Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Out-of-Body Frames in Surgical Videos for Privacy Protection Using Self-Supervised Learning and Minimal Labels

Endoscopic video recordings are widely used in minimally invasive robot-assisted surgery, but when the endoscope is outside the patient’s body, it can capture irrelevant segments that may contain sensitive information. To address this, we propose a framework that accurately detects out-of-body frames in surgical videos by leveraging self-supervision with minimal data labels. We use a massive amount of unlabeled endoscopic images to learn meaningful representations in a self-supervised manner. Our approach, which involves pre-training on an auxiliary task and fine-tuning with limited supervision, outperforms previous methods for detecting out-of-body frames in surgical videos captured from da Vinci X and Xi surgical systems. The average F1 scores range from [Formula: see text] to [Formula: see text]. Remarkably, using only [Formula: see text] of the training labels, our approach still maintains an average F1 score performance above 97, outperforming fully-supervised methods with [Formula: see text] fewer labels. These results demonstrate the potential of our framework to facilitate the safe handling of surgical video recordings and enhance data privacy protection in minimally invasive surgery.
MANAJEMEN KEARSIPAN PADA PERUSAHAAN X

Archive management as archive storage, which is the process of maintaining and securing company records or records, in the success of records management must run on a process that includes classification, creation, receipt, and maintenance of records. This study aims to determine the archive storage system, guidelines for lending archives and obstacles to borrowing archives. This study uses a qualitative method in a descriptive form with a case study approach. The subjects in this study were 4 (four) people using a purposive sampling technique. The results of the study show that there are two archive storage systems in Company X, namely, active archives and inactive archives. Borrowing archives is in accordance with applicable procedures, however, problems in the borrowing of archives still often occur because in the procedure there is no period of time for borrowing archives, this makes employees forget about returning archives and lack of employee awareness of the importance of archives.
Effective Co-Creation Tool Development for Shared Understanding and Active Communication for Patients with Head and Neck Cancer

To improve the quality of life of cancer patients, patient experience (PE) must be improved along with the overall treatment process. This study aimed to develop an effective and practical co-design tool to improve the healthcare service experience of patients with head and neck cancer (HNC) in various factors. The research consisted of four phases: (1) HNC PE categories for healthcare improvement were identified through systematic review, user interviews, and observation; (2) a focus group meeting was held to materialize the card design; (3) a structured and visual card set was developed for stakeholders to share the content and discuss improvements in PE effectively; (4) to evaluate the feasibility of the developed cards, a co-creation workshop with HNC medical staffs was conducted. From the workshop using insight cards, we identified the differences in the medical staff’s and patients’ perspectives on the factors for improving HNC PE in each stage of the treatment journey. Pat Exp Insight Cards as experience-based co-design (EBCD) tools can be useful for stakeholders to empathize with the specific pain points and needs of patients with HNC and to discuss improvement plans efficiently.
Penerapan Data Mining Pada Analisa Pola Pembelian Obat Menerapkan Algoritma Hash Based

Along with the times, human life is also developing. Both from the mindset, perspective and way of life. The development of the times requires society to participate in developing following an increasingly sophisticated era. One of the developments that cannot be denied in social life is the development of increasingly sophisticated technology. So that in doing a business skills are needed in managing and managing a business in accordance with technological developments. The business of selling drugs or often called pharmacies is a business that is spread in almost all corners of the world, including in Indonesia. Pharmacy is a business engaged in medicine. Pharmacies sell various types of drugs that are likely to be prescribed by a medical doctor, or other drugs that are commonly purchased by the public. Here the author's case study is one of the pharmacies in the East Jakarta area. The sustainability of a business can be influenced by several aspects, but the main aspect that is very influential is business management. The database is information about all transactions that have been carried out in the business. Where with the previous sales database, business owners can assess which products are of great interest to consumers and which are not of interest to consumers. This can help business owners prevent losses in terms of expiration dates and manage stocks of goods. This management should be done by optimizing data mining by applying a hash-based algorithm. The Hash Based Algorithm is an algorithm that aims to find combination patterns of several alternatives with criteria or attributes that are used as comparisons by calculating using filtering techniques so as to produce an itemset combination pattern. Based on the results of research using a hash based algorithm, 3 itemsets were produced which became the top priority, namely B = Amoxiline, E = Amyl Nitrite and G = Biotin with a support value of 20% and 50% confidence
Redesigning Embedding Layers for Queries, Keys, and Values in Cross-Covariance Image Transformers

There are several attempts in vision transformers to reduce quadratic time complexity to linear time complexity according to increases in the number of tokens. Cross-covariance image transformers (XCiT) are also one of the techniques utilized to address the issue. However, despite these efforts, the increase in token dimensions still results in quadratic growth in time complexity, and the dimension is a key parameter for achieving superior generalization performance. In this paper, a novel method is proposed to improve the generalization performances of XCiT models without increasing token dimensions. We redesigned the embedding layers of queries, keys, and values, such as separate non-linear embedding (SNE), partially-shared non-linear embedding (P-SNE), and fully-shared non-linear embedding (F-SNE). Finally, a proposed structure with different model size settings achieved 71.4%,77.8%, and 82.1% on ImageNet-1k compared with 69.9%,77.1%, and 82.0% acquired by the original XCiT models, namely XCiT-N12, XCiT-T12, and XCiT-S12, respectively. Additionally, the proposed model achieved 94.8% in transfer learning experiments, on average, for CIFAR-10, CIFAR-100, Stanford Cars, and STL-10, which is superior to the baseline model of XCiT-S12 (94.5%). In particular, the proposed models demonstrated considerable improvements on the out-of-distribution detection task compared to the original XCiT models.
MultiViz: Towards User-Centric Visualizations and Interpretations of Multimodal Models

The nature of human and computer interactions are inherently multimodal, which has led to substantial interest in building interpretable, interactive, and reliable multimodal interfaces. However, modern multimodal models and interfaces are typically black-box neural networks, which makes it challenging to understand their internal mechanics. How can we visualize their internal workings in order to empower stakeholders to visualize model behavior, perform model debugging, and promote trust in these models? Our paper proposes MultiViz, a method for analyzing the behavior of multimodal models via 4 stages: (1) unimodal importance, (2) cross-modal interactions, (3) multimodal representations and (4) multimodal prediction. MultiViz includes modular visualization tools for each stage before combining outputs from all stages through an interactive and human-in-the-loop API. Through user studies with 21 participants on 8 trained models across 6 real-world tasks, we show that the complementary stages in MultiViz together enable users to (1) simulate model predictions, (2) assign interpretable concepts to features, (3) perform error analysis on model misclassifications, and (4) use insights from error analysis to debug models. MultiViz is publicly available at https://github.com/pliang279/MultiViz, will be regularly updated with new visualization tools and metrics, and welcomes input from the community1.
Improving small object detection via cross-layer attention

Small object detection is a fundamental and challenging topic in the computer vision community. To detect small objects in images, several methods rely on feature pyramid networks (FPN), which can alleviate the conflict between resolution and semantic information. However, the FPN-based methods also have limitations. First, existing methods only focus only on regions with close spatial distance, hindering the effectiveness of long-range interactions. Second, element-wise addition ignores the different perceptive fields of the two feature layers, thus causing higher-level features to introduce noise to the lower-level features. To address these problems, we propose a cross-layer attention (CLA) block as a generic block for capturing long-range dependencies and reducing noise from high-level features. Specifically, the CLA block performs feature fusion by factoring in both the channel and spatial dimensions, which provides a reliable way of fusing the features from different layers. Because CLA is a lightweight and general block, it can be plugged into most feature fusion frameworks. On the COCO 2017 dataset, we validated the CLA block by plugging it into several state-of-the-art FPN-based detectors. Experiments show that our approach achieves consistent improvements in both object detection and instance segmentation, which demonstrates the effectiveness of our approach.
Mind pal: an Android App for Alzheimer’s Patients

Alzheimer, a common form of dementia causes deterioration of cognitive abilities of an individual which results in difficulty in carrying out their routine activities. Research suggests that there is no cure for this deadly disease. However, the progression of the disease can be slowed down by improving the patient’s quality of life, providing a solution for enhancing the cognitive abilities of the patient. One of the possible solutions is to motivate the use of Smartphone by the patient. Smart phones play a crucial role for the family members of the Alzheimer patients as it helps the patients in carrying out their routine activities by providing time to time notifications about them. This project will be implemented by using android studio. This app will help patients with Alzheimer’s disease to make them remember all the activities they performed during a day and also remind them of the medicines they need to take at different intervals of time. This app also has a to do-list to help the users add the tasks they want to perform during the day. The project will also consist of a chatbot which acts as a friend to the patient and assist them. Also, a navigation tracker for the care taker which will let them track the patient’s location. Key Word: Android Studio, Activity log, to-do list, medicine reminder, AI Chatbot, Navigation tracking, Profile page.
Innovative Activities of Libraries in Conditions of Digital Society

The purpose of the work is to highlight the activities of libraries as one of the oldest socio-cultural institutions and the peculiarities of their innovative activities in the conditions of the digital society. Research methodology is based on the application of general scientific and special methods of cognition, in particular terminological, systemic approach, analysis, synthesis, logical method, method of visualisation of research results. Methods of content analysis, comparative and analytical monitoring of Internet resources of Ukrainian libraries have also been used. The scientific novelty consists in the expansion of ideas about the realities of the transformation of the activities of libraries and their entry into the socio-cultural space and the functioning of libraries in the conditions of the digital society. Conclusions. In the 21st century, in the conditions of the digital society, the activities of libraries are being transformed and they are becoming centres of not only scientific, but also socially significant information. Forms and types of work with users are changing. Among the new forms of user service, virtual exhibitions, online tours, presentations, video lectures, open access to digital collections have already become popular. Such innovative forms of work as book crossing, book trailer, bibliofresh, quest, library flash mob, information briefing, coworking, coaching are gaining momentum. Electronic libraries that can provide ubiquitous and round-the-clock access to documents of various formats (text, diagram, audio, video data) are becoming popular. World experience shows that an important step in the development of libraries and their provision of information needs of users is the mandatory creation of information-library networks, the search for and implementation of new forms of work. The introduction of the latest information technologies helps to create virtual libraries, blogs, pages in social networks.&#x0D; Key words: library, information technologies, socio-cultural space, innovative forms of work, electronic library.
Coaching in the System of Professional Training of Future Masters of Primary Education During the Period Under Martial Law

This article highlights the relevance of the problem of using coaching in the professional training of future masters of primary education under martial law. Theoretical analysis of scientific sources on comprehension of the definitions of "coaching" and "pedagogical coaching" is carried out; the essence, purpose, and tasks are revealed. It is noted that the purpose of pedagogical coaching is to reveal the potential for improvement of the expected learning outcomes, development of critical and logical thinking, formation of mobility, independence in decision-making, the desire for self-development, self-learning, and success in future professional activities among higher education students.&#x0D; It has been found that the introduction of coaching in the system of professional training of future masters of primary education will serve the process of training competitive and competent specialists with formed values, beliefs, and priority goals. They are capable of making both personal and professional independent decisions and generating new ideas. This method also helps master's students to develop appropriate self-esteem and academic stress resistance. This is the basis of educational and cognitive activity in the conditions of wartime.&#x0D; It is concluded that coaching belongs to innovative technologies of professional education; it is a powerful tool for personal and professional growth for future masters of primary education; it is a technology for organizing scientific and pedagogical research that promotes the implementation of a student-centered approach in higher education institutions based on trust, partnership, and mutual respect. It is also important to provide a comprehensive rather than fragmentary implementation of coaching to achieve the aims of professional education, improve its quality, and establish cooperative subject-subject relations between professors and students.
Let's enhance: A deep learning approach to extreme deblurring of text images

This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data.Our results build on our winning submission to the recent Helsinki Deblur Challenge 2021, whose goal was to explore the limits of state-of-the-art deblurring algorithms in a real-world data setting.The task of the challenge was to deblur out-of-focus images of random text, thereby in a downstream task, maximizing an optical-character-recognition-based score function.A key step of our solution is the data-driven estimation of the physical forward model describing the blur process.This enables a stream of synthetic data, generating pairs of ground-truth and blurry images onthe-fly, which is used for an extensive augmentation of the small amount of challenge data provided.The actual deblurring pipeline consists of an approximate inversion of the radial lens distortion (determined by the estimated forward model) and a U-Net architecture, which is trained end-to-end.Our algorithm was the only one passing the hardest challenge level, achieving over 70% character recognition accuracy.Our findings are well in line with the paradigm of data-centric machine learning, and we demonstrate its effectiveness in the context of inverse problems.Apart from a detailed presentation of our methodology, we also analyze the importance of several design choices in a series of ablation studies.The code of our challenge submission is available under https://github.com/theophil-trippe/HDC_TUBerlin_version_1.
Learning Smooth Target-Aware Spatially Regularized Correlation Filters for UAV Tracking

Spatially regularized correlation filters (SRCF) have recently received increasing interest for Unmanned Aerial Vehicle (UAV) tracking due to their promising results. While the choice of spatial weight matrices is vital for the success of SRCF methods, they are generally learned only with the training samples in current frame, which results in time-discontinuous spatial weight matrices in neighboring frames, thus degrading the CF models. In this paper, we propose a Smooth Target-Aware Spatially Regularized Correlation Filter (STASRCF) framework for UAV tracking. Specifically, we first obtain the initial target-aware spatial weight matrix in each frame by employing the image segmentation techniques for separating the target from the background, then multiple adaptive spatial regularization terms are integrated into the CF framework for jointly updating the spatial weight matrices and CF models. In this way, time-continuous spatial weight matrices and robust CFs can be learned during tracking, thereby benefiting the tracking performance. In addition, we suggest an Alternating Direction Method of Multipliers (ADMM) method for solving STASRCF efficiently, in which each sub-problem has a closed-form solution. Experiments on multiple UAV datasets show that STASRCF can not only surpass the baseline CSR-DCF by an average AUC gain of 1.9%, but also perform favorably against other state-of-the-art CF trackers.
Research on Basic Clinical Treatment Pattern Mining Based on Electronic Medical Record Big Data

This paper analyzed the basic clinical treatment pattern by exploring the relationship between diseases, symptoms and drugs, which help non-medical people understand the basic clinical treatment pattern, so as to better carry out medical and health big data mining and eliminate public prejudice against symptomatic treatment. The FP growth algorithm was used to mine the association rules from EMR big data. Combined with intersection analysis, the basic clinical treatment pattern was summarized. 507 disease-drug rules and 2141 symptom-drug rules were obtained, indicating that both diseases and symptoms are strongly associated with drugs. Intersection analysis showed that 33.7% of the disease-drug rules were symptom-independent, while 34.6% of the symptom-drug rules were disease-independent. The basic clinical treatment pattern consists of three parts: (1) The combination of disease and symptomatic medication pattern. (2) Independent disease medication pattern. (3) Independent symptomatic medication pattern.
Spacecraft attitude maneuver planning with multi–sensor pointing constraints using improved RRT–star algorithm

During the spacecraft attitude maneuvers for the observation of mission targets, multiple instruments may face different pointing constraints, and they are rarely considered by the current attitude maneuver planning methods. In this case, this paper proposes an improved rapidly–exploring random trees star (RRT*) algorithm to obtain discrete quaternion path nodes while satisfying the pointing constraints of multiple sensors. Next, the temporal properties of these path nodes are defined by a trapezoidal path planning method, and bounded constraints on the angular velocity and control moment during the attitude maneuver are introduced. Then, the continuous quaternion curves through the discrete quaternion path nodes are calculated by a segmented cubic spline interpolation function. Finally, the angular velocity and control moment are calculated through the inverse dynamics. The simulation results verify the feasibility of the method. It can satisfy complex constraints and obtain the attitude optimization path, and alleviate the large control moment variations in the start and stop phases of the rest–to–rest maneuver.
Gesture Controlled Wheelchair with IoT Based Fall Detection

Abstract: Any kind of accident related to falling can cause serious injuries to the victim. Fall can happen arbitrarily and anywhere without one’s preparation for remedy. The situation is aggravated especially for an incident occurring at a deserted place where the afflicted victim may be unable to notify other people for help. Disabled people who are bound to a wheelchair are more susceptible to an incident of falls and the accompanying issues. So, we have provided a technical model where the event of falling off a wheelchair is detected. A multitude of IoT-based services has been provided to alleviate the need to notify the intended person about the fall for getting the immediate help required at that moment. An alarm is also provisioned to notify other people in the vicinity. The provision for gesture-based movement for the wheelchair is also provided to operate the wheelchair in case of emergency when other people are not around and it works using inputs from an accelerometer which is also used for fall detection.
Cryptocurrencies’ Values determined and advantages of Cryptocurrencies investments

Abstract: As long as the government continues to print and distribute currency, inflation will rise and spread across society. A well-functioning government or financial institution isn't necessarily a bad thing if it monitors the market and keeps an eye on the amount of money being issued at any one time. Economic growth and prosperity may be aided by even a little bit of inflation. In other cases, courts have found no misconduct on the part of the government. Since the economy was floundering, they continued to pour cash into it. Hyperinflation occurs when the currency loses almost all of its value. On the other side, investing in digital currencies provides instant access to your money, wherever you are or whatever time of day or night you need it. Despite the fact that your money is still invested, you have the freedom to withdraw it anytime you choose. It is quite legal to withdraw funds from your investment account in the case of an emergency. It's possible that the market may decline and you'll be able to withdraw your money before it's all gone. In this piece of the paper, we will discuss about the cryptocurrencies and its’ investments’ advantages.
Implementation of Decentralized Blockchain E-voting

Abstract—This paper proposes a solution to the drawbacks of traditional e-voting Blockchain technology. E-voting is more cost-effective and convenient than the traditional pen and paper method, but it is considered unreliable due to the possibility of physical tampering and a lack of security for voters. By using decentralized Blockchain technology, this research aims to create an e-voting system that guarantees protection of voter identity, secure data transfer, and verifiable results through an open and transparent voting process.