key role. Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama. However, they usually achieve suboptimal performance since the missing contents occupy a very large portion in the panoramic space while CNNs are plagued by limited receptive fields. The spatially-varying distortion in the spherical signals further increases the difficulty for conventional CNNs. To address these issues, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local-to-global strategy</i> for large-scale panorama inpainting. In our method, a depth-guided local inpainting is first applied on the warped panorama to fill small but dense holes. Then, a transformer-based network, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PanoTransformer</i> , is designed to hallucinate reasonable global structures in the large holes. To avoid distortion, we further employ cubemap projection in our design of PanoTransformer. The high-quality panorama recovered at any locale helps us to capture spatially-varying indoor illumination with physically-plausible global structures and fine details.
Using Ensemble Method to Detect Attacks in the Recommender System

Shill attacks are a serious threat to the stability of filtering and recommendation systems. These attacks involve the injection of fake profiles into the system, which can compromise the reliability of system output. Several shilling attack detection techniques have been proposed, but they often have limitations in terms of accuracy. This works presents a enhanced method for detecting attacks in collaborative recommender systems. The proposed method is based on a combination of statistical and machine learning techniques. The statistical techniques are used to identify anomalous user behavior, while the machine learning techniques are used to classify users as either malicious or benign. The main contribution of the proposed method is the use of a hybrid approach that combines the strengths of statistical and machine learning techniques. The statistical techniques are able to identify anomalous user behavior that is not easily detected by machine learning techniques. The machine learning techniques are able to classify users as either malicious or benign with a high degree of accuracy. The proposed method was evaluated on a real-world dataset. The results showed that the proposed method was able to detect attacks with a high degree of accuracy. The proposed method uses a combination of ensemble learning and feature selection to achieve better accuracy than previous methods. The results of the experiments show that the proposed method can achieve an accuracy of up to 99%.
Features based reconstruction technique for torn documents

The reconstruction of torn documents that hold sensitive information or crucial historical data is of great significance in many domains. An efficient technique for hand-torn document restoration is presented in this paper. Pre-processing, feature extraction, and matching three main phases of this technique. The reconstruction is carried out by matching pairwise the feature vectors of the fragments. Using a database consists of 120 torn pages taken from books, newspapers and magazines, the performance of this technique has been measured using simple but effective criteria called error rate. The algorithm was able to accurately reconstruct 91% of the documents, with the remaining being partially reconstructed.
Cryptographic encryption techniques in a password manager

Today, we have a plethora of email accounts, cloud storage, banks, blogs, forums, e-shopping, and other services at our disposal. We have problems choosing passwords for several web accounts. A criminal can acquire access to another user&apos;s services, including his bank password, mail, and internet shopping credentials if you&apos;re using the password for all of these accounts. By utilizing multiple passwords across different systems, users may use passwords that are easy to remember or weak, increasing the likelihood of users forgetting their passwords and raising user support related to detailed performance and resetting your password. To address this problem, we&apos;re working on developing a safe password manager that can manage many user accounts and passwords without putting people under the strain of remembering multiple accounts and passwords. User data contained in the password manager is stored locally on the hard drive / SSD, ensuring great usability, maximum security, and convenience of use for end users.
Artificial intelligence-based human gesture tracking control techniques of Tello EDU Quadrotor Drone

This paper comprehensively reviews significant research on various artificial intelligence-based human gesture tracking techniques for the Tello EDU quadrotor drone. The gestures derived from image acquisition techniques include hand, eye, face, and body. Further, the methods include signal acquisition through leap motion and an electroencephalogram. The framework for developing the algorithm with various gestures is also demonstrated. The review table presents a thorough overview of the studies linked to the various human gesture-based techniques. It encompasses details such as the algorithm type, quantity of poses and landmarks, programming language, hardware and framework employed, and validation particulars. Furthermore, it comprehensively analyzes potential areas for future research and improvements within this field.
RESEARCH AND DESIGN OF A PATH PLANNING USING AN IMPROVED RRT* ALGORITHM FOR AN AUTONOMOUS MOBILE ROBOT

Path planning is innately viewed as a multi-objective optimization problem (MOOP) of the shortest path and the smallest distance to collision-free obstacles. The position of the mobile robot is definitively identified based on data derived from constructing maps with SLAM or extracting data from camera frames. Due to the complexity of the surroundings, obstacle avoidance still requires a complex sensor system with a high processing efficiency demand. The study provides an improved RRT* algorithm for mobile robot path planning in a given environment. RRT* will optimize the number of grid nodes based on the optimal cost functions of nodes in the searching environment. The improved RRT* algorithm with a safety cost increases the efficacy of obstacle avoidance. Hence, an improved RRT* algorithm based on the traditional RRT algorithm will remove redundant path nodes. Furthermore, the third-degree B-spline curve will smooth the path while ensuring MOOPs. Last but not least, simulations and experiments are shown to demonstrate the effectiveness of the suggested strategy.
Web Archiving and Digital Libraries (WADL) 2023

The 2023 edition of the Workshop on Web Archiving and Digital Libraries (WADL) will explore the integration of web archiving and digital libraries. The workshop aims at addressing aspects covering the entire life cycle of digital resources, including creation/authoring, uploading/publishing, crawling, indexing, exploration, and archiving. It will also explore areas such as archiving processes and tools for “non-traditional” resources such as social media, scholarly and government datasets, 3D objects, and digital online art.
Data leakage analysis in wireless networks using Super-vised and Unsupervised Testing

Due to an increasing number of wireless spectrums, the multiple frequencies are tangling resource management process that results hindrance in operation. In addition, the previous data become vulnerable when reports are received for data leakage enigma. In this situation, it is indispensable to secure the data in the dataset and detect the actual amount of data during resource transformation mechanism in wireless networks. A system as been developed to detect the leaked data using supervised and unsupervised testing technique by conducting simulation in Python. The targeted and actual outcome is obtained which deduced through supervised and undersized testing, the outcome remained 96.03%, and 94.53% respectively.