Reviewerly/exHarmony huggingface dataset:
Dilation-erosion for single-frame supervised temporal action localization

To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between actions and backgrounds, i.e., action incompleteness and background false positives. To tackle the two challenges, in this work, we present the Snippet Classification model and the Dilation-Erosion module. In the Dilation-Erosion module, we expand the potential action segments with a loose criterion to alleviate the problem of action incompleteness and then remove the background from the potential action segments to alleviate the problem of action incompleteness. Relying on the single-frame annotation and the output of the snippet classification, the Dilation-Erosion module mines pseudo snippet-level ground-truth, hard backgrounds and evident backgrounds, which in turn further trains the Snippet Classification model. It forms a cyclic dependency. Furthermore, we propose a new embedding loss to aggregate the features of action instances with the same label and separate the features of actions from backgrounds. Experiments on THUMOS14 and ActivityNet 1.2 validate the effectiveness of the proposed method. Code has been made publicly available ( https://github.com/LingJun123/single-frame-TAL ).
Optimizing Young Learners' Role Using Scratch as a Media in a Participative Learning Method

The 21st-century learning paradigm emphasizes the use of information and communication technology (ICT) that supports the students ability to think critically in order to solve the problem. In other words, ICT bridges science with the real world. Scratch, as one of the project’s web-based multimedia products, allows students to develop their creativity in an interactive and fun way. Scratch is used not only to create games but also to make projects such as story animations. Hence, the researchers intend to conduct a research and development with ADDIE approach to answer the challenges of the technological disruption era for young learners. Based on the research results, scratch is declared valid and suitable for use in learning to optimize young learners to increase their creativity.&#x0D; Keywords: scratch, ICT, development, young learners, optimize
Peramalan Kebutuhan Air: Analisis Debit Kebutuhan Air Bersih pada Masa Mendatang

Bengkulu City is one of the cities of Bengkulu Province. Bengkulu is classified as big city in terms of number of population with 374.694 people. Increasing number of populations effected the needs of water as basic necessity. However, enlargement of water needs contradictive with amount of water on global. The reduction in water catchment areas and the amount of pollution decreasing water that is suitable for consumption. Hence, this research aims to predict future water needs as a measure to anticipate a shortage of water supply. Forecasting of the amount of water demand in this study uses a descriptive quantitative method. The result shows the amount of water demand discharge in 2031 is 28.218 Litre/second. So, the amount of discharge of water demand in 2031 will increase by 0.61% from 2021.
Pendampingan Pembelajaran Bahasa Inggris melalui Contextual Teaching and Learning pada Orang Tua Murid

Giving guidance to young learners needs a specific strategy that fixes to their needs and world. This social service was conducted from May up to June for all parents who jlin in the organization of teachers and parents in SDIT Alif Smart Surakarta. We have three methods in conducting this activity: (1) Need Analysis, which was done by interviewing parents about their way of accompanying their children in learning English at home, and (2) Socialization about contextual teaching and learning in learning English. It was done by giving material explanation about the notion, steps, and implementation of contextual teaching and learning in learning English at home in accordance to their daily life; and (3) Feedback. This was done by giving feedback about the implementation of contextual teaching and learning in daily life English at home. At first, we coordinated the activity. And secondly, there’s a meeting to encourage all members to have motivation besides giving material for them. The material is all about the implementation of contextual teaching and learning in guiding children to learn English at home. In fact, we discussed how to help all children to learn English and deal with their daily life. There are also questions and answers about how to guide children to learn English effectively and how to solve problems in learning English.
Distributional Gaussian Processes Layers for Out-of-Distribution Detection

Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. Gaussian Processes can reliably separate in-distribution data points from out-of-distribution data points via their mathematical construction. Hence, we propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has not been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.
On teaching and the practice of implementing big data in higher educational institutions(applying hierarchical clustering to big data)

In modern society, the rapid growth in the volume of data streams stored in various computing devices creates a need for big data analysis and increases the demand for specialists with professional competencies, since the training of future specialists in the field of big data is associated with the need for a comprehensive study of software and hardware, environments. programming and integrating them into the educational process. To do this, this article proposed practicalwork necessary to improve the knowledge of students of higher educational institutions, the formation of new skills and abilities based on the storage, processing and analysis of large amounts of data in the R programming environment.
SelfCode: An Annotated Corpus and a Model for Automated Assessment of Self-Explanation During Source Code Comprehension

The ability to automatically assess learners' activities is the key to user modeling and personalization in adaptive educational systems.The work presented in this paper opens an opportunity to expand the scope of automated assessment from traditional programming problems to code comprehension tasks where students are requested to explain the critical steps of a program. The ability to automatically assess these self-explanations offers a unique opportunity to understand the current state of student knowledge, recognize possible misconceptions, and provide feedback. Annotated datasets are needed to train Artificial Intelligence/Machine Learning approaches for the automated assessment of student explanations. To answer this need, we present a novel corpus called SelfCode which consists of 1,770 sentence pairs of student and expert self-explanations of Java code examples, along with semantic similarity judgments provided by experts. We also present a baseline automated assessment model that relies on textual features. The corpus is available at the GitHub repository (https://github.com/jeevanchaps/SelfCode).
Sign Language Translator using Machine Learning for Communication with Deaf People

Sign language is an incredible advancement that has grown over the years.Unfortunately, the language has some drawbacks.Not everyone knows how to interpret sign language when conversing with deaf people.Communication using sign language is always necessary.Communication without an interpreter is difficult.To solve this, there is a need to develop a product that is versatile and robust, which can convert sign language so that ordinary people can understand it and communicate without barriers.The main aim of this paper is to break down barriers between deaf and non-deaf people and propose a system so that deaf and normal people can communicate with each other.
Improved DevOps Lifecycle by Integrating a Novel Tool V-Git Lab

Aims: we propose a tool that can automatically generate datasets for software defect prediction from GitHub repositories Background: DevOps is a software development approach that emphasizes collaboration, communication, and automation in order to improve the speed and quality of software delivery Objective: This study aims to demonstrate the effectiveness of the tool, and in order to do so, a series of experiments were conducted on several popular GitHub repositories and compared the performance of our generated datasets with existing datasets. Method: The tool works by analyzing the commit history of a given repository and extracting relevant features that can be used to predict defects. These features include code complexity metrics, code churn, and the number of developers involved in a particular code change. Result: Our results show that the datasets generated by our tool are comparable in quality to existing datasets and can be used to train effective software defect prediction models Conclusion: Overall, the proposed tool provides a convenient and effective way to generate high-quality datasets for software defect prediction, which can significantly improve the accuracy and reliability of prediction models.
AUTOMATIC SPEECH RECOGNITION: SPEECH TO TEXT CONVERTER

The current work presents a multilingual speech-to-text conversion system.Conversion is based on information in speech signal.Speech is the natural and most important form of communication for human being.Speech-To-Text (STT) system takes a human speech utterance as an input and requires a string of words as output.The objective of this system is to extract, characterize and recognize the information about speech.NLP is a subfield of Artificial Intelegence (AI) that focuses on enabling computers to understand ,interpret, and generate human language.Speech recognition software can translate spoken words into text using closed captions to enable a person with hearing loss to understand what others are saying.Speech recognition can also enable those with limited use of their hands to work with computers, using voice commands instead of typing .The main purpose of speech to text is to entertain ,to inform and to persuade Speech utterances are prerecorded and stored in a database.Database mainly divided into two parts testing and training.Samples from training database are passed through training phase and features are extracted.Combining features for each sample forms feature vector which is stored as reference.Sample to be tested from testing part is given to system and its features are extracted.Similarity between these features and reference feature vector is computed and words having maximum similarity are given as output.The system is developed in Python environment.
Visualizing the Lost Script of South Asia Under the Umbrella of the Digital Humanities Tool VisualEye

Brahmi Script - is one of the earliest scripts known to India and South Asia and it is also an ancestral script for all the indigenous scripts present in India. The researchers, historians, laymen, etc. need these sources of information systematically and handily. In the scholarly world, a huge amount of data and information (Digital Humanities based collections) related to Brahmi script is available but all this information is scattered in the form of texts, images, videos, etc. This creates chaos to understand the overall story of the Brahmi script from its inception to till date. With these issues, we felt motivated to present the data and information related to Brahmi Script among their users in a more informative, easily accessible, and comfortably understandable manner. In this paper, we have tried to connect all the dots related to Brahmi Script under one umbrella with the help of the Digital humanities tool – VisualEyes. Further, we have mapped these multimedia formats with each other on the historical timeline, interactive maps and story events to establish and understand the relationship among the various facets of the Brahmi Script. The impact of this research is more appealing as it has presented a new dimension for any other script to make it more accessible and easily understandable worldwide while using an innovative open-source tool.
FEATURES OF NATURAL SCIENCE IN NORWAY

The article argues the importance of studying the ways in which natural science in Norway is taught. The research findings could help to modernize the natural science curriculum in Ukraine since all reforms are more or less related to integration. Ukraine, taking into account the proposals of the European Commission, has identified the science, engineering and technology competence as one of the key competences. The expected outcomes include the ability to apply the appropriate set of knowledge and methodologies to explain the natural world, to determine and make conclusions based on the information received, and to be responsible for the consequences of the human activity. The integration approach of the subject areas is relatively new for schools in Ukraine, so studying the experience of developing the natural science curriculum in Norway allows understanding the opportunities offered by integration, since a characteristic feature of science curriculum in general education schools in Norway is an integrated course of biology, chemistry, physics, and geography taught in the first through the eleventh grade. Based on the analysis of psychological and pedagogical publications the features that characterize science curriculum in Norway have been identified. They include: the constant reformation that the science curriculum undergoes aiming at constant improvements to provide for the country’s economic, cultural and social development; the science curriculum is centralized; the content of physics, biology, chemistry, and geography is integrated in the natural science curriculum in Grade 1 to Grade 11 in lower secondary school and upper secondary school; the science curriculum content is comprehensive and coherent; the content is organized in the five subject domains and expected competencies are outlined; «family resemblance approach» is used to integrate the subject areas and to teach the nature of science – it motivate students to develop better attitude to the subject; the inquiry-based learning technology is used as a teaching method; when developing the natural science curriculum the main trends in the society development are taken into account. The development of science education in Norway is characterized by the acceleration of education reform under the influence of globalization factors. The analysis of the natural science curriculum in Grade 1 to Grade 11 is likely to give the perspective for the future studies.
Tool for Management of Human And Robot using Medical ChatBot

A decent existence requires access to quality healthcare.It is extremely important to our day-to-day existence.Nevertheless, getting a doctor's appointment for each health issue is exceedingly difficult.The goal is to create a medical chatbot using computer science that will propose a doctor who specializes in a certain ailment.This can make it easier to increase access to medical data via chatbots.Chat bots are software applications that converse with users by using language.The chatbot keeps the data within the data to identify the sentence keywords, create a call to action, and respond to the question.Conversational User Interfaces (CUIs) let users interact with computers in a direct, human-like way.The way we engage with computers and applications is radically altered.CUI is utilized in this study to train data and a variety of packages that help us provide suitable satisfactory outcomes For the purpose of recovering findings, we will combine machine Learning with Natural Language Processing in this chat bot.Care plays an important role in our daily lives; whenever someone is ill, they visit their general practitioner or a nearby clinic to learn more about the issues they are facing.In recent years, a number of organizations and businesses have worked with hospitals to produce support that could help doctors and medical staff deal with patients in a better manner and reduce their labor by using technology; not only does this help the project's main goal is to help you convey information that you have mined more effectively (information) using Access to timely, easy, contextual information is required for both customers and staff.Keywords: Chat Bot System which can make suggestions to help people with correct decision making without making a failurre of treatment in initial stages of curing disease for hospital and people benefits.Making Faster Progress and results of health development in good side.
Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.
LightAD: Accelerating AutoDebias with Adaptive Sampling

In recommendation systems, the bias issue is ubiquitous as the data is collected from user behaviors rather than reasonable experiments. AutoDebias, which resorts to meta learning to find appropriate debiasing configurations, &lt;i&gt;i.e.&lt;/i&gt;, pseudo-labels and confidence weights for all user-item pairs, has been demonstrated as a generic and effective solution in tackling various biases. Nevertheless, setting pseudo-labels and weights for every user-item pair can be a time-consuming process. Therefore, AutoDebias suffers from a huge computational cost, making it less applicable to real cases. Although stochastic gradient descent with a uniform sampler can be applied to accelerate training, it would significantly deteriorate model convergence and stability. To overcome this problem, we propose LightAutoDebias (short as LightAD), which equips AutoDebias with a specialized importance sampling strategy. The sampler can adaptively and dynamically draw informative training instances, which brings provably better convergence and stability than the standard uniform sampler. Extensive experiments on three benchmark datasets validate that our LightAD accelerates AutoDebias by several magnitudes while maintaining almost equal accuracy.
A Jupyter Notebook Based Tool to Make Screening and Primary Evaluations of Students' Spreadsheet Files Submitted on Learning Management Systems

We have built a prototype of a tool to help teachers to grade spreadsheet files which learners submitted as answers to an assignment on a learning management system such as Moodle. The tool allows teachers to upload all the students' files and shows the list and the visualized images of how each file differs from the correct answer file which the teacher specified. The tool was created as a Jupyter Notebook for teachers to easily and instantly modify according to teachers' needs. It runs on JupyterLab and is also made compatible to Google Colab and Google Drive so that it is usable without prior preparation for any users.
Practical high-speed Gaussian coherent state continuous variable quantum key distribution with real-time parameter monitoring and post-processed key distillation

We demonstrate Gaussian modulated continuous variable quantum key distribution at 50MHz symbol rate. Unlike most demonstrations, we record received signals in real-time and distil keys, producing a record 3Mb/s key rate after 25km transmission.
Research on the Criminal Imputation Path of Network Service Providers

Network service providers have taken on a more prominent part in the process of Internet governance in recent years as one of the important subjects of cyberspace. Criminal justice dilemmas like ambiguous semantics of network service providers, a failure to distinguish between different types of network service providers, and difficulties in convicting network service providers frequently arise when it becomes necessary to assess the criminal law of network service providers. In this paper, the author analyzes the reasons behind the dilemma through the legal norm analysis method, such as the diversification of network services and the high-speed development trend of network technology, the difficulty of unifying the classification standards, and the large differences in the types of network services, and the controversial application of new crimes. Based on this, with reference to the relevant provisions of foreign countries, and based on the existing legal environment in China, network service providers should be divided into two types from the subjective and objective aspects. At the same time, the specific semantics, scope of obligations, and scope of criminal imputation under their respective types should be clarified in order to achieve a more reasonable and feasible path of criminal liability fixation, so as to help the judicial authorities define the subject of network service providers more efficiently, and conviction and sentencing more accurately.