Reviewerly/exHarmony huggingface dataset:
The Effect of Wound Treatment Using Honey on Colonization of Staphylococcus Aureus Bacteria in Diabetic Wounds in Patients with Diabetes Mellitus in the Work Area Banjarmasin Health Center

Background : Diabetic wounds are very easy to experience complications in the form of infection due to bacterial invasion, and sugar conditions blood which tall encourage the growth of bacteria.Bacteria that can cause infection in diabetic wounds wrong one is staphylococci aureus . Wound diabetes can treated with honey. Honey contains antibacterial , antioxidant and hydrogen peroxide properties that help kill bacteria dangerous. Objective : The purpose of this study was to determine the effect of the wound dressing contains honey against bacterial colonization Staphylococcus aureus on wound diabetic . Methods : The research design used is pre-experimental with o ne-group pre-test and post-test design . Sampling method using consecutive sampling as many as 7 respondents. Results : Analysis data use test dependent t-test and obtained score p value 0.000 ( p value &amp;lt; = 0.05), so could concluded there is influence care wound use honey to colonization bacteria Staphylococcus aureus in wounds diabetic Diabetes Mellitus patients in the region work Public health center Banjarmasin . Conclusion : best Use honey as product care wound because nature the antibacterial which could prevent infection and speed up process healing wound .
Performance Evaluation of Supervised ML Algorithms for Elephant Flow Detection in SDN

Software-defined networking (SDN) improves the network management due to the separation of the network control plane from the packet forwarding plane. However, with the increase in data traffic, SDN architectures have raised several challenges in terms of traffic engineering, QoS, and network management. Therefore, it is crucial to develop an intelligent system to classify the flows and predict future traffic. Indeed, in order to propose an adequate forwarding strategy for various flow types (particularly elephant flows (EFs)) in an SDN environment, an accurate flow detection system is required. Hence, in this paper, we propose a model-based SDN controller that includes machine learning algorithms to detect large-size traffic and forward it. Moreover, we represent a comparative simulation to evaluate the performance of some supervised machine learning algorithms such as Naive Bayes (NB), K-Nearest neighbors (KNN), Logistics regression (RL), Support Vector Machine (SVM), and Decision Tree (DT), to detect the elephant flow. A decision tree (DT) and K-Nearest neighbors (KNN) are the best candidate machine learning algorithms in elephant flow detection with an accuracy of 99%.
Law as Counterspeech

Abstract A growing body of work in free speech theory is interested in the nature of counterspeech, i.e. speech that aims to counteract the effects of harmful speech. Counterspeech is usually defined in opposition to legal responses to harmful speech, which try to prevent such speech from occurring in the first place. In this paper we challenge this way of carving up the conceptual terrain. Instead, we argue that our main classificatory division, in theorising responses to harmful speech, should be between pro-discursive and anti-discursive responses. Some legal responses to harmful speech, so we argue, make a positive discursive contribution in their own right. That is, legal restrictions on harmful speech can have a function that is importantly similar to speech that aims at countering the effects of harmful speech.
Bank role in preventing money laundering and cyber security

The purpose of this study is to examine the effect of AML disclosure and cyber security on bank performance and to test whether intellectual capital strengthens or weakens the effect of AML disclosure and cyber security on bank performance. This study uses a sample of banking companies listed on the Indonesia Stock Exchange (IDX). The sample used is 41 banking companies listed on the Indonesian stock exchange. The findings of this study are (1) there is an effect of AML disclosure on Bank Performance. (2) there is no influence of cyber security on bank performance. The limitations of this research do not cover all types of banking in Indonesia, there are still many banking companies in Indonesia but they are not listed. The regulatory implications related to money laundering are the implementation of anti-money laundering programs in the financial services sector could be extended become a disclosure.
Better-than-classical Grover search via quantum error detection and suppression

Grover's search algorithm is one of the first quantum algorithms to exhibit a provable quantum advantage. It forms the backbone of numerous quantum applications and is widely used in benchmarking efforts. Here, we report better-than-classical success probabilities for a complete Grover search algorithm on the largest scale demonstrated to date, of up to five qubits, using two different IBM superconducting transmon qubit platforms. This is enabled, on the four and five-qubit scale, by error suppression via robust dynamical decoupling pulse sequences, without which we do not observe better-than-classical results. Further improvements arise after the use of measurement error mitigation, but the latter is insufficient by itself for achieving better-than-classical performance. For two qubits, we demonstrate a success probability of 99.5% via the use of the [[4,2,2]] quantum error-detection (QED) code. This constitutes a demonstration of quantum algorithmic breakeven via QED. Along the way, we introduce algorithmic error tomography, a method of independent interest that provides a holistic view of the errors accumulated throughout an entire quantum algorithm, filtered via the errors detected by the QED code used to encode the circuit. We demonstrate that algorithmic error tomography provides a stringent test of an error model based on a combination of amplitude damping, dephasing, and depolarization.
Reducing the qubit requirement of Jordan-Wigner encodings of $N$-mode, $K$-fermion systems from $N$ to $\lceil \log_2 {N \choose K} \rceil$

To simulate a fermionic system on a quantum computer, it is necessary to encode the state of the fermions onto qubits. Fermion-to-qubit mappings such as the Jordan-Wigner and Bravyi-Kitaev transformations do this using $N$ qubits to represent systems of $N$ fermionic modes. In this work, we demonstrate that for particle number conserving systems of $K$ fermions and $N$ modes, the qubit requirement can be reduced to the information theoretic minimum of $\lceil \log_2 {N \choose K} \rceil$. This will improve the feasibility of simulation of molecules and many-body systems on near-term quantum computers with limited qubit number.
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.
Empirical indistinguishability: From the knowledge structure to the skills

Recent literature has pointed out that the basic local independence model (BLIM) when applied to some specific instances of knowledge structures presents identifiability issues. Furthermore, it has been shown that for such instances the model presents a stronger form of unidentifiability named empirical indistinguishability, which leads to the fact that the existence of certain knowledge states in such structures cannot be empirically tested. In this article the notion of indistinguishability is extended to skill maps and, more generally, to the competence-based knowledge space theory. Theoretical results are provided showing that skill maps can be empirically indistinguishable from one another. The most relevant consequence of this is that for some skills there is no empirical evidence to establish their existence. This result is strictly related to the type of probabilistic model investigated, which is essentially the BLIM. Alternative models may exist or can be developed in knowledge space theory for which this indistinguishability problem disappears.
Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use

Clinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which are crucial for predicting key outcomes in patients in acute care. In this study, we propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information to predict outcomes in the intensive care unit (ICU). This information is complementary to typically used vital signs and laboratory test results.In this study, we developed a novel phenotype annotation model to extract the phenotypical features of patients, which were then used as input features of predictive models to predict ICU patient outcomes. We demonstrated and validated this approach by conducting experiments on three ICU prediction tasks, including in-hospital mortality, physiological decompensation and length of stay (LOS) for over 24 000 patients using the Medical Information Mart for Intensive Care (MIMIC-III) dataset.The predictive models incorporating phenotypical information achieved 0.845 (area under the curve-receiver operating characteristic (AUC-ROC)) for in-hospital mortality, 0.839 (AUC-ROC) for physiological decompensation and 0.430 (kappa) for LOS, all of which consistently outperformed the baseline models using only vital signs and laboratory test results. Moreover, we conducted a thorough interpretability study showing that phenotypes provide valuable insights at both the patient and cohort levels.The proposed approach demonstrates that phenotypical information complements traditionally used vital signs and laboratory test results and significantly improves the accuracy of outcome prediction in the ICU.
Discussion on English Cultural Communication and English Teaching in the New Media Environment

As a global language, English has become the main foreign language teaching choice in all countries. After decades, great progress has been made in English teaching in China. English has not only been popularized, but also a large number of English professionals from various industries have been trained. In the new media environment, in order to better improve the quality of English teaching, teachers should pay attention to the integration of English culture and teaching content when teaching English. By analyzing the characteristics of English teaching in the new media environment, this paper puts forward the relationship between English cultural exchange and English teaching, and puts forward strategies.
DIGITAL COMPETENCE AMONG RESEARCH SCHOLARS IN RELATION TO THEIR ACADEMIC ACHIEVEMENT AND SELECTED DEMOGRAPHIC VARIABLES

In the present study digital competence and academic achievement of 100 research scholars of various streams of Punjabi University,Patiala were studied.Teachers' Digital Competence Scale by Ramkrishna (2017) was used to collect the data pertaining to the digital competence of research scholars and one demographic data sheet developed by the investigators was used to collect information about gender, stream of study,experience of research work and academic achievement. Gathered data were analyzed by using the statistical techniques.The results show that the level of digital competence of research scholars has various levels and no research scholar possess high level of digital competence. Academic achievement of research scholars has also various levels. There is no significant difference in digital competence of research scholars with regard to gender and research experience. There is significant difference in digital competence of research scholars with regard to stream of study.Research scholars from the stream of education and languages are less competent in digitalization as compared to the research scholars from the stream of engineering, management &amp; science. Moreover, the high academic achievers are more digitally competent as compared to low academic achievers. Further, there is significant and positive relationship between digital competence and academic achievement.So we conclude that the high digital competence enhance the academic achievement
Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Dance emotion recognition is an important research direction of automatic speech recognition, especially in the robot environment. It is an important research content of dance emotion recognition to extract the features that best represent speech emotion and to construct an acoustic model with strong robustness and generalization. The dance emotion data set is small in size and high in dimension. The traditional recurrent neural network (RNN) has the problem of long-range dependence disappearance, and due to the focus on local information of convolutional neural network (CNN), the mining of potential relationships between frames in the input sequence is insufficient. To solve the above problems, this paper proposes a novel linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory (LSTM) for dance emotion recognition. In this paper, the linear prediction coefficient (LPC) and Meier frequency cepstrum coefficient (MFCC) are combined to obtain a new feature, namely the linear prediction Meier frequency cepstrum coefficient (LPMFCC). Then, the combined feature obtained by combining LPMFCC with energy feature is used as the extracted dance feature. The extracted features are input into the bidirectional LSTM network for training. Finally, support vector machine (SVM) is used to classify the obtained features through the full connection layer. Finally, we conduct experiments on public data sets and obtain the better effectiveness compared with the state-of-art dance motion recognition methods.
EXPERIMENTAL VERIFICATION VERIFICATION OF CIVIL RESPONSIBILITY FOR STUDENTS IN LABOR EDUCATION

The article considers the problem of forming civic responsibility of students in the lessons of labor training. The phenomenon of civic responsibility is defined as an integrative manifestation of a number of its constituent components: socially active life position; independence, persistence, self-analysis, self-control and self-organization in public relations; compliance with civil norms and values; honesty; willingness to take responsibility for their own actions; appropriation of norms and principles of public morality; national and planetary consciousness. It is concluded that the indicator of students’ acquisition of optimal levels of civic responsibility is their mastery of its constituent components. Criteria for the formation of civic responsibility are determined, which determine the degree of mastery of its components by students on the basis of productivity of their expression in educational activities and development of civic qualities of students: knowledge and understanding of civic norms and values; formation of value attitudes to the components of civil responsibility; activity of actions in application of practical experience of civil responsibility. On the basis of reasonable criteria and indicators of the formation of civic responsibility of students, the levels of its formation are determined: high, sufficient, medium and initial. The effectiveness of the formation of civic responsibility of primary school students in the lessons of labor training was revealed experimentally. Positive growth dynamics was found at three levels of civic responsibility (sufficient, medium and initial), and regression was established at a high level. Positive dynamics of growth of the initial level and negative regression at a high level of civic responsibility indicate that the existing traditional approaches to the formation of civic responsibility of primary school students in labor training are ineffective and require conceptual adjustment and appropriate organization of specially selected teaching methods.
Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms

The face plays an essential role in identifying people and showing their emotions in society. The human ability to recognize faces is remarkable. But face recognition is a fundamental problem in many computer programs. Due to the inherent complexities of the face and the many changes in its features, different algorithms for face recognition have been introduced in the last 20 years. Face recognition methods that are based on the structure of the face are unsupervised methods that produce good results compared to the linear changes that occur in the image. In this article, the Gabor algorithm, which is the origin of face recognition algorithms, has been described. Over the past decade, most of the research in the area of pattern classification has emphasized the use of the Gabor filter bank for extracting features. Because the Gabor algorithm has shortcomings, researchers have introduced a new method that is a combination of Gabor and PCA. After the introduction of the Gabor method, more complete and accurate algorithms have been introduced, such as Boosting algorithms, which we have briefly explained in this article. Also, here are the results of the comparison made by the researchers between Boosting and Gabor algorithms. The results show that Boosting-based algorithms have performed better compared to Gabor-based algorithms.
Digital Platforms in a Distance Learning Environment: An Educational Trend or the Need of the Hour?

The COVID-19 pandemic has demonstrated the promise of distance education using digital technologies. For Ukraine, this method of education has gained additional importance, since after the beginning of the unprecedented for the 21st century. Russian military aggression, the appeal to distance education technologies has become relevant. The purpose of the article is to analyze digital distance learning platforms, determine their effectiveness and prospects for further use. The work uses general scientific research methods (analysis, synthesis, induction, deduction). At the same time, the research is based on the principles of generalization, specification and abstraction. With the help of the comparison method, it was possible to compare the use of digital distance learning platforms in Ukraine, the USA and Saudi Arabia, where specific empirical data are presented. The research also used the axiomatic method, which provides for the ascent from theoretical statements (axioms) to specific conclusions. The conclusions concluded that distance learning is cheaper and more productive because it leaves more time for students to improve themselves and implement their own projects, and for teachers to conduct scientific work. The example of Saudi Arabia shows that a large number of students can be reached through the use of digital technologies and distance learning. This experience is useful for Ukraine. It is recommended to develop digital technologies of distance education in Ukraine, because its use will make it possible to save budget funds, guaranteeing the maximum involvement of teachers and students in the educational space.
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking

Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all history information, the dialogue state in the last turn is typically adopted as the input for predicting the current state by DST models. However, these models tend to keep the predicted slot values unchanged, which is defined as state momentum in this paper. Specifically, the models struggle to update slot values that need to be changed and correct wrongly predicted slot values in the last turn. To this end, we propose MoNET to tackle state momentum via noise-enhanced training. First, the previous state of each turn in the training data is noised via replacing some of its slot values. Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values. Furthermore, a contrastive context matching framework is designed to narrow the representation distance between a state and its corresponding noised variant, which reduces the impact of noised state and makes the model better understand the dialogue history. Experimental results on MultiWOZ datasets show that MoNET outperforms previous DST methods. Ablations and analysis verify the effectiveness of MoNET in alleviating state momentum and improving anti-noise ability.
Efficient brain age prediction from 3D MRI volumes using 2D projections

Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20 - 50 seconds using a single GPU, which two orders of magnitude faster compared to a small 3D CNN. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.
THE COMPARATIVE STUDY ANALYSIS LOGICAL FILES RECOVERY AND LOW LEVEL FILES RECOVERY USING DIGITAL FORENSIC METHODS

The development of application and network technology is currently so rapid. This technology is widely used as a solution to overcome data inspection problems in the field of Digital Forensics. The importance of Digital Forensics as evidence, especially on computers and mobile devices, is increasing, along with the rapid use of these mobile devices in daily communication. Data and files on computers and smartphones can be deleted intentionally or unintentionally. To recover the data, it is necessary to have Files Recovery. Furthermore, Logical Files Recovery can be made through password encryption, obtained through hack/attack password applications such as Password or even hacked with the "RecoveryMyFile" application. In addition, a search with a file that has a password description can be done. One of the file recovery applications that can be used is the Agent ransack search application, which is more powerful and convenient than Windows Search, which is more complicated if you need to search faster, indexing needs to be done, and restarts. By using Files Recovery analysis, the development of file or data deletion crimes can be overcome with increasingly rapid and advanced information technology, in this case through applications that can be used for file recovery and to restore deleted files.