Reviewerly/exHarmony huggingface dataset:
Political Opinion Analysis in Social Networks: Case of Twitter and Facebook

The 21st century has been characterized by an increased attention to social networks. Nowadays, going 24 hours without getting in touch with them in some way has become difficult. Facebook and Twitter, these social platforms are now part of everyday life. Thus, these social networks have become important sources to be aware of frequently discussed topics or public opinions on a current issue. A lot of people write messages about current events, give their opinion on any topic and discuss social issues more and more. The emergence and enormous popularity of these social networks have led to the emergence of several types of analysis to take advantage of them. One of them is the analysis of opinions in texts. It aims at automatically classifying opinions in order to position them on a sentiment scale, thus allowing to characterize a set of opinions without having to rely on a human to read them. Currently, opinion analysis offers us a lot of information related to public opinion, either in the commercial world or in the political world. Many studies have shown that machine learning techniques, such as the support vector machine (SVM) and the naive Bayes classifier (NB), perform well in this type of classification. In our study, we first propose an approach for tracking and analyzing political opinions in social networks. Then, we propose a trained and evaluated machine learning model for political opinion classification. And finally, the study aims at setting up a web interface to collect and analyze in real time political opinions from social networks
Multilingual Image Corpus: Annotation Protocol

In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects.The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related images annotated with segmentation masks and object classes.We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc.For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of highquality copyright free images.The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation.The dataset is designed both for image classification and object detection and for semantic segmentation.In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.
DBOS: A Database-Oriented Operating System : Keynote 1

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Current operating systems are complex systems that were designed long before today’s computing environments. This makes it difficult for them to meet the scalability, heterogeneity, availability, and security challenges in current cloud and parallel computing environments. To address these problems, we propose a radically new OS design based on data-centric architecture: all operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. We discuss how a database OS (DBOS) can improve the programmability and performance of many of today’s most important applications, propose a plan for the development of a DBOS proof of concept, and give results on a pilot that suggest the approach has merit.
An Analysis of Students Misbehaviour in Distance Learning

Online learning has become an inevitable trend in the world of education. Even before the outbreak of the Covid 19 pandemic, many educational institutions had implemented blended and distance learning in their teaching and learning activities. In fact, distance learning is an innovative step that allows the transfer of knowledge without time and space boundaries. Effective distance learning cannot be separated from behaviour of students. Students discipline is a crucial element to create an effective online class situation. The study has objective of analysing misbehaviour occurred in online class. The approach used in this research is qualitative using questionnaires and interviews as data collection techniques. 10 English teachers were interviewed to obtain the data about students’ misbehaviour occurred. Furthermore, the data were analysed descriptively. The results showed that the most common students misbehaviour in online class was failure of assignment submission, language aggression and disrespecting teachers.
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing

The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages.While zero-shot cross-lingual transfer relies on multilingual word embeddings to apply a model trained on one language to another, Yarowsky and Ngai (2001) propose the method of annotation projection to generate training data without manual annotation.This method was successfully used for the tasks of named entity recognition and coarse-grained entity typing, but we show that it is outperformed by zero-shot cross-lingual transfer when applied to the similar task of fine-grained entity typing.In our study of fine-grained entity typing with the FIGER type ontology for German, we show that annotation projection amplifies the English model's tendency to underpredict level 2 labels and is beaten by zero-shot cross-lingual transfer on three novel test sets.
Lifelong Event Detection with Knowledge Transfer.

Traditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but it is limited to a pre-defined target ontology. In reality, the ontology of interest may change over time, adding emergent new types or more fine-grained subtypes. We propose a new lifelong learning framework to address this challenge. We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks. In event detection and more general IE tasks, rich correlations or semantic relatedness exist among hierarchical knowledge element types. In our proposed framework, knowledge is being transferred between learned old event types and new event types. Specifically, we update old knowledge with new event types’ mentions using a self-training loss. In addition, we aggregate old event types’ representations based on their similarities with new event types to initialize the new event types’ representations. Experimental results show that our framework outperforms competitive baselines with a 5.1% absolute gain in the F1 score. Moreover, our proposed framework can boost the F1 score for over 30% absolute gain on some new long-tail rare event types with few training instances. Our knowledge transfer module improves performance on both learned event types and new event types under the lifelong learning setting, showing that it helps consolidate old knowledge and improve novel knowledge acquisition.
DramaCoref: A Hybrid Coreference Resolution System for German Theater Plays

We present a system for resolving coreference on theater plays, DramaCoref.The system uses neural network techniques to provide a list of potential mentions.These mentions are assigned to common entities using generic and domain-specific rules.We find that Drama-Coref works well on the theater plays when compared to corpora from other domains and profits from the inclusion of information specific to theater plays.On the best-performing setup, it achieves a CoNLL score of 32% when using automatically detected mentions and 55% when using gold mentions.Single rules achieve high precision scores; however, rules designed on other domains are often not applicable or yield unsatisfactory results.Error analysis shows that the mention detection is the main weakness of the system, providing directions for future improvements.
Penentuan Penerima Bantuan Rumah Tidak Layak Huni Menggunakan Metode Simple Additive Weghting

Uninhabitable Housing Assistance (RTLH) is a government program which distribute to village office, it has purpose to improve the life quality of community. Poor people can life convenient with Uninhibitable Houses Assistence. Determination of social assistence construction of Uninhabitable Houses is going to do by relying on the intitution. The purpose of this research is creating a decision support system that can help to determine the appropriate poor people are receiving social assistence of Uninhabitable House (RTLH) using simple additive weighting (SAW) method. It is used the creteria such as, the monthly income, the occupation, the total of burden, the condition of house wall, the condition of house floor, the condition of house roof, and the condition of bathroom. The result of this research is the beneficial system for receiver of Uninhabitable Houses.
Underlying Mental Factors Contributing to Software Complexity

Software complexity and program comprehension are inversely related. Higher the code complexity, poorer the comprehension. But we neither have good software complexity measure, nor do we understand how the program comprehension took place in human mind. This is because we know so little about the working of the human brain; how it processes internal and external information. In this paper we have identified 5 mental factors which adds into the code complexity. In order to explain these factors, we took 10 code snippet pairs in C language (2 each for every factor). Code snippets in pair are identical - in terms of number of variables, operators, control structure- but we believe one of the snippets in pair is carrying the higher cognitive load due to underlying mental factor identified. To the best of our knowledge these factors identified here in this paper are not used in calculating the code or software complexity. We believe these identified mental factors can be validated by various brain imaging and Eye tracking techniques like EEG and fMRI. They can also be validated by conventional software experimental methods. We believe these identified factors will increase our understanding of Program comprehension as well as it will lead better software complexity measure. This could be very useful in computer science education. The very process of understanding how the human mind decode the software can be possibly understood. In long run this could help us in better understanding of the functioning of human brain.
Implementation of the Simple Additive Weighting Method in Determining Centroids in the Process of Clustering the Poor in Kakatpenjalin Village, Lamongan Regency

Clustering is an algorithm in a decision support system that functions to organize an object into groups of data. In the clustering process, of course, a cluster centre is needed by the desired data group. However, the clustering process has a problem. Related research states that the results of k-means clustering can influence the selection of cluster centre points. Random selection of cluster centre points can result in different clustering results in the same data group. Not only on k-means, but k-medoids also have the same problem. So that to produce a good cluster, you must start by choosing the right cluster centre. To solve this problem, the Simple Additive Weighting method is used to select the centre point of the cluster. Simple Additive Weighting selects the centre point of the cluster by adding and summarizing the dataset. The summation is done by giving weight to each criterion and each criterion has its alternative value. From this weighted addition, the final value will be obtained. From the sum of SAW, then one of the objects with the highest and lowest values ​​can be taken to serve as the centre of the cluster.
Safe Reinforcement Learning with Natural Language Constraints

In this paper, we tackle the problem of learning control policies for tasks when provided with constraints in natural language. In contrast to instruction following, language here is used not to specify goals, but rather to describe situations that an agent must avoid during its exploration of the environment. Specifying constraints in natural language also differs from the predominant paradigm in safe reinforcement learning, where safety criteria are enforced by hand-defined cost functions. While natural language allows for easy and flexible specification of safety constraints and budget limitations, its ambiguous nature presents a challenge when mapping these specifications into representations that can be used by techniques for safe reinforcement learning. To address this, we develop a model that contains two components: (1) a constraint interpreter to encode natural language constraints into vector representations capturing spatial and temporal information on forbidden states, and (2) a policy network that uses these representations to output a policy with minimal constraint violations. Our model is end-to-end differentiable and we train it using a recently proposed algorithm for constrained policy optimization. To empirically demonstrate the effectiveness of our approach, we create a new benchmark task for autonomous navigation with crowd-sourced free-form text specifying three different types of constraints. Our method outperforms several baselines by achieving 6-7 times higher returns and 76% fewer constraint violations on average. Dataset and code to reproduce our experiments are available at https://sites.google.com/view/polco-hazard-world/.
Genesis of the Early Cretaceous Tudui–Shawang gold deposit, Jiaodong Peninsula, North China Craton: Evidences from H-O-He-Ar-S-Pb isotopic compositions

A new gold metallogenic belt has recently been identified in which contains reserves of > 120 tonnes Au along the northeastern margin of the Jiaolai Basin, Jiaodong Province.Nevertheless, the origin of these deposits are currently under vigorous debate.Among these deposits, the Tudui-Shawang gold deposit is a particular fault-hosted sulfide-rich gold deposit, with three mineralization stages, i.e., quartz-coarse-grained pyrite mineralization, quartz-polymetallic sulfide mineralization, and quartz-calcite mineralization.Here, we present records of which combined systematic H-O-He-Ar-S-Pb isotopic data of this deposit.The δ 18 O H2O values of quartz in ore stages 1 and 2 range from +3.4‰ to +6.3‰ and +2.3‰ to +4.5‰, whereas the δD SMOW from -93‰ to -86‰ and -97‰ to -83‰.Both suggest that the ore-forming fluids are derived from magmatic sources, followed by mixing with meteoric water.The measured 3 He/ 4 He ( 40 Ar/ 36 Ar) of hydrothermal fluids in pyrites are 1.53 to 3.21 R a (732 to 1783) for stage 1, and 0.38 to 2.58 R a (360 to 1544) for stage 2. The linear correlation between He and Ar isotopes is the result of a combination between mantle components and crustal fluid.The estimated 3 He/heat ratios range from 0.13 to 2.55 × 10 -12 cm 3 STP J -1 , reflecting the heat source is actually high 3 He/ 4 He fluids originating from mantle.The pyrite δ 34 S from
Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue

Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information.However, the quality of most existing models' generated response is still limited, mainly due to their lack of finegrained reasoning on deterministic knowledge (w.r.t.conceptual tokens), which makes them difficult to capture the concept shifts and identify user's real intention in cross-task scenarios.To address these issues, we propose a novel intention mechanism to better model deterministic entity knowledge.Based on such a mechanism, we further propose an intention reasoning network (IR-Net), which consists of joint and multi-hop reasoning, to obtain intention-aware representations of conceptual tokens that can be used to capture the concept shifts involved in task-oriented conversations, so as to effectively identify user's intention and generate more accurate responses.Experimental results verify the effectiveness of IR-Net, showing that it achieves the stateof-the-art performance on two representative multi-domain dialog datasets.
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method

Policy gradient (PG) gives rise to a rich class of reinforcement learning (RL) methods. Recently, there has been an emerging trend to accelerate the existing PG methods such as REINFORCE by the \emph{variance reduction} techniques. However, all existing variance-reduced PG methods heavily rely on an uncheckable importance weight assumption made for every single iteration of the algorithms. In this paper, a simple gradient truncation mechanism is proposed to address this issue. Moreover, we design a Truncated Stochastic Incremental Variance-Reduced Policy Gradient (TSIVR-PG) method, which is able to maximize not only a cumulative sum of rewards but also a general utility function over a policy's long-term visiting distribution. We show an $\tilde{\mathcal{O}}(\epsilon^{-3})$ sample complexity for TSIVR-PG to find an $\epsilon$-stationary policy. By assuming the overparameterizaiton of policy and exploiting the hidden convexity of the problem, we further show that TSIVR-PG converges to global $\epsilon$-optimal policy with $\tilde{\mathcal{O}}(\epsilon^{-2})$ samples.
Controlled tasks for model analysis: Retrieving discrete information from sequences

In recent years, the NLP community has shown increasing interest in analysing how deep learning models work.Given that large models trained on complex tasks are difficult to inspect, some of this work has focused on controlled tasks that emulate specific aspects of language.We propose a new set of such controlled tasks to explore a crucial aspect of natural language processing that has not received enough attention: the need to retrieve discrete information from sequences.We also study model behavior on the tasks with simple instantiations of Transformers and LSTMs.Our results highlight the beneficial role of decoder attention and its sometimes unexpected interaction with other components.Moreover, we show that, for most of the tasks, these simple models still show significant difficulties.We hope that the community will take up the analysis possibilities that our tasks afford, and that a clearer understanding of model behavior on the tasks will lead to better and more transparent models.
Voice and Face Recognition for Web Browser Security

Abstract: This paper analysis about browser privacy multimodal authentication mechanisms. Face and voice recognition will be used as authentication methods in this process. The OpenCV library is used in the framework's face recognition section. It detects and recognizes faces from a database using basic eigen face recognition approaches. The MFCC (Mel Frequency Cepstrum Coefficients) and Gaussian Mixture Model are used to recognize voices. Following successful authentication, the cookies on the local hard disc are decrypted, allowing us access to the browser cookies. Initially, after a user registers, we will encrypt the browser cookies with AES, one of the most secure encryption methods available. keywords: MFCC, Gaussian Mixture Model, Browser cookies, authentication, AES, encryption, decryption, Open CV, Eigen.
How We Did It Ourselves: A Case Study on Water Quality Improvement

A water utility team improved the water quality in the utility's storage tanks by employing a “we can do it together” attitude.
Analisis Usia Paro Hidup (Half-Life) dan Keusangan Literatur Journal Of English Language Teaching Volume 8 Tahun 2019

This study aims to analyze 64 articles contained in the Journal of English Language Teaching Volume 8 of 2019 by using quantitative methods through the Evaluative Bibliometrics technique namely Citation Analysis.This volume has 4 numbers, each number has a different number of articles.Number 1 there are 475 citations, number 2 there are 50 citations, number 3 there are 273 citations, and number 4 there are 217 citations.Cited analyzed there were 1016 citations.The results of this analysis obtained the half-life of the Journal of English Language Teaching Volume 8 in 2019 of 15,75 years with 269 citations (26,47%) and new citations as many as 747 citations (73,53%).Based on the results of the calculation of this analysis the scientific work that will be written in the following year is advised to take references no more than 15,75 years from the written work.
Highlighted Document Image Classification

There are many existing document image classification researches, but most of them are not designed for use in constrained computer resources, like printers, or focused on documents with highlighter pen marks.To enable printers to better discriminate highlighted documents, we designed a set of features in CIE Lch(a * b * ) space to use along with the support vector machine.The features include two gamut-based features and six low-level color features.By first identifying the highlight pixels, and then computing the distance from the highlight pixels to the boundary of the printer gamut, the gamut-based features can be obtained.The low-level color features are built upon the color distribution information of the image blocks.The best feature subset of the existing and new features is constructed by sequential forward floating selection (SFFS) feature selection.Leave-one-out cross-validation is performed on a dataset with 400 document images to evaluate the effectiveness of the classification model.The cross-validation results indicate significant improvements over the baseline highlighted document classification model.
Pengaruh pembelajaran online berbasis SPADA, lingkungan, dan sarana prasarana terhadap motivasi belajar

This study discusses online learning based on SPADA, environment, and infrastructure on learning motivation for students of the Faculty of Economics and Business, PGRI Kanjuruhan University, Malang. This study aims to analyze: 1) The effect of SPADA-based online learning, the environment, and infrastructure on learning motivation, 2) the influence of SPADA-based online learning on learning motivation, 3) the influence of the environment on learning motivation, 4) the influence of infrastructure on learning motivation. . This research is a quantitative research. The total population in this study was 330 students consisting of students of the Faculty of Economics and Business class of 2017, Universitas PGRI Kanjuruhan Malang. The sampling technique used is random sampling. The data collection method uses a questionnaire in the form of a Google Form. The validity of the data was obtained through a trial from 30 respondents outside the sample. The data analysis method used multiple linear regression analysis. In this study there are: The influence of SPADA-based online learning, the environment, and infrastructure on learning motivation, the influence of SPADA-based online learning on learning motivation, the influence of the environment on learning motivation, the influence of infrastructure on learning motivation