Reviewerly/exHarmony huggingface dataset:
Analysis of Audio Transcription Tools with Real Corpora: Are They a Valid Tool for Interpreter Training?

The COVID-19 crisis poses an unpredicted challenge for communication in healthcare for patients and healthcare staff. This has increased even more the trend to remote communication that was lately exponentially preferred due to more availability of languages, reduction of expenses and time. Technologies also provide help to the interpreters since they can offer among others the possibility of converting speech to text and so they can provide a more accurate rendition of the conversations which is in most cases of vital significance. Our research has been carried out within the framework of the VIP II Project and tries to focus on the use of technologies as a support for professional interpreters’ work and at the same time on the accuracy of these text-recognition tools. These experiences could be valid to propose effective guidelines for the practice of the profession and the valuable inclusion of these mechanisms in the training of future healthcare interpreters.
Yoga Pose Estimation Using Machine Learning

Yoga, in the Western culture, is considered a form of posture-based physical activity which helps relieve stress and relax your muscles while increasing flexibility. On the other hand, traditional yoga is focused on meditation and released from worldly attachments. It was first brought up in Rigveda with references in the Upanishads. Yoga originated in India, more than 5000 years ago. While some poses in yoga are simple to understand and perform, some poses require precision in the angle at which your body stays to avoid injuries. In this paper, we propose a system to detect the yoga pose of an individual and help them with the correct pose, if wrong. There are several key points detection algorithms that can be used such as OpenPose, PoseNet, and PifPaf. The key points extracted from the video captured are passed to the system, to calculate the angles made by several joints. These angles are used to check whether the posture is correct or not.
Improved Complexity in Localization of Copy-Move Forgery Using DWT

This paper proposes an image forgery localization algorithm based on extracting texture features from fixed size image blocks. The computation complexity of the block-based methods depends on the number of blocks, which in turn depends on the image size. Higher the block number more is the complexity. Thus, in an attempt to improve the complexity, this paper proposes a technique based on discrete wavelet transform (DWT) of image. The single level DWT of image yields four sub-band images of which the LL sub-band is considered for further processing in place of the original image, as it carries the maximum spatial information. While the size of the LL band is one fourth the size of the original image, the proposed algorithm when applied to the LL band image yields reasonable results.
Management of School Operational Assistance in Increasing the Quality of Education

School Operational Assistance (BOS) is an implementation of Law Number 20 of 2003 article 34 paragraph 2 that the central and regional governments guarantee the implementation of compulsory education without charging a fee. BOS management in madrasas needs to be carried out to support the provision of facilities and infrastructure in order to streamline teaching and learning activities and improve student learning achievement BOS management at MAS Sinar Islami Bingai Langkat Regency consists of budget planning, fund management, fund utilization, and supervision. As for some of the principles implemented by MAS Sinar Islami Bingai Langkat Regency, they include: 1) transparency (openness in managing activities), 2) accountability (the use of funds can be accounted for), 3) effectiveness (arranging finances to finance activities so that they are in accordance with planning), 4) efficiency (use of time, effort and cost with a minimum budget to get satisfactory results). This study used a descriptive qualitative method where researchers immediately went into the field to collect data objectively and naturally. The techniques for collecting data in this study used observation techniques, interviews and documentation studies. The results of the research conducted at MAS Sinar Islami Bingai, Langkat Regency, are that several parties in the madrasah have prepared a funding management plan, the management of funds is in accordance with the plan that has been discussed so that it is right on target and can improve teacher welfare and improve student achievement, the use of BOS funds is in accordance with existing technical guidelines as well as implementing a trustworthy attitude, supervising the management of funds consisting of internal madrasah parties and external parties.
Automated ADR Analysis from Twitter Data Using N-Gram-Based Feature Extraction Methods and Supervised Learning Classification

A sizable dataset regarding customer preferences and experiences with a range of products is available on online discussion forums and review websites. This data can be utilized to draw illuminating conclusions using opinion mining techniques like sentiment analysis. The pharmaceutical industry's online consumer reviews will be examined in this essay. This field's online user reviews frequently include information on a number of subjects, such as drug efficacy and side effects, which makes automatic analysis both exciting and challenging. Assessing viewpoints on the many aspects of medication reviews, however, can provide informative information, support of making decision, and enhance monitoring the public health through the disclosure of the accumulated knowledge. This research introduces a technique that automatically detects ADRs that are openly referenced in public data in order to identify the ADR impacts of COVISHIELD. Additionally, it offers a method that increases and visually illustrates how well sentiment analysis and machine learning algorithms anticipate outcomes. The work compared classical and quantum SVM algorithms in terms of performance metrics. Results clearly show that the quantum classifier outperformed others in terms of accuracy.
Multiple Infinity in the Reality of the Virtual: Consequences for the Personality

The article is devoted to the problem of a new type of multiple infinity that appears in virtual reality. This third type of infinity poses the problem of a new philosophical understanding of human reality in virtuality, its striving for the authentic.
Exploiting Geometric Constraints in Multi-Agent Pathfinding

In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents’ shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents’ start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.
Improving Zero-Shot Coordination Performance Based on Policy Similarity

Over these years, multi-agent reinforcement learning have achieved remarkable performance in multi-agent planning and scheduling tasks. It typically follows the self-play setting, where agents are trained by playing with a fixed group of agents. However, in the face of zero-shot coordination, where an agent must coordinate with unseen partners, self-play agents may fail. Several methods have been proposed to handle this problem, but they either take a lot of time or lack generalizability. In this paper, we firstly reveal an important phenomenon: the zero-shot coordination performance is strongly linearly correlated with the similarity between an agent's training partner and testing partner. Inspired by it, we put forward a Similarity-Based Robust Training (SBRT) scheme that improves agents' zero-shot coordination performance by disturbing their partners' actions during training according to a pre-defined policy similarity value. To validate its effectiveness, we apply our scheme to three multi-agent reinforcement learning frameworks and achieve better performance compared with previous methods.
Symmetry Detection and Breaking in Linear Cost-Optimal Numeric Planning

One of the main challenges of domain-independent numeric planning is the complexity of the search problem. The exploitation of structural symmetries in a search problem can constitute an effective method of pruning search branches that may lead to exponential improvements in performance. For over a decade, symmetry breaking techniques have been successfully used within both optimal and satisficing classical planning. In this work, we show that symmetry detection methods applied in classical planning with some effort can be modified to detect symmetries in linear numeric planning. The detected symmetry group, thereafter, can be used almost directly in the A*-based symmetry breaking algorithms such as DKS and Orbit Space Search. We empirically validate that symmetry pruning can yield a substantial reduction in the search effort, even if algorithms are equipped with a strong heuristic, such as LM-cut.
Efficient Multi Agent Path Finding with Turn Actions

Current approaches for real-world Multi-Agent Path Finding (MAPF) usually start with a simplified MAPF model and modify the resulting plans so they are kinematically feasible. We investigate one such problem, called MAPF with turn actions MAPF_T, and show that ignoring the kinematic constraints significantly increases solution cost. A first modification of the popular Conflict-Based Search algorithm to MAPF_T yields significantly better plans but comes at the cost of substantial decreases in scalability. We then introduce several techniques that can improve the performance of CBS for MAPF_T, including stronger and generalised versions of existing symmetry-breaking constraints and a novel pruning technique that eliminates redundant branches in the CBS constraint tree. Experimental results on six popular MAPF domains show convincing improvements for CBS success rate and substantial reductions in node expansions and runtime.
Search Algorithms for Multi-Agent Teamwise Cooperative Path Finding [Extended Abstract]

Multi-Agent Path Finding (MA-PF) finds collision-free paths for multiple agents from their respective start to goal locations. This paper investigates a generalization of MA-PF called Multi-Agent Teamwise Cooperative Path Finding (MA-TC-PF), where agents are grouped as multiple teams and each team has its own objective to minimize. In general, there is more than one team, and MA-TC-PF is thus a multi-objective planning problem with the goal of finding the entire Pareto-optimal front that represents all possible trade-offs among the objectives of the teams. We show that the existing CBS and M* for MA-PF can be modified to solve MA-TC-PF, which is verified with tests. We discuss the conditions under which the proposed algorithms are complete and are guaranteed to find the Pareto-optimal front for MA-TC-PF.
Study on Six-tone in Dan Opera—Morphological Analysis of Music based on Shang Kernel Theory

Dan Opera is a local drama of Danyang.The core part of Dan Opera is transformed and evolved artificially from the folk rap -"Lang Dang" music in Danyang.After the Lang Dang was transformed into the music of the Dan Opera, the main basic singing tune of the Dan Opera -"Lang Dang tune" was formed.The theory of Shang kernel theory and the revelation of its core position is a great discovery in the research of musical morphology theory in the 20th century.This viewpoint is first put forward by Mr. Tong Zhongliang in 1995 in the article < The Shang Kernel Theory -A Comparison of Some Problems in the Relationship between Chinese and Western Music Studies >.Later, scholars such as Luodi, Du Yaxiong, etc. have published articles to discuss the rhythmology basis, the structure of the tune, and the arrangement of rhythmology in the Shang kernel theory. This paper takes Dan Opera and as the specific object and turns the problems caught in the research of rhythmology to musical morphology. Through the analysis of concrete, objective, and practical historical heritage works and the most common singing tune, Qupai, and representative works in Dan Opera, the paper studies the potential relationship between the six tone phenomena and "Shang kernels" existing in Dan Opera , tries to make every effort to apply the Shang kernel theory into traditional Chinese music works and seeks to discover the existing traces of the Shang kernel theory, so as to endow the theory and practice of Shang kernel theory with its practical significance and application value.
Empirical Study on Teaching Achievements Nurturing System

This study aimed to trigger further exploring and studying on teaching achievements cultivation by analyzing the significance of the design and development, as well as the details of the system operation and using effect of Web TANS.
Non-semantic Research of Music

The non-semanticity of music is an important point about the particularity of music. "Music has non-semanticity" hasbasically become a fixed formula.It can be said that it is difficult to shake its position in the essence of music aesthetics.It is an important part of the essence of music aesthetics.It is an essential part of the discussion of the special nature of music in the academic discussion on the issue of language -related issues on music.Meaning cannot be explained literally "no language meaning".At the same time, it should also be "unknown reflected in music", "unobstructed language embodied by music", and "the language reflected in music is difficult to generalize".Think about these three aspects.Therefore, this article will review the literature from the above three aspects.
Thoughts on Aesthetic Character in Music Education

Music as a cultural form full of art, and all have artistic value, the same, with a special sense of beauty to help people ease the mood, relax, vent and enjoy the mood.The new curriculum standard proposes to give full play to the important role of art curriculum in cultivating students' aesthetic and humanistic quality.Aesthetics, as the name implies, is people's examination and experience of beauty.In the context of music education, aesthetics is "the experience of life vitality obtained by observing and reacting to the character of life vitality".
The impact of ICT on Shaping Reflexes and Manual Coordination in Children and Teenagers: Pupils’ and Teachers’ Opinions

The article presents part of a diagnostic and correlational study of mixed character that establishes pupils’ information competency in the use of ICT methods and tools. It investigates the effects of children’s and teenagers’ ICT use (importance levels of new media influence) on improving reflexes and manual coordination. To obtain the data, the author used a diagnostic survey method (questionnaire and interview) and statistical methods (chi-square test of independence and Pearson’s correlation coefficient). Together, 2,510 pupils and 1,110 teachers were surveyed. It was found that in the aggregated importance hierarchy of ICT effects, the category ‘reflexes and manual coordination’ was given a high weight by the pupils (third place) and a very high weight by the teachers (second place). There was a faint and negative correlation (r @ -0.009) between pupils’ opinions and teachers’ observations on the effects of ICT use by children and teenagers in the improvement of reflexes and manual coordination. The calculations of differential factors revealed statistically significant differences between the opinions of the two groups and gender, educational stage, place of learning and the gender of teachers. There was a noticeable discrepancy between the pupils’ and teachers’ opinions: a not very significant ‘separation’ of the world of children and teenagers (‘us’) from the world of teachers (‘them’). This may raise concerns as teachers’ recognition and understanding of students’ needs, according to the ideas of constructivism, is one of the key factors in the success of the educational process.
AUTO BILLING SMART CART

Technology has changed so much, so is the rate of people of all ages who are attracted to electronic gadgets.In many industries, electronic devices such as smart card readers, barcodes, and RFID scanners are increasingly used.Supermarkets also need these kinds of gadgets.Currently, every person in the mall purchases the product placed in the trolley.Upon purchase, the person will have to stand in a queue for billing.In the billing process, an employee scans each product's barcode and bills it to the final.This process can take a lot of time and it can be even worse on holidays, special offers or weekends.To overcome this, a smart way to shop in malls has been developed.Each product has an RFID tag instead of a barcode.The Smart Trolley features an RFID reader, LCD module.When a person places any product on the trolley, it is scanned and the product's cost, name, and expiration date are displayed.The total cost will be added to the final check out bill.The bill is stored in the microcontroller's memory.
MULTIPLE DISEASE PREDICTION USING MACHINE LEARNING, DEEP LEARNING AND STREAM-LIT

Multiple Disease Prediction using Machine Learning, Deep Learning and Streamlit is a comprehensive project aimed at predicting various diseases including diabetes, heart disease, kidney disease, Parkinson's disease, and breast cancer.This project leverages machine learning algorithms such as TensorFlow with Keras, Support Vector Machine (SVM), and Logistic Regression.The models are deployed using Streamlit Cloud and the Streamlit library, providing a user-friendly interface for disease prediction.The application interface comprises five disease options: heart disease, kidney disease, diabetes, Parkinson's disease, and breast cancer.Upon selecting a particular disease, the user is prompted to input the relevant parameters required for the prediction model.Once the parameters are entered, the application promptly generates the disease prediction result, indicating whether the individual is affected by the disease or not.This project addresses the need for accurate disease prediction using machine learning techniques, allowing for early detection and intervention.The userfriendly interface provided by Streamlit Cloud and the Streamlit library enhances accessibility and usability, enabling individuals to easily assess their risk for various diseases.The high accuracies achieved by the different models demonstrate the effectiveness of the employed machine learning algorithms in disease prediction.
PENDAMPINGAN PEMBUATAN MEDIA PEMBELAJARAN BERDASARKAN TEMA KELAS 1-3 MENGGUNAKAN BENDA DISEKITAR PADA SDIT AZ-ZAHRA TAKENGON

This community service intends to increase elementary school teachers' capacity to create instructional materials that are consistent with the learning theme, particularly for teachers of grades 1-3. At SD IT Az-Zahra, this service was provided by 12 teachers of grades 1-3. This activity involves assisting in the creation of learning materials based on the lesson's theme utilizing available materials. Teachers who could create learning material and deliver them well were able to get the desired effects from this activity. It is intended that by engaging in this exercise, teachers would become more motivated and inspired to continue using the resources at hand.
A STUDY ON HOST MACHINE OVERLOAD DETECTION ALGORITHM IN CLOUD DATA CENTER

A critical step in the motion of virtual machine consolidation (VMs)process is host-overloading detection. A particularly intriguing method of avoiding host overload is to use machine learning to forecast future host demand. Host overflow detection, which attempts to foretell whether or not a physical server will become overloaded by VMs, is a crucial stage when consolidating VMs. Major cloud management difficulties in relation to clouds computing include energy utilisation and service level agreements (SLAs). The utilisation inside cloud computing data centres is rapidly growing to meet the large growth in demand for high-performance computing (HPC), storage, and networking resources for commercial and academic purposes. In this research, we attempt to study the performance of the detection of host overload algorithm in cloud datacenters.
Person Re-identification Lightweight Network Based on Progressive Attention Mechanism

This paper proposes a lightweight person re-identification network that incorporates a progressive attention mechanism The network aims to address the low accuracy issue in person re-identification caused by various factors such as different viewing angles, poses, illumination conditions, occlusions, and low image resolutions. Additionally, the network design takes into consideration the need for lightweight model deployment in practical scenarios. To improve the network’s performance using limited training data, data augmentation techniques are employed to expand the training dataset and enhance the robustness of the network model. The Resnet-50 architecture serves as the backbone network, and a feature shunt structure with depthwise separable convolutions is introduced to reduce computational parameters and accelerate person retrieval inference speed. Furthermore, the feature extraction and embedding processes are separated, and a progressive attention module is introduced. This module gradually segments the features into local blocks of different granularity, allowing for the learning of discriminative features at each granularity level. This progressive approach enhances the network’s ability to perceive foreground information from coarse to fine levels and improves feature matching capability. To supervise the model, a triplet loss function is utilized, specifically designed to address challenging samples. This loss function helps reduce intra-class variations while increasing inter-class separability. The efficacy of the proposed method in person re-identification is substantiated by conducting experimental evaluation on both the Market-1501 and DukeMTMC-ReID datasets. The experimental results demonstrate that the method achieves mAP indices of 88.1% and 79.1% on the respective datasets, providing strong evidence for its effectiveness in addressing the challenges of person re-identification.