Reviewerly/exHarmony huggingface dataset:
Semantic Role Labeling Based on Valence Structure and Deep Neural Network

Semantic roles labeling is a kind of shallow semantic analysis. Existing Chinese semantic analysis methods and semantic roles labeling systems do not effectively characterize Chinese essential features, and it causes the currently larger difference between Chinese SRL systems and English SRL systems. Valence structures can better characterize syntactic structures and semantic constitution relations of Chinese sentences, so we incorporated the valence information of predicates into semantic roles labeling. Experimental results show that proper use of valence information significantly improves the performance of semantic roles labeling system: the verbal SRL approach achieves the performance of 93.69% in F1-measure and the nominal SRL approach achieves the performance of 79.23% in F1-measure on golden parse trees and golden predicates, and all outperform the state-of-the-art SRL systems. In recent years, end-to-end semantic role labeling based on deep neural networks has attracted more and more attention. However, current semantic role labeling methods use deep neural networks without language features. We propose a deep neural network model that integrates valence information, which was evaluated on the CoNLL-2005, CoNLL-2012 shared task datasets, and achieved better results than previous work: the performance of the CoNLL-2005 shared task dataset is improved 0.74 percentage points; the semantic role labeling methods proposed in this paper achieved an F1 value of 84.80% in CoNLL-2012 shared task datasets.
Understanding illness experiences of patients with primary sclerosing cholangitis (SOMA.LIV)

As a long-time wish, researchers always want to find a way to fuse human knowledge directly into machine models that can fulfill intelligent tasks. Existing researches attempted to approach this goal by manually labeling data or writing simple rules without considering the human's inherent ability to estimate uncertainty. These approaches cannot take full advantage of human abilities and hence make the overall system inefficient. In this paper, we propose a novel approach INFER that can distill knowledge from humans in the form of rules with uncertainty. Firstly, we propose a new paradigm of providing human intelligence by expressing human knowledge as uncertain rules. After obtaining the set of rules, we design a rule aggregation model and a classification model, these two models are jointly trained in a knowledge distillation framework with the uncertainty knowledge. We further improve the distillation process with a curriculum learning-based training method. Through these, we can directly extract knowledge from inaccurate human knowledge. Empirical results on four different tasks demonstrate our proposed INFER approach can significantly improve model performance. Furthermore, the proposed uncertainty can provide more information and be effective in refining rules.
Use of Varied Observation Methods With Word Square Lks In Improving Student's Understanding Of Animal Classification Materials

Biology education emphasizes providing hands-on experience. Therefore, students need to be helped to develop a number of skills so that they explore and understand their surroundings and themselves. The fact that is often found in the field is Biology learning which is centered on the teacher as a knowledge provider for students, the delivery of subject matter tends to be dominated by the lecture method. Students are less active in the learning process to build and discover themselves through interaction with their environment, so students only memorize facts from books and not from the results of discovering and building their own knowledge.
Fault Tolerant Hash Join for Distributed Systems

Nowadays, enterprises are inclined to deploy data processing and analytical applications from well-equipped mainframes with highly available hardware components to commodity computers. Commodity machines are less reliable than expensive mainframes. Applications deployed on commodity clusters have to deal with failures that occur frequently. Mostly, these applications perform complex client queries with aggregation and join operations. The longer a query executes, the more it suffers from failures. It causes the entire work has to be re-executed. This paper presents a fault tolerant hash join (FTHJ) algorithm for distributed systems implemented in Apache Ignite. The FTHJ achieves fault tolerance by using a data replication mechanism, materializing intermediate computations. To evaluate FTHJ, we implemented the baseline, unreliable hash join algorithm. Experimental results show that FTHJ takes at least 30% less time to recover and complete join operation when a failure occurs during the execution. This paper describes how we reached a compromise between executing recovery tasks for the least amount of time and using additional resources.
A dual-pathway network combining global and local representation enhanced automatic segmentation of hippocampus

Accurate segmentation of the hippocampus from MR brain images is a critical step for investigating early brain development. Unfortunately, the previous methods hippocampus segmentation is not suitable for subdividing hippocampus into multiple anatomically different subregions, such as head, body, tail, based on structural magnetic resonance imaging. The main problem is lack of discriminative and robust features representation for distinguishing the hippocampus from the surrounding subregions structures. To this end, we designed a dual-pathway network to learn the global and local representation from both global and local pathway. We proposed a global pyramid attention fusion module (GPAFM) in global pathway, which extracted coarse and global features representing the gist of the object. And a local feature aggregation module was adopted in local pathway to extract fine and local features representing the details for determining object categories. The experimental results have demonstrated that GPAFM successfully parcellated the hippocampus into head, body and tail subregions with high accuracy. Dice coefficient on three hippocampus datasets have further demonstrated the validity of the model. Compared with superior methods, the proposed method had better performance on parcellating the hippocampus subregions.
Perbandingan Metode K-NN dan SVM Berdasarkan Kinerja Pegawai

Limited qualified human resources cause employees not to do the job in accordance with the company's operational standards properly and correctly. At this time PT. XYZ does not have tools to identify employee performance, therefore researchers conduct research to assist PT. XYZ in classifying employee performance. The methods used in this study were K-NN and SVM with a sample of 873 PT. XYZ employee data. Based on the trials conducted, the K-NN method has the highest accuracy rate of 90.13%, 91% precision rate, and 98.95% recall rate. The most optimal number of neighbors (k value) for the K-NN method is 5 with an accuracy rate of 88.35%.
Benchmark for Investigating the Security in Software Development Phases

People, hardware, software, data, and networks are all components of an information system. Each component must be strong enough to allow the information system to operate quickly and reliably. Software systems are one of the most important components of information systems in comparison to all other components. Integrating security into software products and systems is a huge issue for software engineers. Including sophisticated and secure software management. According to the current networked environment, software has grown defenseless against both purposeful and unintentional malicious intent. The main disadvantage of software security flaws is a lack of vigilance in addressing objectified security during the Programme development process. Without much care, software security has long been a secondary issue.
Recent Trends in Sentiment Analysis using Different Machine Learning based Models: A Short Review

Innovation is transforming the entire world rapidly. Every person is using the internet, and it has become an everyday requirement. Nowadays, every person uses the Web 3.0 and shares knowledge and opinions through social networking sites such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Google}+$</tex> , Facebook, and Twitter. These opinions and thoughts are of great importance for first-time users, as they seek to get insights from these written texts to make their own decisions. This study's goal is to survey, make a comparison analysis, and present performance assessment data for all currently used machine learning approaches for sentiment analysis, making use of machine learning models (multinomial Naive Bayes, Support Vector Machines, and Logistic Regression).
The world of computer systems

Alignment with alignment guides automatically aligns pictures, charts and images with the text. The new discoverable layout options in Word 2013 make wrapping text easier to use. Most of the users are familiar with earlier versions of Word. The new versions are made to make work much easier and faster. However, the users are not aware of the advantages of the newest versions which are much convenient and compatible with the day to day work that we are involved with. Therefore understanding and being up to date with the new versions are essential for effective work performance. The assignment focuses on the special and unknown features of the Word 2013 to make the user aware of its advantages. In this we focused at main fifteen (15) special and unknown features that are useful for the user to carry out work effectively. The blog in MS Word 2013 provides a platform in creation of blogs in simple manner, even without having a vast knowledge. Also, it facilitates step by steps to user to complete the task without any hassle. Digital signature in advance mood facilitates to share the information in more secured Environment among the users. Also, macro in Word 2013 is advanced than earlier versions and while providing user Friendly environment. “Insert online Video” is a new feature in Word 2013 and we can create more interactive documents and watch an inserted online video without even leaving the document. Further Word 2013 has some image editing features that can create some nice images. Generally, we need some type of Reader like Adobe Reader to look at the PDF Read-only files. But in Office 2013 we can use Word to save in PDF format and open up PDF files, even make changes to those files and save them. Out of these features, One Drive provides the free storage facility online (Cloud) which act as a personal online hard drive. Collapse and expand feature especially helpful to work with long and complicated documents as it provides an outline of the document. Also the new feature of “present a document online” provides the accessibility to documents for remote users even without having word. Track changes feature is important to do suggested changes to the document.
IMAGE ANALYSIS OF 12 LEAD ELECTROCARDIOGRAM USING WAVELET TRANSFORMATION

The heart is one of the most important human organs. One of instruments to detect cardiac abnormalities is the electrocardiogram (ECG). This research tries to analyze ECG image in normal heart condition from ECG machine. The previous research related to the pre-processing process is the same, only at the feature extraction process look for peaks P, Q, R, S, T, heart rate, and Deviation-ST. While this research is the characteristic extraction process using wavelet transformation. The image of lead ECG 12 is processed using discrete wavelet transforms with decomposition up to ten levels, by searching for mean square error (MSE). The type of mother wavelet and the wavelet order used are Daubechies (db) with 1 (db1 (Haar)). The smallest MSE value decomposition results are obtained at the level 5, which are lead I, II, III, aVR, aVF, V4 and V5, lead V1 &amp; V2 on level 4, for aVL (level 9), V3 (level 7) and V4 (level 6). It is expected that such research can be followed up to the identification model of cardiac abnormalities using wavelets.
Attentional Composition Networks for Long-Tailed Human Action Recognition

The problem of long-tailed visual recognition has been receiving increasing research attention. However, the long-tailed distribution problem remains underexplored for video-based visual recognition. To address this issue, in this article we propose a compositional learning based solution for video-based human action recognition. Our method, named Attentional Composition Networks (ACN), first learns verb-like and preposition-like components, then shuffles these components to generate samples for the tail classes in the feature space to augment the data for the tail classes. Specifically, during training, we represent each action video by a graph that captures the spatial-temporal relations (edges) among detected human/object instances (nodes). Then, ACN utilizes the position information to decompose each action into a set of verb and preposition representations using the edge features in the graph. After that, the verb and preposition features from different videos are combined via an attention structure to synthesize feature representations for tail classes. This way, we can enrich the data for the tail classes and consequently improve the action recognition for these classes. To evaluate the compositional human action recognition, we further contribute a new human action recognition dataset, namely NEU-Interaction (NEU-I). Experimental results on both Something-Something V2 and the proposed NEU-I demonstrate the effectiveness of the proposed method for long-tailed, few-shot, and zero-shot problems in human action recognition. Source code and the NEU-I dataset are available at https://github.com/YajieW99/ACN .
Quantum dynamical speedup for correlated initial states

Abstract The maximal evolution speed of any quantum system can be expressed by the quantum speed limit time. In this paper, we consider a model in which the system has a correlation with the environment. The influence of the initial correlation between the system and environment on the quantum speed limit is investigated. It is shown that the appearance of non-Markovianity effects causes the speedup of quantum evolution. Moreover, we demonstrate the dependence of quantum dynamical speedup on the quantum coherence of the correlated initial state.
Researches Advanced in Intubation Based on SLAM

Intubation is an emergency medical procedure used to rescue people who are unconscious or unable to breathe on their own. In the process of tracheal intubation, traditional intubation is difficult due to narrow viewing angle, trachea bending, and occlusion of internal structures. Thanks to the rapid development of simultaneous localization and mapping (SLAM) and virtual reality (AR) technologies, intubation aids are also developing towards intelligence and automation. Based on the collection and analysis of a large number of literatures, this paper conducts an in-depth study of the existing SLAM-based intubation-assisted technology. Specifically, on the basis of analyzing the current research on the application of equipment, visualization schemes, and algorithm schemes in tracheal intubation and intubation training, this paper studies the application progress in terms of efficiency, versatility, accuracy, and user feedback. Further, we summarize the existing key issues and discuss future developments.
Abstract 5398: Optimization generator app (OpGen) for radiotherapy treatment planning using case-based reasoning

Abstract Case-based reasoning (CBR) means adopting previous experiences (i.e, data from previous patients) to meet new demands (new patients). In this scenario, we developed an application named OpGen to generate optimization objective values for head and neck radiotherapy inverse treatment planning, using the previously treated patient with an acceptable outcome. OpGen will be helpful in radiotherapy treatment planning automation and efficiently reducing the required time to reach the target goals and improving the quality and safety of the treatment.OpGen has been developed in Python and has four main modules: 1- Analyzing radiotherapy treatment planning parameters of the previous patients in the database. We have developed (a) a powerful graphical user interface (GUI) that allows quantitative and qualitative analysis of the previous knowledge, including the type of frequently used objective parameter (Min Dose, Max Dose, Min DVH dose, etc.); (b) The range of values used for each specific objective type, and (c) Histogram of the number of patients vs. values used for a specific objective type. 2- Importing patients from treatment planning and retrieving a user-defined number of treatment planning information from the previously treated patients in the database corresponding to the given patient using the user-defined quantitative features (geometrical, distance or geo-distance) and retrieval algorithms (k-Nearest Neighbor and Random Forest). 3- Generating optimization objective values based on the following available options: (a) The most similar patient in the database; (b)- Average or median value over the number of patients retrieved from the database; and (c) Deep learning generated values using a model trained over 100 similar cases retrieved from the database. 4- Generate a portable database of the objective values and patient geometric information for any inter- or intra-institutional usage of the algorithm. OpGen is compatible with RayStation (RaySearch Laboratories™) treatment planning and can retrieve, analyze and generate treatment planning optimization parameters in less than 3 minutes. OpGen can potentially increase the quality and safety of radiotherapy treatment planning by improving the efficiency of the optimization process. Citation Format: Reza Reiazi, Surendra Prajapati, Abdallah Sherif Mohamed, Clifton David Fuller, Mohammad Salehpour. Optimization generator app (OpGen) for radiotherapy treatment planning using case-based reasoning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5398.
Traffic Rules Violation Detection System

Abstract: The paper presents a system that uses surveillance videos to automatically detect bike riders without helmets by us-ing object segmentation and background subtraction techniques. A consolidation approach is introduced to improve accuracy, and three feature representations are compared. The system achieves a detection accuracy of 93.80 percentage and has an average processing time of 11.58 milliseconds, making it a cost-effective and real-time solution for managing traffic violations related to helmet usage
THE DEVELOPMENT OF CREATIVE THINKING OF THE INDIVIDUAL IN THE PROCESS OF LEARNING ENGINEERING GRAPHICS

The article deals with the development and formation of creative thinking of the individual in the process of teaching Engineering graphics, as well as the structure of classes aimed at developing the creativity of students. The proposed examples of graphic tasks in Engineering Graphics contain a problem, reveal new aspects of the object under study based on the analysis of the initial data, and bring students as close as possible to their professional activities.
Clop hackers are adapting to US law enforcement action

Headline INT: Clop hackers are adapting to US law enforcement
Potential ore possibility of rocks of the deposit Kalmakyr of Almalyk district (Middle Tien-Shan)

The article presents the mineralogical and geochemical features of the types and grades of ores of the porphyry copper deposit Kalmakyr. The chemical, mass spectrometric results of analyzes of selected samples are given. When describing polished sections, ore minerals and their structural and textural features were established. The polyformation and potential ore content of the rocks of the deep horizons of the Kalmakyr deposit was revealed. It has been determined that the change of copper mineralization with depth to copper-molybdenum is due to the change of diorite by monzonitoids. Gold and silver ores appear to be superimposed, young, and associated with basic-felsic dykes.
EXTRA COURSE ACTIVITIES IN CHEMISTRY AS A MEANS OF FORMING PROFESSIONALLY SIGNIFICANT QUALITIES OF PERSONALITY

The article discusses the professionally significant qualities of a person in the educational space in chemistry, their formation and development. The pedagogical experiment was carried out in educational institutions of the city of Voronezh on the example of extracurricular activities. Pedagogical technologies were used: projects, research activities, circle activities. A study ofprofessionally significant personality traits of students in dynamics was carried out.
COLLABORATIVE EDUCATION AS AN EFFECTIVE WAY TO IMPROVE THE TRAINING OF TEACHERS OF VOCATIONAL TRAINING

In the new educational conditions, taking into account the development of the scientific and technical sphere, various forms of cooperation between educational and industrial enterprises using digital technologies are of particular importance. The collaboration of organizations is consider revising in the article as a unifying element within the educational process. The article focuses on technological opportunities that allow combining knowledge, skills, experience, and resources to improve the effectiveness of vocational education.
THE PROCESS OF FORMATION OF PROFESSIONAL AND PERSONAL SELF-REGULATION OF FUTURE SPECIALISTS IN ADAPTIVE PHYSICAL CULTURE AND ITS PERFORMANCE INDICATORS

The article discusses the introduction and implementation of a conceptual model of formation of readiness for professional and personal self-regulation of future specialists in adaptive physical culture. The author presents the developed program of self-regulation ofprofessional activity of students, future specialists in the field of adaptive physical education. The article presents the results of a study of organized learning, taking into account indicators that characterize certain identified criteria for self-regulation. As a result of an experimental study, the author proved that the participation of students in optional training allows you to control the process of formation of readiness for professional and personal self-regulation, raising its level and developing the general level ofprofessionalism of future specialists in the field of adaptive physical education.
An analysis of awareness of media and information literacy among research scholars at Mahatma Gandhi Kashi Vidyapith University

Media and Information Literacy (MIL) places a strong emphasis on an essential literacy strategy that empowers readers to critically evaluate what they have read, heard, and understood. All educational levels demand it, but universities require it much more. The survey method and questionnaire instrument are used in the study's execution. The study's objectives are to determine the degree of MIL awareness among research scholars and to evaluate the utility and relevance of MIL among research scholars. The majority of responders are familiar with the term MIL, which helps them save time.