Reviewerly/exHarmony huggingface dataset:
Learning with Holographic Reduced Representations

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors \cite{Plate1995} by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differentiable component of a deep learning architecture. HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop an output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach.
A Two-Stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation

Kidney cancer is aggressive cancer that accounts for a large proportion of adult malignancies. Computed tomography (CT) imaging is an effective tool for kidney cancer diagnosis. Automatic and accurate kidney and kidney tumor segmentation in CT scans is crucial for treatment and surgery planning. However, kidney tumors and cysts have various morphologies, with blurred edges and unpredictable positions. Therefore, precise segmentation of tumors and cysts faces a huge challenge. Consider these difficulties, we propose a cascaded deep neural network, which first accurately locate the kidney area through 2D U-Net, and then segment kidneys, kidney tumors, renal cysts through Multi-decoding Segmentation Network (MDS-Net) from the ROI of the kidney. We evaluated our method on the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) dataset. The method achieved Dice score, Surface Dice and Tumor Dice of 69.4%, 56.9% and 51.9% respectively, in the test cases. The model of cascade network proposed in this paper has a promising application prospect in kidney cancer diagnosis.
E-Learning for English for Business-Based Podcast: One of Learning Solutions Amid the Pandemic of COVID-19.

This research used qualitative methods of descriptive where information was required directly and where time and resources were limited It was designed to investigate whether e-learning podcast-based could be used in learning to listen in English for students of the University of Bina Sarana Informatika Management Study program This research used samples that are 6 classes as a source of research data with the 2nd-semester students who came from Management study program The number of 1 average class consisted of 20-25 students Researchers used the Android-based Anchor Application as a podcast maker as well as Google Classroom as an E-Learning learning medium To collect data,researchers used observations, interviews, and documentation Interviews were conducted by giving 25 questions through online based on theoretical approaches related to this study with 90 sample data Researchers used test instruments with pre-test and post-test as research instruments This research was focused on the use of ICT in the learning process by way of e-learning Podcasts based Thenovelty of this research is this study used a different target and numbers of the target from the previous studies, namely Management Program Students who only get Basic English teaching during lectures As a result of this research was the use of podcasts more effective than e-learning without using the media podcast, characterized by a grade average result of 76 05 after Post tests were done © 2020 Published under licence by IOP Publishing Ltd
Rain Prediction Clustering in Australia Using the K-Means Algorithm in the WEKA and RStudio Application

Purpose: The purpose of this study is how to create an ideal cluster in predicting rainfall in Australia based on the percentage of the sum of squares error (SSE) using the K-Means algorithm with WEKA and RStudio applications. Design/methodology/approach: The method or stages applied in predicting rain in Australia are through several stages including Data Collection, Data Pre-processing (including Missing Value handling in it), Data Mining Modeling by applying the K-Means Clustering algorithm using WEKA and RStudio, Validation results with SSE as well as Data Visualization using plots. Findings/result: Based on the results obtained, clusters of 2 with an SSE of 28.0% are ideal clusters for predicting rain in Australia. In the WEKA software, rain clusters are represented by blue nodes, and non-rainy clusters are represented by red nodes. While in the RStudio software, rain clusters are represented by black nodes and non-rainy clusters are represented by red nodes. Originality/value/state of the art: Get the ideal cluster in predicting rainfall in Australia by comparing the results obtained using the WEKA and RStudio applications.
Optimized Text Summarization using Customized Recurrent Neural Network Model

In current era, the growth of big data is raising in large scale and it becomes tough to the users to load and draw conclusions from it. An efficient approach is required to generate short and precise summaries in user required form. In recent years, Sequence to Sequence models gained lot of focuson text summarization to handle various challenges like fluency, human readability and also generation of optimistic summaries. The proposed approach effectively handled these issues using Customized Recurrent Neural Networks (C-RNNs) model to generate optimistic text summary. Theproposed model generated optimized text summaries where the data collected from social media/E-Commerce sites. C-RNN model are highly demanded for entrepreneur’s and consumers when they need precise text summaries. Experimental results show that proposed model is outperformedthe state of the art models in terms of syntactic and semantic structure and achieves qualitative results.
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules

One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users’ commands, a task trivial for humans due to their common sense. In this paper, we propose a zero-shot commonsense reasoning system for conversational agents in an attempt to achieve this. Our reasoner uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal). Our reasoner uses a state-of-the-art transformer-based generative commonsense knowledge base (KB) as its source of background knowledge for reasoning. We propose a novel and iterative knowledge query mechanism to extract multi-hop reasoning chains from the neural KB which uses symbolic logic rules to significantly reduce the search space. Similar to any KBs gathered to date, our commonsense KB is prone to missing knowledge. Therefore, we propose to conversationally elicit the missing knowledge from human users with our novel dynamic question generation strategy, which generates and presents contextualized queries to human users. We evaluate the model with a user study with human users that achieves a 35% higher success rate compared to SOTA.
Optimization of Relevance Weighting Algorithm Based on Hadoop Platform in Human Resource Information System

This paper introduces the relevance weighting algorithm based on the Hadoop platform to optimize the human resource information system, improve the human resource management process, reduce business workload, and improve work efficiency. First, analyze and design a system for human resource information management through the correlation weight algorithm on the Hadoop platform. Realize parameter setting, partition management, staff management, user management and production management. The experimental results show the effectiveness of this system.
Emergent Discrete Communication in Semantic Spaces

Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do alone. However, the current standard of using one-hot vectors as discrete communication tokens prevents agents from acquiring more desirable aspects of communication such as zero-shot understanding. Inspired by word embedding techniques from natural language processing, we propose neural agent architectures that enables them to communicate via discrete tokens derived from a learned, continuous space. We show in a decision theoretic framework that our technique optimizes communication over a wide range of scenarios, whereas one-hot tokens are only optimal under restrictive assumptions. In self-play experiments, we validate that our trained agents learn to cluster tokens in semantically-meaningful ways, allowing them communicate in noisy environments where other techniques fail. Lastly, we demonstrate both that agents using our method can effectively respond to novel human communication and that humans can understand unlabeled emergent agent communication, outperforming the use of one-hot communication.
Analysis of teenagers' preferences and concerns regarding HMDs in education

Virtual reality (VR) has become a powerful and promising tool for education, and numerous studies have investigated the application and effectiveness of VR education. However, few studies have focused on the expectations and concerns of teenagers regarding head-mounted displays (HMDs), which are used for this purpose. In this paper, we aim to explore the current problems and necessary advancements required in VR education based on a survey of 163 senior high school students who experience VR educational content for 1h. The usability and comfort of the HMD system, the physical and psychological effects on the students, and their preferences and concerns are investigated. The results show that HMDs increase students' interest, concentration, and enthusiasm for learning. However, isolated virtual environments make students feel nervous and afraid. The immersive environment also makes them worry about VR addiction and confusing the physical world with the virtual one. VR has great potential in the field of education, but the issue of safety needs to be considered in the future.
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning

Mapping natural language instructions to programs that computers can process is a fundamental challenge. Existing approaches focus on likelihood-based training or using reinforcement learning to fine-tune models based on a single reward. In this paper, we pose program generation from language as Inverse Reinforcement Learning. We introduce several interpretable reward components and jointly learn (1) a reward function that linearly combines them, and (2) a policy for program generation. Fine-tuning with our approach achieves significantly better performance than competitive methods using Reinforcement Learning (RL). On the VirtualHome framework, we get improvements of up to 9.0% on the Longest Common Subsequence metric and 14.7% on recall-based metrics over previous work on this framework (Puig et al., 2018). The approach is data-efficient, showing larger gains in performance in the low-data regime. Generated programs are also preferred by human evaluators over an RL-based approach, and rated higher on relevance, completeness, and human-likeness.
Copyright Under the Criminal Law of Foreign Countries

The article successively considers the rules establishing criminal sanctions for the copyright infringement in different countries. The features of the regulation of each of the analyzed countries are given. The need for domestic legislators to pay more attention to the positive practices of foreign counterparts is substantiated.
Examining the Pedagogical Best Practices of Student Mastery of Cyber Security in Higher Education

The intricacy of information security management (ISM) has become a major issue for organizations as Cybercrime is on the rise and cybercriminals are only getting better at what they do. The skills gap is growing between the people who hack and the people who could help protect against cybersecurity vulnerabilities of new technologies, such as drones and autonomous vehicles. As threats that exploit vulnerabilities in our cyber infrastructure grow and evolve, an integrated cybersecurity workforce must be capable of designing, developing, implementing, and maintaining defensive and offensive cyber strategies. The role of higher educational institutions (HEI) preparing students in cyber security workforce development could not have been more important as institutions are finding themselves with the challenge of preparing a workforce for a battle that is apparently hard with which to keep pace. The purpose of this study qualitative research was to determine best practices in cybersecurity competencies and teaching pedagogies in higher education. The study sought to find the best pedagogies being used in cybersecurity programs in institutions designed as Centers of Academic Excellence (CAE) to prepare students. The study also sought to determine specific pedagogical best practices depending upon type of competency. Interviews were conducted with cybersecurity educators from eight (8) colleges and universities in Alabama and Tennessee who are members of the National Centers of Academic Excellence (CAE) program in Cyber Defense (CD). The interviews data collected were kept confidential. The results from this study indicate that while cybersecurity competencies can be achieved through all the four (4) pedagogical themes: hands-on virtual reality online; hands-on with instructor; mixed or blended learning; and flipped classroom learning, hands-on with virtualization led by an instructor stands out as the best pedagogy.
Designing Knowledge Management System with Big Data for Data and Information Center Ministry of Health Indonesia

Sharing and capture knowledge is one of the important indicators in the ministry of health. The role of staff’s knowledge is a benchmark in determining service performance and quality, in this case the Knowledge Management System (KMS) is a solution in supporting the process of knowledge capture and knowledge sharing. However, the growth of innovation in knowledge management is also required in practice. The implementation of big data into the Knowledge Management System plays a role in extracting, transforming, loading, which can help the information system development division and this information technology and infrastructure management subdivision to collect, filter and combine data from various sources. The results of the KMS design are in the form of a site-based application that has a wiki module, discussion forums and document management to share medical experiences and there are search features needed by staff. Keywords—Knowledge, Knowledge Management, KMS, Big Data
Penerapan Metode Pembelajaran Somatic Auditory Visual Intellectual (SAVI) Untuk Meningkatkan Kemampuan Menulis Karangan Sederhana Kelas III SD

This research is written because of the low of students’ writing skills on simple writing. It is caused by the unvaried of learning method. This research is conducted to solve that problem. The used method in the research is Somatic Auditory Visual Intellectual (SAVI) on learning process. The objective of this thesis is to analyze, describe and find out the application of SAVI method for the 3rd grade of Elementary School and how is the improvement of their writing skills on simple writing of the 3rd students of Elementary School by using SAVI method. The type of this research is Classroom Action Research with Kemmis and Taggart design research which has four steps; planning, action, observation, and reflection. This research is conducted using two cycles with qualitative data analysis technique through data reduction, data presentation and verification. Whereas for quantitative data analysis technique uses data processing and students’ worksheet. The research subject is the 3rd students of State Elementary School in Bandung. The research instrument consists of Lesson Plan, students’ worksheet, evaluation sheet and learning media. The result of this research, based on the cycle I and cycle II shows that students’ writing skill on simple writing is improving. Based on the data, it concludes that the application of SAVI method is able to improve the 3rd Elementary School students’ writing skills on simple writing.
The Effectiveness of Think Talk Writing and Guided Writing Techniques on Teaching Descriptive Writing for the Tenth-Grade Students of SMK Swasta Yayasan Abdi Karya Torganda

This research presents a research report on the using of Think Talk Writing (TTW)Method and Guided Writing Techniques for students’ writing skill at SMK Swasta Yayasan Abdi Karya (Yadika) Torganda . The objectives are to find out the effect of using Think Talk Writing (TTW) and Guided Writing Techniques (GWT) for students’ writing skill and which method is the most effective one. It was by using classroom action research. The subject of this study were grade X students of Convesional High School at SMK Swasta Yayasan Abdi Karya (Yadika) Torganda. In this study the writer took 105 students of SMK Swasta Yayasan Abdi Karya (Yadika) Torganda a as the sample. They were divided into three groups. The first and the second groups are (Experimental Group). They were taught by using Think Talk Writing (TTW) and Guided Writing Techniques (GWT) in writing text, while the third group (Control Group) was taught without using Think Talk Writing (TTW) and Guided Writing Techniques (GWT). The technique for data analysis is quantitative research. In analyzing the data the mean of the students score for pre-test in experimental group one (TTW ) is 69,27 and the mean of the students score for post-test in experimental group one is 81,30. The mean of the students score for pre-test in experimental group two (GWT)  is 68,83 and  the mean of the students score for post-test in experimental group two is 80,29.The mean for pre-test in control group is 66,8 and the mean for post-test in control group is 76,77 The conclusion is that the using of Think Talk Writing (TTW) and Guided Writing Techniques (GWT)  are significantly affect the students’ writing skill.TTW is the most effective one in teaching News Item Text than GWT . It is suggested that teachers should apply this method as one of methods to improve students’ Ability in Writing Descriptive Text.
The Development Assessment Instruments Reading Appreciation Primary School

The purpose of this study is to develop a reading assessment instrument that can be used by teachers to assess students' reading skills in learning Indonesian Language in elementary school. In addition, the developed instrument can also be used by teachers as a learning tool or learning strategy in reading. Among the instruments that were developed are reading appreciationassessment instruments. Reading the appreciation of this study refers to Barrett's Taxonomy of appreciation levels. The study was carried out using research and development methods. The review process consists of three phases, namely the needs analysis phase, the design and development phase, and the implementation and evaluation phase. The needs analysis phase was carried out in three elementary schools in Banda Aceh City area, involving both teachers and students from the schools. Based on the data from the needs analysis (phase 1), the study continues with the instrument design and development phase (phase 2), and after the developed instrument meets the validity and reliability’sprerequisite conditions set by theexpert and user assessments, the next phase is implementation and evaluation (phase 3). The third phase of the study was carried out in two stages, namely the implementation and evaluation of small groups/preliminary field testing (8 students) and the implementation and evaluation involving one class students/operational field testing (26 students). The results of the third phase indicate that the developed instrument of reading appreciation assessment is effective, right on the target, in accordance with the stage of students' reading ability, according to the needs of the teacher, and can be used by the teacher as an assessment instrument and reading learning strategy.
Blockchain: An Analysis of Different Consensus Mechanisms as Solution for the Byzantine Generals Problem

Blockchain technology, which is mostly used for Bitcoin money, is a decentralised transaction and data management system. In a decentralised and distributed network, blockchain is a chain of blocks that stores information with digital signatures. Different blockchain infrastructures are available.One of the most well-known and fundamental challenges that decentralised networks encounter is the Byzantine Generals' Problem.This paperpresents an analysison the different consensus mechanism focused on study of the designs of the blockchain. It describes blockchain's taxonomy and architecture, as well as a basic overview of the Byzantine Generals Problem.The classification of blockchains based on their design has been described in this study. Following that, a quick overview of some of the systems that fit within this classification is provided. Finally, a comparison of these systems was conducted under specific features.
Extending a Text-to-Pictograph System to French and to Arasaac

We present an adaptation of the Text-to-Picto system, initially designed for Dutch, and extended to English and Spanish.The original system, aimed at people with an intellectual disability, automatically translates text into pictographs (Sclera and Beta).We extend it to French and add a large set of Arasaac pictographs linked to WordNet 3.1.To carry out this adaptation, we automatically link the pictographs and their metadata to synsets of two French WordNets and leverage this information to translate words into pictographs.We automatically and manually evaluate our system with different corpora corresponding to different use cases, including one for medical communication between doctors and patients.The system is also compared to similar systems in other languages.
Statistical characteristics of multi-channel cooperative dynamic speckle metric

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
Subcategorizing Adverbials in Universal Conceptual Cognitive Annotation

Universal Conceptual Cognitive Annotation (UCCA) is a semantic annotation scheme that organizes texts into coarse predicate-argument structure, offering a broad coverage of semantic phenomena.At the same time, there is still need for a finer-grained treatment of many of the categories.The Adverbial category is of special interest, as it covers a wide range of fundamentally different meanings such as negation, causation, aspect, and event quantification.In this paper we introduce a refinement annotation scheme for UCCA's Adverbial category, showing that UCCA Adverbials can indeed be subcategorized into at least 7 semantic types, and doing so can help clarify and disambiguate the otherwise coarse-grained labels.We provide a preliminary set of annotation guidelines, as well as pilot annotation experiments with high inter-annotator agreement, confirming the validity of the scheme.