Reviewerly/exHarmony huggingface dataset:
Multimodal Conversation Modeling via Neural Perception, Structure Learning, and Communication

Author(s): Zheng, Zilong | Advisor(s): Zhu, Song-Chun | Abstract: Multimodal conversation modeling is an important and challenging problem when building conversational agents. Pioneer works mostly focus on end-to-end multimodal fusion techniques, which require large volumes of pairwise data and lacks interpretability.This dissertation aims at closing the loop of vision and language multimodal modeling from the perspectives of neural perception, structure learning, and communication. Specifically, it makes four major contributions: 1. We explicitly model the joint distribution of vision and language as a Gibbs distribution. Then, we propose an analysis by synthesis cooperative training schema that uses the learned joint distribution to sample from one modality to another, e.g. category to image, attribute to image, etc. Further, we argue that such a training paradigm can be explained in the cognitive theory, where the conditional generator is a fast-thinking initializer that provides a rough output and the sampling process is a slow-thinking solver that refines the output with detailed multimodal information. 2. We propose to view the multimodal dialogue as a graph, where each node is a round of dialogue and the edges represent the semantic dependencies among dialogue turns. Moreover, we propose an Expectation-Maximization (EM)-based algorithm that can both predict partially observed nodes and infer graph structures. We show that such an unsupervised structure learning paradigm can provide post-hoc interpretability to various multimodal dialogue tasks. 3. We present a crucial but barely discussed challenge -- implicature and pragmatics -- in the field of conversational reasoning. We show that human communicate based on their intents and beliefs, where implicatures commonly come along. Considering the missing gap in the current natural language community, we propose a dataset generation protocol based on Spatial-Temporal And-Or-Graphs (ST-AOGs). We show that most of the state-of-the-art language models result in a large performance gap compared with humans. 4. We present a human-robot collaboration task -- bomb defusing game, that requires explanation to help human understand machine's behavior. We argue that such explanations should be generated according to the user's mental preferences, i.e. utilities. Therefore, we propose an explanation generation algorithm based on Hidden Markov Model (HMM), which considers the user's mental utilities as a hidden variable that changes based on observations. We show that, compared with rule-based conversational system, our generated explanations are more natural and are helpful in gaining human trust.
Implementation of A1 Mobile Learning Deutschtrainer Learning Media Against German Vocabulary Mastery for Class XI Students of SMA Negeri 1 Bone

Abstract. This study aims to determine the results of the application of learning media Mobile learning (Deutschtrainer A1) on the mastery of German vocabulary for class XI students of SMA Negeri 1 Bone. Deutschtrainer A1 is a learning medium in the form of a YouTube application and video developed by the Goethe Institut which combines aspects of listening, reading and writing and helps to practice basic vocabulary. This application is free to download for iOS and Android. In connection with this, this media is then applied to class XI students of SMA Negeri 1 Bone in the form of classroom action research. The subjects of this study were students of class XI SMA 1 Bone. The data was collected through tests which were analyzed descriptively qualitatively using the percentage technique. The results showed that the vocabulary mastery of students in the first cycle was 75% and included in the sufficient category, then there was an increase in the second cycle with a percentage of 88.33% and included in the very good category. This shows that during the learning process takes place in two cycles there is a very significant increase in the mastery of German vocabulary for students of class XI SMA Negeri 1 Bone using the Deutsch application. Keywords : Mobile learning, Deutschtrainer A1, vocabulary
HISTORY OF ARCTIC EXPLORATION: A SHORT BIBLIOGRAPHIC REVIEW

The paper presents the activities of the Scientific Library of the Siberian Federal University on the development and implementation of the project “Creation of a Scientific and Educational Geographical Library in SFU”. This project was launched jointly with the Russian Geographical Society. The goal of the project is to form and provide users with high-quality information and educational resource on the profile and topics of the main directions of development of geographical sciences, popularization of geographical knowledge. The activity of the library in the field of formation of unified information scientific and educational space, in the context of the main directions, reflecting the development strategy of the SibFU is analysed. The study of the Arctic is one of the priority tasks of the university. The Arctic vector plays an important role in creating a new library model, in the context of the formation of information resources: the works of SibFU scientists related to the study of Siberia and the Arctic, and providing public access to the accumulated knowledge. The basis of this collection is a unique collection of documents by S. B. Slevich, Doctor of Geographical Sciences, Professor, Academician of the Russian Ecological Academy. The paper discusses the activities for the implementation of the project. The library forms a collection of documents on topics - indigenous peoples, ecology of the Far North, industrial development of northern territories, construction on permafrost and digitizes rare publications. A geographic reading room has been opened to organize access to resources. Together with the Presidential Library. B.N. Yeltsin annually held a scientific and practical seminar “Arctic Day at the Siberian Federal University”. Ways of further work to promote the project have been identified.
Understanding English Texts for Non-English Students: Reading Habits and Reading Preferences in Az-Zainiyah Nurul Jadid

The role of students in Indonesia is needed, especially their ideas and opinions to develop this country. The quality of human resources is certainly very relevant to students' interest in reading. Reading habits are not only related to the teaching and learning process, but also can shape the personality of an individual by living the reading results. The research aims to study the process of reading habits and reading preferences favored by students. The research method used was a descriptive survey involving 45 students of non-English students in Az-Zainiyah Nurul Jadid. In obtaining data to find out the problems faced by students in understanding English reading texts, a questionnaire was used relating to reading habits and reading preferences. The findings from this study are expected to be used as an evaluation material that reading skills also need to be introduced further to students so that they can use them in the reading process.
LibGuides: GED & ESL Resources: GED

Study help for GED course for ESL. Includes resources from RCC & JCLS websites Information about and resources for GED
LibGuides: Research Data Management: Best Practices & FAIR data

A guide to managing outputs of research projects and handling issues such as copyright and data protection laws
혼합학습(Blended Learning)을 적용한 디자인 수업 실증사례 연구 -팀 프로젝트와 스마트디바이스 앱 기반 학습을 중심으로-

This study presents the educational utility of blended learning by analyzing the effectiveness of learning after class by blending team project learning and smart device app-based learning methods. Qualitative analysis and survey analysis were conducted and the results were as follows. First, team project activities based on task resolution were conducted freely through detailed activities such as sharing roles, planning meetings, and coordinating opinions. Team activities were carried out with respect and consideration, team member bonding, and a sense of responsibility. Second, the smart device app is recognized as a medium for work and communication, and fast feedback has been made, making it highly impactful on classroom activities. Third, in terms of learning satisfaction, most learners showed an interest in the course and were satisfied with the project results. The smart device app was used as a learning and communication medium for personal and team activities and was analyzed as a blended method applicable to classes that conduct practical activities as an efficient tool to further activate project activities.
Approaching the Minimum Distance Problem by Algebraic Swarm-Based Optimizations

Finding the minimum distance of linear codes is one of the main problems in coding theory. The importance of the minimum distance comes from its error-correcting and error-detecting capability of the handled codes. &#x0D; It was proven that this problem is an NP-hard that is the solution of this problem can be guessed and verified in polynomial time but no particular rule is followed to make the guess and some meta-heuristic approaches in the literature have been used to solve this problem.&#x0D; In this paper, swarm-based optimization techniques, bat and firefly, are applied to the minimum distance problem by integrating the algebraic operator to the handled algorithms.
Best Computer Monitors For Music Production

Producing music can be as enjoyable as listening to the music if you use the right equipment. Using a computer monitor that is well equipped to cater to ...
LibGuides: 2021 Reading Challenge: Food

A guide of book recommendations designed for readers completing MPL's annual reading challenge!
Darth Vader Voice Navigation Pack [1.38]

Polish Voice in Navigation Darth Vader Mod tested on version 1,38.1,0s 1. Click the download button 2. Download the rar file and unpack it 3. ...
Hours of Horus: Keyless Cryptocurrency Wallets

We put forth a keyless wallet, a cryptocurrency wallet where money can be spent using a password alone, and no private keys are required. It requires a smart contract blockchain. We propose a scheme in which the user uses an OTP authenticator seed to generate a long series of time-based OTP passwords for the foreseeable future. These are encrypted and organized in a Merkle tree whose root is stored in a smart contract. The user can spend funds at any time by simply visually providing the current OTP password from an air gapped device. These OTPs can be relatively short: Just 6 alphanumeric characters suffice. Our OTP scheme can work in proof-of-stake as well as static and variable difficulty proof-of-work blockchains. The low-entropy passwords and OTPs in our scheme are protected from brute force attacks by requiring that an adversary accompany any attempt by a transaction on the chain. This quickly incurs enormous economic costs for the adversary. Thus, we develop the first decentralized rate limiting scheme. We use Witness Encryption (WE) to construct a timelock encryption scheme in which passwords are encrypted from past into future blocks by leveraging the NP-language having proof-of-work or proof-of-stake performed as the witness. Witness Encryption is a currently impractical cryptographic primitive, but our scheme may become practical as these primitives are further developed.
Anomaly detection using machine learning algorithms

Anomaly detection techniques are a well-known area for solving a particular kind of problem when there is the presence of an anomaly. Anomaly is the deviation from standard or we can say normal (regular) pattern. Anomalies are the data points that do not match the expected pattern. Anomalies can occur in transactions like Credit Card Fraud, Campaign Response. It also can occur in Video Surveillance like in Traffic, and can also as Spam, Malware/Intrusion. As technology is evolving day by day, it gives us many profits but at the same time many losses are also incurred. Like with the evolution of the domain of e-commerce and online banking, there is a drastic increase in the usage of credit cards for payment. Mostly the shopping (online shopping) is done by making the use of credit cards and due to this big usage of the credit cards; we are also facing the problem of fraud and big damages. So, it is very important to detect fraud in transactions so that we do not have to face big financial losses. There are many Machine learning algorithms or techniques, both supervised and unsupervised are available for anomaly detection. Here we can classify the fraudulent transaction in credit card fraud as point anomaly, so for detecting it we can use supervised algorithms like SVM, KNN, Random forest algorithm, and unsupervised algorithms like Isolation Forest algorithm, LOF algorithm. In this paper, we are discussing our experiment on credit card fraud detection by using Unsupervised Anomaly Detection algorithm. Later, we will also compare the performance of all the algorithms that we are using (Isolation forest Algorithm and LOF Algorithm).
Application of a Cultural Algorithm for 3D Inversion of Magnetotelluric Data

Summary A modified cultural algorithm (MCA) implementation for the three-dimensional (3D) magnetotelluric (MT) inverse problem is presented. MCA uses several parameters for belief space forming. Information about number of applicants, number of parents and mutation probability is used to form belief space together with min/max interval for mutation of genes. The COPROD2 data set (Canada) is used as an MT benchmark of real field data.
Exploiting Program Structure for Scaling Probabilistic Programming

Author(s): Holtzen, Steven | Advisor(s): Van den Broeck, Guy; Millstein, Todd | Abstract: Probabilistic modeling and reasoning are central tasks in artificial intelligence and machine learning. A probabilistic model is a rough description of the world: the model-builder attempts to capture as much detail about the world’s complexities as she can, and when no more detail can be given the rest is left as probabilistic uncertainty. Once constructed, the goal of a model is to perform automated inference: compute the probability that some particular fact is true about the world. It is natural for the model-builder to want a flexible expressive language – the world is a complex thing to describe – and over time this has led to a trend of increasingly powerful modeling languages. This trend is taken to its apex by probabilistic programming languages (PPLs), which enable modelers to specify probabilistic models using the facilities of a full programming language. However, this expressivity comes at a cost: the computational cost of inference is in direct tension with the flexibility of the modeling language, and so it becomes increasingly difficult to design automated inference algorithms that scale to the kinds of systems that model builders want to create.This thesis focuses on the central question: how can we design effective probabilistic programming languages that profitably trade-off expressivity and tractability for inference? The approach taken here is first to identify and exploit important structure that a probabilistic program may possess. The kinds of structure considered here are discrete program structure and symmetry. Programs are heterogeneous objects, so different parts of programs may exhibit different kinds of structure; in the second part of the thesis I show how to decompose heterogeneous probabilistic program inference using a notion of program abstraction. These contributions enable new applications of probabilistic programs in domains such as text analysis, verification of probabilistic systems, and classical simulation of quantum algorithms.
RikoNet: A Novel Anime Recommendation Engine

Anime is quite well-received today, especially among the younger generations. As anime has recently garnered mainstream attention, we have insufficient information regarding users’ penchant and watching habits. Therefore, it is an uphill task to build a recommendation engine for this relatively obscure entertainment medium. In this attempt, we have built a novel hybrid recommendation system that could act both as a recommendation system and as a means of exploring new anime genres and titles. We have analyzed the general trends in this field and the users’ watching habits for coming up with our efficacious solution. Our solution employs deep autoencoders for the tasks of predicting ratings and generating embeddings. Following this, we formed clusters using the embeddings of the anime titles. These clusters form the search space for anime with similarities and are used to find anime similar to the ones liked and disliked by the user. This method, combined with the predicted ratings, forms the novel hybrid filter. In this article, we have demonstrated this idea and compared the performance of our implemented model with the existing state-of-the-art techniques.
Collaborative-AI knowledge graph generation: Taxonomization of IATE, the EU terminology

Formalized knowledge is a powerful resource for AI projects, but it is usually created at great expense. Taxonomization is linking a flat set of concepts into a hierarchical knowledge graph, and in this work, we present our approach to semi-automatic generation of such concept maps, elevating a sub-domain of IATE terminology into a multilingual knowledge graph. We taxonomized a flat list of concepts within the COVID sub-domain, benchmarking two approaches to tackle this task: automatic concept map creation using an enhanced ML-powered language model and manual creation of the graph by a linguist expert. We dwell on advantages of the collaborative method, made easy by a user-friendly UI, and show how the achieved productivity rate can make taxonomization of large terminology databases viable. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Anna Voice Navigation Pack [1.38]

Polish voice in navigation Anna Mod tested on version 1.38.0.43s Works fine, if you notice errors, let me know in the comments, which I will ...
ST-LSTM: A SPATIAL-TEMPORAL LONG-SHORT TERM MEMORY NETWORK FOR CRIME EVENT RATE PREDICTION

In India, the crime rate is increasing day by day. It is essential to develop different modern advanced tools and techniques to predict the location and time of the crime events in advance. This prediction will enable the police to improve the monitoring/vigilance and strengthen intelligence in the particular district to avoid such crime events. There are several Spatio-temporal statistical methods are used to predict such events in the past. Most of the time, it is fragile and unable to predict the event's location accurately. This paper attempt is made to predict the crime event. A SpatialTemporal Long-Short Term Memory (ST-LSTM) model is proposed, which naturally trusts spatial-temporal factors into LSTM to overcome the data shortage problem. The experiments and evaluation are performed with NCRB Dataset. This proposed model is evaluated in the experiments set up explicates the accurate results.
Image Data Compression Based on Two Hybrids Algorithms

Image compression is a way to reduce the storage space of images. Therefore, image compression process increases the performance of such as the data transmission process.  This paper aims to present a new technique to compress digital image data. In this technique, two-hybrid algorithms were used to compress the image data.  The first system consists of one-dimensional discrete cosine transform (DCT), differential pulse code modulation (DPCM), and Huffman code for difference signal. The second hybrid system utilizes an expert system called Learning Automata (LA) to code the difference signal obtained from the first system. A compression ratio of about (10.8:1) was obtained from the first system. The second system provides a (20.6:1) compression ratio with non-noticeable impairment. The information loss is caused by a hybrid (DCT/DPCM) system, not by the LA system. The conclusion drawn is that using two-hybrid systems to compress the image data provides a high compression ratio. Furthermore, learning automata is preferable since it removes all the redundancy in the row data. However, in learning automata,  a Huffman code is determined pixel by pixel which takes a long time.
A Study on Ethical Data Management, Publication, and Use

You are viewing a submission for OR2021 Presentation Submission16th International Open Repositories Conference in June 7th - 10th, 2021 that was featured in the Good Systems Network Digest May 2021.
FOSTERING STUDENTSâ€™ PRONUNCIATION IN SPEAKING SKILL THROUGH ROLE PLAY TECHNIQUE

This Research is entittled â€œ: Fostering Studentsâ€™ Pronunciation in Speaking Skill Through Role Play Teachniqueâ€. The aim of this research is to find whether role play technique can foster studentsâ€™ pronunciation in speaking skill at the eight grade students of SMPN 1 Arjawinangun. In getting the data of studentsâ€™ pronunciation. The writer used oral test. The writer test the students supra- segmental features of pronunciation included intonation, word stress, sentence stress, pitch and juncture. this research used quantitative research and used pre-experiment design. the score of studentsâ€™ pre-test and post-test were analyzed by the formula of Sundayana (2015). The result showed that taccount score is higher that ttable score (6.40 > 2.0433). it means that whe writersâ€™ hyphothesis (H1) is accepted. Therefore, the writer makes conlusion that role play can foster studentsâ€™ pronunciation in speaking skill at the eight grade studentsâ€™ of SMPN 1 Arjawinangun.
A SURVEY ON CLOUD COMPUTING SECURITY WITH CROSS PLATFORM

Cloud computing is the lately spread revolution. Every organization wants this type of technology for their programs interfaces. Surveys on particular cross cloud organization models and usable cloud profit models in the field of cloud computing are considered. According to industry style we notice that utilization of cross cloud which is use for solving industrial upcoming objection using a mix of both private and public cloud to an effective method to address the cloud status of your information does so that the office can get it. Storemost important information on private cloud and provide less basic information on public cloud where huge storing capacity of data. Cross cloud is particularly beneficial for effective or extremely flexible workload. This paper portrays the overview, service model, traits, supplier storage and issues of cloud computing.
중국인 한국어 학습자의 목적격 조사 ‘을/를’의 사용 오류 연구

This study revealed the errors of the objective proposition markers Eul/Reul comprehensively for Chinese Korean Language learners. The analysis of the misusing for the objective proposition makers Eul/Reul showed that the errors quantity didn''t decrease with the improvement of Korean level and developed as order Advanced(27.83% > Primary(19.09%) > Intermedia-te(15.30%). The order of relative error rate in error types was replacement error(76.35%) > omission error(13.17%) > morphological error(6.89%)> addi-tion error(3.89%). Although the causes of each type of error were different, they could be summed up as follows: 1) intralingual transfer which was mainly in the form of the negative transfer of Chinese language, 2) interlingual interference which was mainly caused by the over-generalization of the grammar rules of the Korean language, 3) the influence of learning environment, 4) the communication strategy of avoiding the use of Eul/Reul. Based on the results of the error analysis, some suggestions on the teaching were put forward.
LibGuides: Summer Reading 2021: General Guiding Questions & How to Read

Information about Summer Reading at Central Catholic High School Tips on reading and annotating over the summer. Additional questions to think about.
What are Requirements and Why are They Your Friends... A Benevolent Introduction to Requirements

When first becoming involved in the development of a proposal or project, the process of building requirements is key. This presentation was produced for the PI Launchpad course, which brings together potential and first time Principal Investigators.