Reviewerly/exHarmony huggingface dataset:
Tes Takayama sebagai Sarana Identifikasi Sampel Darah Terpapar Media Dekomposisi Tanah

In criminal cases, blood spots are one of the markers of a crime. Spots left on an object, no matter how small, can be used as evidence, whether it comes from humans or not. Criminals often try to eliminate traces of their actions by hiding or disposing of bloodstained items in the hope of complicating the investigation process. This study refers to the use of the Takayama Test in determining the origin of blood spots. This test is one of the method to test whether the spots found are blood or not, and confirm th origin of the blood. Based on the number of cases in which the perpetrators disposed of evidence, this study aims to determine the extent to which the Takayama Test is used to identify blood spots on cloth with the influence of soil decomposition media. The study used a True Experimental design with randomization and a control group. Data analysis using descriptive analysis. The results obtained from observations for 30 days, the Takayama test showed positive results and could identify the presence of blood spots on the soil decomposition media. A positive result is indicated by the presence of hemochromogen crystals that form on blood spots that have been exposed to the soil. The formation of brownish black crystals was found in the spots exposed to the soil medium.
Improved Federated Average Algorithm for Non-independent and Identically Distributed Data

Federated learning is a distributed machine learning model that can protect data privacy, where a large number of clients and servers participate in the joint train procedure. However, the problem of heterogeneous data has been a potential obstacle for federated learning. Traditional joint averaging algorithms suffer from low accuracy and slow convergence on non-IID (identical and independently distributed) data. To this end, this paper proposes an improved FedAvg based algorithm called FedAvg-Z. We introduce penalty coefficients and adaptive parameter averaging methods in our algorithm, which adjusts the influence of clients on the global model as a way to increase the convergence and stability of the model. We first demonstrate the reliability of our method on the dataset and then compare it experimentally with the traditional algorithm. The experiments show that the algorithm has higher accuracy and convergence speed in the non-IID case.
Determining the most important educational, administrative, social and psychological obstacles facing blended education in teaching volleyball from the point of view of the teachers of the faculties of the central and southern regions

The research included four chapters, where the first chapter included a definition of the research from the introduction to the research and its importance. The development of the educational sciences and the emergence of modern strategies that help teachers and students acquire knowledge and sciences in a manner consistent with the nature of the learner in order to reach effective education that achieves the desired goals, and the problem of Research the existence of obstacles that prevent the success of blended education, and the study aimed to identify the concept of blended education and its importance, its advantages, and justifications for its use in universities in general, and faculties of physical education and sports sciences and teaching volleyball in particular, and the requirements of blended education and its obstacles and to identify the reality of blended education in colleges from the point of view of The faculty members looked at, and revealed the problems that limit the use of blended education in those colleges, and the degree of availability of physical equipment to assist in the application of blended education.
MediaPipe to Recognise the Hand Gestures

Human Computer Interaction (HCI) can be improved drastically using the hand gesture based recognition system. This system is designed to detect the gestures of the hands in the images captured in real time. There are certain areas of intersect in the hands that are there for the classification. The gaming devices like Xbox, PS4 and smart phones are also using this method to solve few problems. In this paper a smart method i9s developed to solve the problem. Using Python 3.9 and MediaPipe, the hand gestures are recognised in the real-time images. The background subtraction is the key method used to generate the results. The hand is detected and processed for the finding of the binary image with the fixed number of pixels. The palm position, dimension and the gesture are recognised. In this experiment, the finger count and the position of the fingers is considered as the gesture. The finger count can also be calculated after the hand is recognised. The major areas of the Image Processing and the MediaPipe in Python are covered to solve this problem.
Prefix-free parsing for building large tunnelled Wheeler graphs

We propose a new technique for creating a space-efficient index for large repetitive text collections, such as pangenomic databases containing sequences of many individuals from the same species. We combine two recent techniques from this area: Wheeler graphs (Gagie et al., 2017) and prefix-free parsing (PFP, Boucher et al., 2019). Wheeler graphs (WGs) are a general framework encompassing several indexes based on the Burrows-Wheeler transform (BWT), such as the FM-index. Wheeler graphs admit a succinct representation which can be further compacted by employing the idea of tunnelling, which exploits redundancies in the form of parallel, equally-labelled paths called blocks that can be merged into a single path. The problem of finding the optimal set of blocks for tunnelling, i.e. the one that minimizes the size of the resulting WG, is known to be NP-complete and remains the most computationally challenging part of the tunnelling process. To find an adequate set of blocks in less time, we propose a new method based on the prefix-free parsing (PFP). The idea of PFP is to divide the input text into phrases of roughly equal sizes that overlap by a fixed number of characters. The original text is represented by a sequence of phrase ranks (the parse) and a list of all used phrases (the dictionary). In repetitive texts, the PFP of the text is generally much shorter than the original. To speed up the block selection for tunnelling, we apply the PFP to obtain the parse and the dictionary of the text, tunnel the WG of the parse using existing heuristics and subsequently use this tunnelled parse to construct a compact WG of the original text. Compared with constructing a WG from the original text without PFP, our method is much faster and uses less memory on collections of pangenomic sequences. Therefore, our method enables the use of WGs as a pangenomic reference for real-world datasets.
Aplikasi Pemodelan Logit, Probit dan Clog-Log Pada Regresi Binomial (Studi Kasus: Pemodelan Penyakit Jantung)

This study aims to compare the modeling of the causative factors of heart disease using logit, probit and clog-log regression. The three models gave the same results for both the simultaneous and partial tests in modeling the probability case of heart disease. The Probit model gives the best results with the smallest error value criteria (AIC and BIC) and the largest prediction accuracy value. All complications of disease (high blood pressure, cholesterol, diabetes, smoking habits) have a higher chance of developing heart disease risk than those who do not. In terms of gender, men have a higher risk of heart disease than women. In general, it can be concluded that complications and gender have an effect on heart disease. Based on these results, it is expected to increase awareness of the risk of heart disease and its causes.
Profil kolaborasi guru bimbingan dan konseling dengan guru mata pelajaran dalam mengatasi kesulitan belajar di kelas XI IPS SMA Negeri 13 Padang

This research is motivated by the lack of collaboration between Guidance and Counseling Teachers and Subject Teachers in overcoming learning difficulties experienced by students. This study aims to determine the form of collaboration carried out by the Guidance and Counseling teacher with subject teachers in diagnosing learning difficulties in the form of 1) identifying students who have learning difficulties, 2) determining the location of students' learning difficulties, and 3) determining the factors causing the occurrence. learning difficulties in students. This research uses a descriptive qualitative method. The key informants of this study were 2 counseling teachers and 2 subject teachers, additional informants were 2 subject teachers who taught in class XI IPS at SMAN 13 Padang. This study uses interviews as a technique of collecting data. The data analysis technique consists of three stages, namely data reduction for grouping data relevant to the research objectives and focus, data presentation and drawing conclusions. The results of the study revealed: 1) Collaboration between counseling teachers and subject teachers in identifying students who have learning difficulties, namely providing information and discussing the data collected in determining students who have learning difficulties. 2) Collaboration between guidance and counseling teachers and subject teachers in determining the location of student learning difficulties, namely exchanging information and communicating informally in determining how to locate student learning difficulties. 3) Collaboration between guidance and counseling teachers and subject teachers in determining the factors causing student learning difficulties, namely conducting discussions, counseling and also discussing. The results of this study are recommended for counseling teachers to collaborate with other parties such as subject teachers, school principals, and parents and other parties in overcoming learning difficulties of students.
Succinct Classical Verification of Quantum Computation

We construct a classically verifiable succinct interactive argument for quantum computation (BQP) with communication complexity and verifier runtime that are poly-logarithmic in the runtime of the BQP computation (and polynomial in the security parameter). Our protocol is secure assuming the post-quantum security of indistinguishability obfuscation (iO) and Learning with Errors (LWE). This is the first succinct argument for quantum computation in the plain model; prior work (Chia-Chung-Yamakawa, TCC '20) requires both a long common reference string and non-black-box use of a hash function modeled as a random oracle. At a technical level, we revisit the framework for constructing classically verifiable quantum computation (Mahadev, FOCS '18). We give a self-contained, modular proof of security for Mahadev's protocol, which we believe is of independent interest. Our proof readily generalizes to a setting in which the verifier's first message (which consists of many public keys) is compressed. Next, we formalize this notion of compressed public keys; we view the object as a generalization of constrained/programmable PRFs and instantiate it based on indistinguishability obfuscation. Finally, we compile the above protocol into a fully succinct argument using a (sufficiently composable) succinct argument of knowledge for NP. Using our framework, we achieve several additional results, including - Succinct arguments for QMA (given multiple copies of the witness), - Succinct non-interactive arguments for BQP (or QMA) in the quantum random oracle model, and - Succinct batch arguments for BQP (or QMA) assuming post-quantum LWE (without iO).
A SURVEY ON COMMON ERRORS IN STUDYING SPEAKING SKILL OF ENGLISH-MAJORED FRESHMEN AT TAY DO UNIVERSITY, VIETNAM

Speaking is regarded as the most important aspect of communication skills, and it contributes to the success of language learners in their studies and careers, as well as the success of teachers in their teaching process. However, English-majored students, especially English-majored freshmen, usually face some errors in speaking English. Consequently, this research, entitled "A Survey on Common Errors in Studying Speaking Skills of English-majored Freshmen at Tay Do University" was conducted to figure out some common problems in speaking English that these English-majored students have encountered. The participants of this research were 80 students from two classes, English 15A and English 15B, at Tay Do University. The main research instrument for collecting the data was a questionnaire. The findings of this research pointed out the participants’ errors in speaking skills problems (pronunciation, grammar tense, pronunciation, mother tongue, vocabulary) and psychological problems (anxiety of error, lack of motivation, shyness, lack of confidence). The results showed that the students had errors in their speaking skills. Thanks to the results, some useful solutions were proposed for English-majored students.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0965/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
V2PFQL: A proactive fault tolerance approach for cloud‐hosted applications in cloud computing environment

Proactive Fault tolerant (FT) approaches for infrastructure's cloud computing which have been increasingly developed in research scope with the support of machine learning need consistent monitoring and highly accurate predicting systems. However, it is difficult to implement the proactive fault tolerant approach of infrastructure's cloud computing because of complex architecture, dynamic services, and many interdependent factors existing in cloud environment. To overcome this problem, a proactive FT approach for cloud-hosted application is proposed to achieve a high degree of automation, including physical machine (PM) fault analysis and self-learning virtual machine (VM) migration. It is based on MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) loop to take into account the knowledge inside controllers in run time. To evaluate PM fault analysis model, the web e-Commerce is deployed by OpenStack Stein while the prototype of VM migration component is built by CloudSim for VM migration solution evaluation. The effectiveness of the approach is demonstrated in experiment results.
Pengembangan Media Lego Untuk Meningkatkan Hasil Belajar Siswa pada Materi Hidrokarbon di Sekolah Menengah Atas

The difficulty of students in learning chemistry subjects is due to the use of media that is not in accordance with the characteristics of students who want fun-based learning. Therefore, it is necessary to develop a Lego hydrocarbon learning media that is suitable for use as a learning medium for hydrocarbon materials. This study aims to produce a feasible and practical Lego hydrocarbon learning media. The feasibility of the Lego hydrocarbon learning media in terms of two criteria, namely (1) validity is determined from the validation results, (2) practicality is determined from student response questionnaires. This type of research is research and development of the ADDIE model. The results showed that the Lego hydrocarbons learning media was suitable for use as learning media with the eligibility criteria measured from the results of the validity of the material aspects and the aspects of the Lego hydrocarbons media of 0.95 and 0.92, respectively, with very valid criteria. The percentage of practicality from the results of student responses in the initial field trial and the main field trial were 90.42% and 95.64%, respectively, with both very practical criteria. Thus, the Lego hydrocarbon learning media is appropriate to be used as a chemistry learning medium
Comparative Analysis of Image Enhancement Algorithms

In the complex instruments utilised in essential fields such as satellite cameras, CT scanners, and High-Resolution Cameras (Underwater), image capture is critical without human-rated aberrations, sounds, or atmospheric disturbances. Even full reference QA (quality assessment) approaches have a limited ability to predict quality accurately. As a result, the difficulty of evaluating and enhancing photographs is further subdivided into domain-specific issues by focusing on a small set of artefacts. The most popular is entropy, which is usually relevant in picture coding: it is a lower limit for the average coding length in bits per pixel that may be attained without any loss of information by an optimal coding scheme. The word 'specific' is significant because it establishes right away that the strategies covered in this paper are primarily problemsolving techniques. For example, a procedure that works well for improving X-ray images may not be the ideal option. Thus, a method that works well for boosting X-ray photos may not be the greatest option for enhancing photographs obtained by a satellite thousands of miles away from the Earth. Image enhancement algorithms proposed in this paper are Intensity-Hue-Saturation transformation, Histogram Equalization algorithms, Edge Detection techniques and Retinex theory algorithms. These algorithms are implemented under satellite imagery, medical scans, underwater images, and their parameter analysis.
Case of Reading Teaching on Task-Based Teaching Model in Middle Schools

In April 2022, the Ministry of Education of the People's Republic of China officially announced the English Curriculum Standards for Compulsory Education (2022 Edition). Since the release of the English Curriculum Standards for Compulsory Education (2011 Edition), the reform of the English curriculum for compulsory education has made remarkable achievements; at the same time, however, there are also some problems that need to be solved urgently. The new curriculum standards should change the teaching of vocabulary and grammar knowledge as the main content, select learning contents around the comprehensive performance of core literacy, design teaching units as a whole, and plan the blueprint of unit education. According to the students' comprehensive performance and the objectives of the school section, the content should be closely related to the real life, especially the social life and learning life that students are familiar with and meet the characteristics of their physical and mental development. The content selected should reflect the characteristics of the times and social progress, focus on the themes of people and nature. Elements on theme, discourse, language knowledge, cultural knowledge, language skills and learning strategies should be considered when selecting the the teaching content. Ultimately, students are expected to use the knowledge and skills to solve tasks and problems in practice. In terms of middle school English teaching, the author holds that Task-Based Teaching Mode will help teachers to achieve the above goals. Task-Based teaching emphasizes learning by doing, which allows students to interact with teachers, and it also creates a favorable environment for students to learn and internalize. The middle school English teaching goal is to help the students to obtain the basic English knowledge and the ability of using English to communicate through listening,speaking,reading and writing training. As a helpful teaching approach, the Task-Based Teaching Model can be widely used. Based on the English classroom of middle school, this thesis discusses the relationship between Task-Based Teaching Model and the modern English class through analyzing the reading in middle school English class. The paper draws a conclusion that Task-Based Teaching Model is effective in middle school English classroom and it can improve students comprehensive ability to a all-round development level.
Correcting Mispronunciations in Speech using Spectrogram Inpainting

Learning a new language involves constantly comparing speech productions with reference productions from the environment. Early in speech acquisition, children make articulatory adjustments to match their caregivers' speech. Grownup learners of a language tweak their speech to match the tutor reference. This paper proposes a method to synthetically generate correct pronunciation feedback given incorrect production. Furthermore, our aim is to generate the corrected production while maintaining the speaker's original voice. The system prompts the user to pronounce a phrase. The speech is recorded, and the samples associated with the inaccurate phoneme are masked with zeros. This waveform serves as an input to a speech generator, implemented as a deep learning inpainting system with a U-net architecture, and trained to output a reconstructed speech. The training set is composed of unimpaired proper speech examples, and the generator is trained to reconstruct the original proper speech. We evaluated the performance of our system on phoneme replacement of minimal pair words of English as well as on children with pronunciation disorders. Results suggest that human listeners slightly prefer our generated speech over a smoothed replacement of the inaccurate phoneme with a production of a different speaker.
The Role of Breast Imaging in Pre- and Post-Definitive Treatment of Breast Cancer

Breast imaging is an integral part of breast cancer management. Many imaging modalities are available in assisting clinicians in the screening and detection of breast cancer.&nbsp; These can be broadly grouped under three categories: X-ray-based breast imaging, magnetic-field-based breast imaging, and ultrasound wave-based breast imaging.&nbsp; Mammography is the most used X ray-based breast imaging. This chapter provides an overview of various imaging modalities with emphasis on mammography, magnetic resonance imaging, and ultrasound. Their uses in pre-and post-treatment settings, along with their advantages and disadvantages are presented from an Indonesian perspective.
Implicit U-Net for volumetric medical image segmentation

U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combining a convolutional feature extractor with an implicit localization network, our implicit U-Net has 40% less parameters than the equivalent U-Net. Moreover, we propose training and inference procedures to capitalize sparse predictions. When comparing to an equivalent fully convolutional U-Net, Implicit U-Net reduces by approximately 30% inference and training time as well as training memory footprint while achieving comparable results in our experiments with two different abdominal CT scan datasets.