Reviewerly/exHarmony huggingface dataset:
Clustering Pasien Yang Layak Donor Darah Dengan Algoritma K-Means Studi Kasus Pmi Kota Medan

Adequate supply of blood is important in health services, especially in emergency situations. In an effort to increase the efficiency and effectiveness of the selection process for prospective blood donors, this study aims to apply the K-Means algorithm in clustering patients who are eligible to become blood donors at PMI Medan City. Patient data including age, weight, hemoglobin level and blood pressure were obtained from UDD PMI Medan City. The K-Means algorithm is then applied to the data to form groups based on patient characteristics. The results of the study show that the K-Means algorithm is able to classify patients into groups based on their characteristics efficiently, and can provide guidance to PMI Medan City in selecting potential blood donors and improving the collection and distribution of blood supplies in a more targeted manner
Rethinking Similar Object Interference in Single Object Tracking

Similar object interference (SOI) problem challenges the single object tracking (SOT) task, leading to the failure of feature-based trackers and subsequent performance degradation. Unfortunately, current generic SOT benchmarks do not effectively tackle this critical challenge, while popular SOT algorithms consistently underestimate the influence they have on tracking performance. To bridge this gap and further enhance the investigation of similar object interference in SOT, we adopt the following viewpoints: (1) By examining the operational principles of mainstream trackers and their performance on representative SOT datasets, we redefine similar objects, taking into account the cognitive bias that exists between trackers and humans when dealing with this challenge. (2) Subsequently, we develop a mining methodology that enables the extraction of the SOI sub-dataset from SOT datasets without relying on human intervention. This methodology comprises two main components: determining the SOI challenge and screening the SOI sequences. The SOI dataset is acquired from representative SOT dataset using our proposed approach, known as SOI2023. This dataset serves as an ideal environment to facilitate the investigation of challenges related to similar object interference. (3) Additionally, we conduct extensive tracking experiments with 20 typical trackers and their variants on SOI2023 and analyze their performance for similar object interference scenes in several dimensions. The experimental results demonstrate the effectiveness of our proposed mining method, while revealing the strengths and weaknesses of current trackers when faced with the challenge of similar object interference. We hope this work can provide inspiration to the tracking community and also provide support and insights for robust tracking under the SOI challenge.
Research on dynamic management of convergence media ecology node based on blockchain

At present, the media industry is in the rapid development of convergence media, and is undergoing the development process of gradually evolving from full coverage of new media to full integration of media. Blockchain is a distributed database technology, with decentralized, immutable, traceable and other characteristics. The convergence media ecology built on blockchain technology can delegate power to ordinary nodes in the ecology, which is expected to have a profound impact and change on the existing convergence media business model, content creation method and industry structure. Based on the special attributes of the media industry, the blockchain convergence media ecology uses the form of alliance chain in the early stage of establishment. The existing blockchain node management mechanism is universal and does not adapt to the characteristics of the media industry. In order to ensure the basic operation and maintenance of the blockchain convergence media ecology, this paper defines the participation and credit degree system of the ecological node. On this basis, a Node dynamic access and exit (NDAE) operation and maintenance mechanism based on node interaction and node integrity was established. Simulation experiments show that the NDAE mechanism can better handle the access, exit and forced exit of nodes, which ensures the security of the blockchain convergence media ecology to a certain extent.
Image Segmentation of the Cell Nuclei Staining Using U-net and Segment Anything Model

Cell nuclei examination plays a pivotal role in biological research due to their primary responsibility for housing genetic information, as well as providing a major landmark for identifying a cell. This study introduces an automated approach for studying cell nuclei morphology, leveraging machine learning models to identify their forms and yield insights for further investigation. The principal challenge addressed herein is the time-intensive requirement of manually labeling raw images in the training set. Our proposed solution employs Meta's recently developed segment-anything model (SAM) to expedite this process. The SAM model offers several auto-segmentation methodologies, including full-automatic segmentation and point-as-input segmentation. Our experiments indicate that the point selection mode offers the most promising results. Its accuracy matches human labeling and significantly reduces the time investment and presents scope for enhancement through modification specific to biomedical image processing. By adopting the SAM model to the biology domain, this work opens avenues for further automation in cell nuclei study with potential generalization capabilities.
Penerapan Metode Two Sided Side Match Untuk Penyisipan Gambar Pada File Audio

Message data security at this time is very important to do security techniques, because of the increasing number of users of communication media technology that is increasing so that other parties can find out the contents of the message. Steganography technique is a technique that can be used to insert picture messages in audio files. The Two Sided Side Match method is a Steganography method that uses two sides of the pixel value, namely the top side and the top right side. To determine how many binary images to insert into a pixel. This Steganography method is also the art of hiding secret messages with a technique that does not arouse the suspicion of the other party, so that the message can only be known by the sender and recipient of the message which was built using the Microsoft Visual Studio 2010 programming language. important messages or secret messages because the steganography technique hides important messages on certain objects. In this very efficient steganography technique, it is used to hide messages without reducing the quality of the inserted message object
Behavior Mining of Robot-Animal Mixed Swarm

Emergent intelligence and collective behaviors have long captivated the interest of researchers. These phenomena often stem from intricate interactions among numerous individuals adhering to straightforward rules. This paper focuses on a mixed swarm consisting of both controllable and uncontrollable swarms. We aim to harness the controllable group's behavior to influence and intervene in the uncontrollable group's actions. Here the controllable group is a robot swarm that can be controlled by human, while the uncontrollable group primarily comprise biological swarms. Designing behavioral rules for the controllable group to effectively affect the behavior of the uncontrolled group presents a challenge. To address this issue, we propose a behavior-mining-based approach. By utilizing the NEAT algorithm through iterative processes, we generate a multitude of potential behavioral rules for the controllable group. Subsequently, we gather data on how the uncontrollable group responds to the actions of the controllable group. We then apply dimensionality reduction and clustering techniques to extract distinctive behavioral trajectory features for the uncontrollable group (method features). Ultimately, through a series of simulation experiments, we validate the effectiveness of this approach.
Exploiting Paraphrasers and Inverse Paraphrasers: A Novel Approach to Enhance English Writing Fluency through Improved Style Transfer Training Data

In the realm of enhancing English writing fluency, the scarcity of high-quality training data has perennially posed a significant challenge. Moreover, elevating the fluency of writing while ensuring the preservation of semantic integrity compounds the intricacies of this task. In this study, we introduce and implement a style converter rooted in the Paraphraser and Inverse Paraphraser methodologies, aimed at ameliorating English writing fluency. Concurrently, this converter facilitated the generation of a voluminous corpus of synthetic training data. Utilizing this data, we fine-tuned GPT-2 to forge an English text style transfer model. Remarkably, despite our model being trained on a dataset substantially smaller than that of prevailing baseline methods, it exhibited exemplary performance across multiple evaluation metrics, even surpassing these baselines on certain pivotal indices. These findings corroborate the efficacy of our approach and underscore its immense potential in the domain of English writing fluency enhancement. This investigation not only offers a novel optimization strategy for English composition but also furnishes researchers in cognate fields with fresh research perspectives and methodologies.
A Comparative Study of Indoor Place Recognition Using Multi-modal Range Information

This paper presents a discussion on the challenges encountered in localizing mobile robots in large-scale indoor environments, particularly the issues of kidnapping and localization failure, which are prevalent in factories and malls. Visual place recognition (VPR) methods are often used to alleviate these issues; however, in large-scale indoor settings, obtaining ground truth through precise 3D reconstruction using cameras can be time-consuming and challenging due to their limited field of view and varying illumination conditions. LiDAR is more suitable for large-scale mapping due to its wider field of view and its robustness to environmental changes and sensor drifts; however, the cost of LiDAR systems can be prohibitive for indoor applications. RGB-D cameras are lighter and less expensive, making them more suitable for a wide range of applications on multi-robot platforms. Therefore, it is important to investigate the use of RGB-D camera data for place recognition, especially in indoor contexts where only LiDAR maps can be used for place recognition. This paper presents a rigorous comparative study based on existing techniques to identify the key factors in addressing indoor place recognition challenges.
Single-Camera 3D Human Pose Estimation: Addressing Occlusion Challenges and Predictive Quality Assessment

Human Pose Estimation, or HPE, pertains to the process of precisely determining the spatial coordinates of distinct anatomical segments of the human body depicted within an image frame. This challenge has garnered significant attention over the last decade and is poised to remain a focal point in the future. Beyond its significance within the scientific community, this field holds practical implications for applications such as augmented reality, military operations, and surveillance of densely populated areas. The present study concentrates on the intricacies of 3D HPE using a single 2D camera. The exploration encompasses the accessibility of data, prevailing metrics pertinent to this endeavour, and the complications introduced by occlusion. A specific focus is placed on introducing a methodology to evaluate the accuracy of predictions for visible and occluded key points in scenes characterized by corresponding visibility conditions. Promising outcomes are obtained, as the accuracy of predictions for visible key points remains consistent even in the presence of occlusion affecting other key points. Furthermore, a prospective avenue of research is proposed, involving the identification of occluded key points through the analysis of high-frequency noise incorporated into joint positions over temporal intervals. To conclude, an analysis is provided concerning both the challenges and opportunities inherent in this research endeavour.
Penerapan Algoritma Apriori Dalam Penjualan Produk

Micro, small and medium enterprises (UMKM) in the shopping sector have great potential to meet the needs of people in the middle to lower and upper economic categories. How ever, small shopsoften experience problems in managing the supply of goods or materials needed by customers. This can lead to a lack of goods that customers want, and ultimately disappoint them. There fore, inventory control is very important in maintaining customer satisfaction and business continuity small shops. To help overcome this problem, research can be carried out using the Apriori algorithm. This algorithm can help identify the most sold products, so small shops can manage optimal inventory. Thus, small shops can meet customer needs effectively and efficiently, and avoid product shortages that can interfere with customer satisfaction. In research, several variables are used to collect data, such as transaction date,product name, and the amount of sales/purchases. This data can then be analyzed using the Apriori algorithm, so that it can be concluded that final result of the confidence or 2 itemset is that if you buy sugar, you will buy rice and if you buy eggs, will you buy rice
Image Segmentation with Vision-Language Models

Image segmentation traditionally relies on predefined object classes, which can pose challenges when accommodating new categories or complex queries, often necessitating model retraining. Relying solely on visual information for segmentation heavily depends on annotated samples, and as the number of unknown classes increases, the model's segmentation performance experiences significant declines. To address these challenges, this paper introduces ViLaSeg, an innovative image segmentation model that generates binary segmentation maps for query images based on either free-text prompts or support images. Our model capitalizes on text prompts to establish comprehensive contextual logical relationships, while visual prompts harness the power of the GroupViT encoder to capture local features of multiple objects, enhancing segmentation precision. By employing selective attention and facilitating cross-modal interactions, our model seamlessly fuses image and text features, further refined by a transformer-based decoder designed for dense prediction tasks. ViLaSeg excels across a spectrum of segmentation tasks, including referring expression, zero-shot, and one-shot segmentation, surpassing prior state-of-the-art approaches.
THE APPLICATION OF THE 't' TEST AND MARKOV CHAIN ANALYSIS IN BIOSTRATIGRAPHY OF 'X' WELL

There is no habit in biostratigraphers to apply the statistic methods to solve problems in their jobs, whereas in fact these methods are very useful when conventional ways failed. The use of this method has become very important in reservoir scale where microfossil content is low and indicator species is rare or absent. Applying biozonation in reservoir scale is much less reliable since reservoir layers are mostly below zonal resolution, so that the application of high resolution biostratigraphy is needed. In this case, the integration of biometric study and 't' or 'f' test can be used to define bioevent precisely. In sequence stratigraphy, data of depositional environment in various system tracts that determined using microfossil assemblage are very important. However, for the reasons of barren or no samples, this information might not be obtained by biostratigraphy or other methods. Due to the geological cycle and repeatable nature of depositional sequence, Markov Chain analysis can be used to predict the lost information about environment of deposition.
LDSeq: Latent Diffusion Models for Sequence to Sequence Text Generation

Diffusion models have demonstrated remarkable success in generating continuous data, such as images and audios. Previous studies on text generation employing continuous diffusion models have revealed the potential of the diffusion framework. However, challenges like embedding collapse persist, limiting the overall generation performance. In this paper we introduce LDSeq, a latent diffusion framework employing a two-stage training procedure for sequence-to-sequence text generation. In the proposed framework, we first train a Variational Auto-Encoder (VAE) on downstream datasets to compress the target text of samples into a continuous latent space, and then we train a conditional latent diffusion model in the fixed continuous latent space, where the latent vectors are iteratively sampled conditioned on the input source text. The disjoint training stages prevent the collapse of diffusion space. Experimental results on paraphrase generation and text summarization datasets show that LDSeq achieves comparable or superior performance in comparison to AR and NAR baselines while requiring lower training cost. Furthermore, We discuss some potential future directions for enhancing diffusion models in the text generation domain.
Modifikasi Pembangkit Kunci Algoritma Vigenere Cipher Berdasarkan Pembangkit Bilangan Acak CSPRNG Untuk Mengamankan Citra Digital

The distribution of digital images in the current digital era is very rapid. The distribution of digital images has become a habit for every person every day, some are for public consumption and some are confidential. We can see the distribution of digital images in the use of social media, online shops, offices and government. Of course, everyone who distributes digital images does not want to lose the meaning of the digital image because people do bad work, or the digital image is misused by parties who do not have authority over the image, so it needs to be secured. Security issues have been an issue that has been discussed endlessly since the past until now. Information that is confidential, sensitive or of high value is kept secure so that it cannot be accessed by people who do not have authority over the information. Likewise with digital images, they must be safe from people who do not have authority over the image. Digital images can be secured using cryptographic algorithms. This research discusses how to modify the Vigenere cipher with a CSPRNG key generator based on the RSA algorithm. The Vigenere cipher is a classic cryptographic algorithm that utilizes key length as a strength to secure data and information. The longer the key applied, the more secure the encryption, and long keys must be random and unique. In this research, the key that will be used is a CSPRNG random number generator based on the RSA algorithm. The random numbers generated by this algorithm are believed to be very difficult for attackers to compute. By applying this algorithm to encrypt digital images, it is believed to be able to secure digital images, the pixel pattern in the image will be as random as possible. The transmitted digital image will be distributed in a random pixel pattern and will appear useless to attackers. The application of this algorithm is believed to be able to maintain digital image security.
Perancangan Aplikasi Kata Lintas: Teka-teki Silang Dengan Menerapkan Aplikasi Fisher Yates

Crossword puzzle is a word game which is usually in the form of a series of empty spaces in the form of black and white squares, the objective of this game is to fill the white boxes with letters, form certain words or phrases, by completing clues that lead to to a specific answer. The type of crossword puzzle game is currently developing, one of which is a cross word crossword puzzle, in this game the collection of letters is random and the user will look for a word from the random letters. However, it is still not known what randomization algorithm was used to make the crossword game application to scramble the letters so that they can become random letters that will be compiled by the user. After seeing some previous research, choosing the Fisher Yates Shuffle Algorithm which is applied using the Visual Basic Application as a solution is considered very appropriate, where this algorithm will initialize letters first to numbers then the initialized letters are randomized using an algorithm, and the user guesses the series of letters that are used has been scrambled before.
Penerapan Algoritma Punctured Elias Code pada Aplikasi Kamus Besar Bahasa Indonesia (KBBI) Berbasis Android

The large Indonesian dictionary has a large size because the quality of the resulting dictionary is better, so the file size needed for it is getting bigger. And the compression process is needed to reduce the file capacity to be smaller in order to save on the use of storage space. With a relatively large data size, during the data transfer process it can fail because the storage media does not have enough space. By performing the compression process on the large Indonesian dictionary, a process can be carried out to reduce the size of the data by reducing the bits in the large Indonesian dictionary, making it easier to carry out a data transfer process effectively. The procedure for compressing the large Indonesian dictionary (KBBI) starts from selecting the large Indonesian dictionary to be compressed, then the book will be compressed with the punctured Elias code algorithm so as to produce a compressed book that has a smaller size.2. The implementation of the punctured Elias code algorithm for compression of the Big Indonesian Dictionary (KBBI) is done by reading the hexadecimal value from a Big Indonesian Dictionary (KBBI), then changing the hex value into a new bit value in the form of a binary number, then rearranging the binary value into new character. Where the compression results are based on the ratio of 32% based on 80 bit data samples with 25 Bit compression results
An Improved Gradient Optimizer with Adaptive Mechanism

In recent years, various improved gradient descent methods have been applied to neural networks and proved to be an effective way for training. However, the gradient descent method in networks has the defects of depending on the training model too much and is difficult to modify the parameters after setting. In this paper, we propose an adaptive mechanism, which can set the learning rate adaptively. And the learning rate will adaptively increase or decrease with the training process. In addition, it also has a certain sensing ability. By comparing with other methods, it is proved that our method has good generalization and reliability.
Implementasi Algoritma H.264 Untuk Kompresi File Pada Aplikasi Transfer File

Transfer files that have a relatively large size, result in a long transfer process and also consume a large internet quota in the sending process. Where the smaller the file size to be sent, the transfer process is also fast, and does not consume a lot of internet quota. To solve this problem, it is necessary to have compression which functions to reduce the size of the files to be transferred. One of the compression algorithms is H.264. The compression process using the H.264 algorithm is carried out when the file is sent, then when the file is received by the recipient, the file will be decompressed. Using the H.264 algorithm, the file will be sent successfully reducing the file size with a ratio of compression (RC) 2.5%, compression ratio (CR) 40%, redundancy (RD) 60%, and helping the transmission process of sending large files will be faster and save internet quota.