Reviewerly/exHarmony huggingface dataset:
Prediksi Sentimen Investor Pasar Modal Di Jejaring Sosial Menggunakan Text Mining

The decline in optimism for capital market investors is one of the financial impacts on the business world that arose from the SARS-COVID19 pandemic. This event was reflected in a decrease in trading volume followed by a sharp drop in the JCI on the Indonesia Stock Exchange starting March 2020. Thus, a slowdown in the economic recovery resulting from the pandemic is reflected in investor sentiment in the capital market. On the one hand, the rapid development of the internet in Indonesia has triggered the investor's activities in the information searching prior buy and sell securities, mostly use online platforms, which contribute to influencing investor preferences and sentiment. This study conducted a qualitative examination of the features/terms of stock investment in the capital market and collected them in a compact dictionary (lexicon). Therefore, lexicon-based investor opinion extraction was extracted from Twitter, followed by the text sentiment analysis, and forming a classification model based on Naive Bayes and Decision Tree. This research output shows that the polarity of capital market investor sentiment is optimistic with the sentiment features that often appear, namely "cuan", "bearish," "serok", "copet", "untung", "cut loss", and "nyangkut." Meanwhile, the Decision Tree classification model provides better performance.Keywords : investor, lexicon, social network, stock exchange, text miningCorrespondence to : aestikani.mahani-2019@feb.unair.ac.id Penurunan optimisme investor pasar modal adalah salah satu dampak keuangan pada dunia usaha yang timbul akibat pandemi SARS-COVID19. Hal ini tercermin dari turunnya volume perdagangan yang diikuti penurunan tajam IHSG di Bursa Efek Indonesia mulai Maret 2020. Sehingga kekhawatiran atas perlambatan pemulihan ekonomi sebagai dampak pandemi, tercermin dari sentimen investor di pasar modal. Di satu sisi, perkembangan internet di Indonesia yang pesat, memicu kecenderungan aktivitas investor dalam pencarian informasi sebelum membeli dan menjual surat berharga secara online, turut berkontribusi dalam mempengaruhi preferensi dan sentimen investor. Penelitian ini menggali ekspektasi investor yang tercermin pada sentimen investasi, dimana pasar modal sebagai salah satu barometer penting perekonomian suatu negara. Kajian ini mengeksplorasi fitur/terms investasi saham yang kerap muncul di pasar modal dan mengumpulkannya dalam kamus leksikon. Kemudian, dilakukan ekstraksi opini investor berbasis leksikon yang digali dari jejaring sosial Twitter, dilanjutkan dengan tahap text mining yaitu menganalisis sentimen, dan membentuk model klasifikasi berbasis Naive Bayes dan Decision Tree. Keluaran penelitian ini menunjukkan bahwa polaritas sentimen investor pasar modal adalah positif dengan fitur sentimen yang sering muncul yaitu “cuan”, “bearish”, “serok”, “copet”, “untung”, dan “cut loss”. Sedangkan model klasifikasi Decision Tree memberikan performansi akurasi yang kebih baik.Kata Kunci : Analisis sentimen; Investor; Leksikon; Text mining; Twitter
UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense

Detecting humor is a challenging task since words might share multiple valences and, depending on the context, the same words can be even used in offensive expressions.Neural network architectures based on Transformer obtain state-of-the-art results on several Natural Language Processing tasks, especially text classification.Adversarial learning, combined with other techniques such as multi-task learning, aids neural models learn the intrinsic properties of data.In this work, we describe our adversarial multi-task network, AMTL-Humor, used to detect and rate humor and offensive texts from Task 7 at SemEval-2021.Each branch from the model is focused on solving a related task, and consists of a BiL-STM layer followed by Capsule layers, on top of BERTweet used for generating contextualized embeddings.Our best model consists of an ensemble of all tested configurations, and achieves a 95.66% F1-score and 94.70% accuracy for Task 1a, while obtaining RMSE scores of 0.6200 and 0.5318 for Tasks 1b and 2, respectively.
Machine Translation with Pre-specified Target-side Words Using a Semi-autoregressive Model

We introduce our TMU Japanese-to-English system, which employs a semi-autoregressive model, to tackle the WAT 2021 (Nakazawa et al., 2021) restricted translation task.In this task, we translate an input sentence with the constraint that some words, called restricted target vocabularies (RTVs), must be contained in the output sentence.To satisfy this constraint, we use a semi-autoregressive model, namely, RecoverSAT (Ran et al., 2020), due to its ability (known as "forced translation") to insert specified words into the output sentence.When using "forced translation," the order of inserting RTVs is a critical problem.In our system, we obtain word alignment between a source sentence and the corresponding RTVs and then sort the RTVs in the order of their corresponding words or phrases in the source sentence.Using the model with sorted order RTVs, we succeeded in inserting all the RTVs into output sentences in more than 96% of the test sentences.Moreover, we confirmed that sorting RTVs improved the BLEU score compared with random order RTVs.
Comprehension Based Question Answering using Bloom’s Taxonomy

Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge.Bloom's Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models.Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions.We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.
Library Automation – An Introduction

This article focused on the overview of library automation and the changing scenario of library management.The impact of ICT has changed the library operation and its functionality in to s fast to faster mode.Clients need not to visit shelf to shelf to find out a find out a document.They just get their documents sitting in front of a desktop automation has reduced the man power.This article will discuss about the concept the concept of automation its requirement and various components helps to automate library.Some software package has given which are available for automation purposes.
ENHANCING STUDENT’S TECHNICAL VOCABULARY SIZE BY USING FRAYER MODEL GRAPHIC ORGANIZER

English vocabulary seems to be a crucial problem experienced by non-native students in STMIK Jayanusa Padang. Their low vocabulary knowledge caused several problems in understanding a text especially related to technical vocabulary. The purpose of this research was to improve the students' technical vocabulary size of the third-semester students at STMIK Jayanusa Padang through the Frayer model graphic organizer. This research used classroom action research. The data was collected through tests, field notes, and interviews. The findings of this research revealed that there was an improvement in students' technical vocabulary size obtained from the pretest, cycle 1 to cycle 2. The average score of the pretest was very low, 0.4. Then, it gradually improved in cycle 1 became 0.83 with an increasing point was 0.43 points. Next, the average score found in cycle 1 then better improved in cycle 2 which turned into 1.43 with the increasing point from cycle 1 to cycle 2 was 0.6 points. Shortly, Frayer Model influenced the enrichment of students' technical vocabulary size.&#x0D;
Similarity and symmetry measures based on fuzzy descriptors of image objects’ composition

The paper describes a method for measuring the similarity and symmetry of an image annotated with boundingboxes indicating image objects. The latter representation became popular recently due to the rapid development offast and efficient deep-learning-based object-detection methods. The proposed approach allows for comparing setsof bounding boxes to estimate the degree of similarity of their underlying images. It is based on the fuzzy approachthat uses the fuzzy mutual position (FMP) matrix to describe spatial composition and relations between boundingboxes within an image. A method of computing the similarity of two images described by their FMP matrices isproposed and the algorithm of its computation. It outputs the single scalar value describing the degree of content-based image similarity. By modifying the method’s parameters, instead of similarity, the reflectional symmetry ofobject composition may also be measured. The proposed approach allows for measuring differences in objects’composition of various intensities. It is also invariant to translation and scaling and – in case of symmetry detection– position and orientation of the symmetry axis. A couple of examples illustrate the method.
Video Source Forensics for IoT Devices Based on Convolutional Neural Networks

With the wide application of Internet of things devices and the rapid development of multimedia technology, digital video has become one of the important information dissemination carriers among Internet of things devices, and it has been widely used in many fields such as news media, digital forensics and so on. However, the current video editing technology is constantly developing and improving, which seriously threatens the integrity and authenticity of digital video. Therefore, the research on digital video forensics has a great significance. In this paper, a new video source passive forensics algorithm based on Convolutional Neural Networks(CNN) is proposed. CNN is used to classify the maximum information block of specified size in video I frame, and then the classification results are fused to determine the camera to which the video belongs. Experimental results show that the recognition algorithm proposed in this paper has a better performance than other methods in trems of accuracy and ROC curve. And our method still can have a good recognition effect even if a small number of I frames are used for recognition.
Investigating English Students` Vocabulary Learning Strategies at Raden Intan State Islamic University

The purpose of this study was to determine the types of Vocabulary Learning Strategies (VLS), the most and least frequently used VLSs by female and male students, and the types of VLSs chosen to address vocabulary learning problems. This study employed a survey technique. The population for this study was drawn from Lampung State Islamic University's English Education Department, which consisted of three classes and had a total enrollment of approximately 75 students. Female students totaled 64, while male students totaled 11. The sample consisted of fourth-semester students. The instrument used in this study was a questionnaire that Gu and Johnson proposed in 1996. Interviews were also used as a tool. All English department students at Lampung State Islamic University used one of four vocabulary learning strategies. Among the four categories of Vocabulary Learning Strategies, students in the three classes favoured metacognitive strategies the most, as indicated by the mean score (2.90). On the other hand, the least frequently used strategy was memory/repetition, as indicated by the lowest mean score (2.22). Female students tended to use metacognitive strategies, as indicated by their highest mean score (2.87), while male students tended to use memories/repetition strategies, as indicated by their average score (2.2). Additionally, the most frequently used strategy by male students was metacognitive strategy with a mean score of (2.93), while the least frequently used strategy was activation with a mean score of (2.93). (2.31). To overcome the problem of vocabulary acquisition, the students used the strategies of consulting a dictionary first, followed by learning grammar and asking other people second. The final strategies used were word practise and internet surfing.
Team JARS: DialDoc Subtask 1 - Improved Knowledge Identification with Supervised Out-of-Domain Pretraining

In this paper, we discuss our submission for DialDoc subtask 1.The subtask requires systems to extract knowledge from FAQ-type documents vital to reply to a user's query in a conversational setting.We experiment with pretraining a BERT-based question-answering model on different QA datasets from MRQA, as well as conversational QA datasets like CoQA and QuAC.Our results show that models pretrained on CoQA and QuAC perform better than their counterparts that are pretrained on MRQA datasets.Our results also indicate that adding more pretraining data does not necessarily result in improved performance.Our final model, which is an ensemble of AlBERT-XL pretrained on CoQA and QuAC independently, with the chosen answer having the highest average probability score, achieves an F1-Score of 70.9% on the official test-set.
Video-guided Machine Translation with Spatial Hierarchical Attention Network

Video-guided machine translation, as one type of multimodal machine translations, aims to engage video contents as auxiliary information to address the word sense ambiguity problem in machine translation.Previous studies only use features from pretrained action detection models as motion representations of the video to solve the verb sense ambiguity, leaving the noun sense ambiguity a problem.To address this problem, we propose a video-guided machine translation system by using both spatial and motion representations in videos.For spatial features, we propose a hierarchical attention network to model the spatial information from object-level to video-level.Experiments on the VATEX dataset show that our system achieves 35.86 BLEU-4 score, which is 0.51 score higher than the single model of the SOTA method.
A key distribution protocol based on quantum entanglement swapping for unmanned surface vehicle

Abstract Aiming at the problem of Unmanned Surface Vehicle’s (USV) secure communication, this paper proposes a key distribution protocol based on quantum entanglement swapping for USV. Compared with the current techniques, this paper uses the principle of quantum unclonability and quantum entanglement in quantum mechanics to realize the secure communication of USV. Meanwhile, in view of the low visibility at sea and the influence of weather such as wind and waves, this study adopts a key distribution architecture of quantum relay to realize the establishment of quantum channel between any two ships and obtain the absolutely secure key eventually. Therefore, the proposed method can provide long-distance, secure and reliable communication for USVs in the civil and military fields.
ESTIMATING THE RELATIONSHIP BETWEEN GOVERNANCE, ECONOMIC GROWTH, INEQUALITY AND POVERTY

The main purpose of this paper is to estimate the relationship between governance, economic growth, inequality and poverty for 81 countries for the period 2000-2016. We divided the countries into three groups, low-income countries, lower-middle-income countries and upper-middle-income countries. To answer our research question, we use a structural model with simultaneous equations on Panel data. Our results show that the positive or negative impact of governance on the growth-inequality-poverty triangle changes depending on the dimension of governance taken into account and the sample being studied. Given the negative relationship between inequality and growth, democracy should a second time positively affect economic growth through the reduction of inequalities.
Today’s Education: The Technology Breakthrough And The Preservation Of Traditions

Within international discourse, the scientific conceptualization, interpretation of concepts and processes of modern education occupied by digitalization is following an evolutionary path which has not reached its culminating point yet.This determines the presence of various pedagogical attitudes in the reflection of the present and the future of education and of the mission of people involved.Many researchers suggest their own narrative evaluation of phenomena existing in education today.In a wide scientific discourse, the correlation between the methodology of mass digitalization, which claims to be innovative, and the existing pedagogical traditions supported by the personality-oriented humanitarian component of education is problematized.The authors reveal a scientific vision of the reality in the digitalization era of Russian education and demonstrate how the individuals involved influence it.The article presents anthropologically reflected experience in building competent and conscious Coexistence of people of different generations -students, teachers, parents in the recent situation of forced distance learning.This facilitated the creation of a complete picture of educational environment in the digital era, the identification of the role and mission of each individual involved.The authors' thinking about coscholarization in digital educational environment is based on anthropological traditions of pedagogical theory and practice.This scientific, pedagogical vision is the authors' attempt to motivate everyone involved in education to think seriously what one cannot but take into account in his/her aspirations and actions, what he/she still has to do in order to make education of the present a firm foundation for education of the future.
Implementation Of Data Mining On Sales At Resto D'sdl Lembang Using Aprioric Algorithm Method

This research was conducted to help determine the menu or package that is often purchased simultaneously. The priori algorithm is used to produce data mining regarding the determination of association rules which is carried out by calculating support and confidence in the sales of food menu mechanisms in a restaurant with a case study of the restaurant d'DSL Lembang. The resulting a priori algorithm will be implemented and tested with the Tanagra application. From the results of the discussion and data analysis carried out, it was found that with the application of the a priori algorithm in determining the combination between itemset with a minimum support of 60% and a minimum confidence of 90%, it was found in the initial calculation of the combination of one itemset, a combination of 2 itemset, a combination of 3 itemset and a final association rule with The highest value of support and confidence is if the consumer orders the Hot Sweet Tea menu, Chicken Driver Package and Liwet Rice at the same time with a support value of 58% and a confidence value of 100%.
Fortified Multi-Party Computation: Taking Advantage of Simple Secure Hardware Modules

Abstract In practice, there are numerous settings where mutually distrusting parties need to perform distributed computations on their private inputs. For instance, participants in a first-price sealed-bid online auction do not want their bids to be disclosed. This problem can be addressed using secure multi-party computation (MPC), where parties can evaluate a publicly known function on their private inputs by executing a specific protocol that only reveals the correct output, but nothing else about the private inputs. Such distributed computations performed over the Internet are susceptible to remote hacks that may take place during the computation. As a consequence, sensitive data such as private bids may leak. All existing MPC protocols do not provide any protection against the consequences of such remote hacks. We present the first MPC protocols that protect the remotely hacked parties’ inputs and outputs from leaking. More specifically, unless the remote hack takes place before the party received its input or all parties are corrupted, a hacker is unable to learn the parties’ inputs and outputs, and is also unable to modify them. We achieve these strong (privacy) guarantees by utilizing the fact that in practice parties may not be susceptible to remote attacks at every point in time, but only while they are online, i.e. able to receive messages. To this end, we model communication via explicit channels. In particular, we introduce channels with an airgap switch (disconnect-able by the party in control of the switch), and unidirectional data diodes . These channels and their isolation properties, together with very few, similarly simple and plausibly remotely unhackable hardware modules serve as the main ingredient for attaining such strong security guarantees. In order to formalize these strong guarantees, we propose the UC with Fortified Security (UC#) framework, a variant of the Universal Composability (UC) framework.
How Effectively Can Students’ Personal Smartphones be Used as Tools in Physics Labs?

This study seeks to answer the question of how effectively students can use their smartphones as tools for measuring and processing data when they perform physics experiments. The research was conducted in a local secondary school in Athens, Greece. The sample consisted of fifty-two 16-year-old students (10th grade). The students formed 26 pairs, and were asked to perform an experiment using their smartphones for measuring, processing and saving data, and then to email the data file to the researchers. During the implementation, students of each pair completed the steps on a worksheet. Two researchers monitored each pair individually and recorded scores and comments on evaluation sheets. The worksheets, the evaluation sheets, and the experimental data emailed by each pair constituted the data of the present study. The findings of the study show that the integration of students' smart mobile devices in the performance of physics experiments in the classroom or in the school lab is possible without posing particular problems. However, this integration presupposes the proper planning by the teacher and the dedication of appropriate time both for the preparation of the students for the activity, and for the installation of the necessary applications in the devices.
Tools Impact on the Quality of Annotations for Chat Untangling

The quality of the annotated data directly influences in the success of supervised NLP models.However, creating annotated datasets is often time-consuming and expensive.Although the annotation tool takes an important role, we know little about how it influences annotation quality.We compare the quality of annotations for the task of chat-untangling made by nonexperts annotators using two different tools.The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links.Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.