Reviewerly/exHarmony huggingface dataset:
Sparse Subspace Clustering for Stream Data

In the past few years, sparse subspace clustering (SSC) has gained many studies and found wide applications. However, SSC suffers from the limitation in scalability. Furthermore, current SSC methods could hardly tackle stream data where the structure of subspaces may change along time. In this paper, we propose a novel method to extend SSC to stream data (StreamSSC). Our method is based on maintaining a small subset of representatives to characterize the structure of the underlying subspaces during stream data. StreamSSC is efficient in both computation and memory. Experimental results on both synthetic and real-world streams demonstrate the effectiveness of StreamSSC. For efficiency, StreamSSC is faster than existing online subspace clustering methods by roughly a magnitude.
Knowledge Big Graph Fusing Ontology with Property Graph: A Case Study of Financial Ownership Network

The scale of know­ledge is growing rapidly in the big data environment, and traditional know­ledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a know­ledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the know­ledge representation model is called know­ledge big graph. In addition, this paper applies the know­ledge big graph model to the ownership network in the China’s financial field and builds a financial ownership know­ledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership know­ledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the know­ledge big graph could provide technical reference for big data know­ledge organization and services.
β-Variational Classifiers Under Attack

Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision. However, despite their popularity, it has been shown that they provide limited robustness in their predictions. In particular, it is possible to synthesise small adversarial perturbations that imperceptibly modify a correctly classified input data, making the network confidently misclassify it. This has led to a plethora of different methods to try to improve robustness or detect the presence of these perturbations. In this paper, we perform an analysis of β-Variational Classifiers, a particular class of methods that not only solve a specific classification task, but also provide a generative component that is able to generate new samples from the input distribution. More in details, we study their robustness and detection capabilities, together with some novel insights on the generative part of the model.
An End-to-End Network for Emotion-Cause Pair Extraction.

The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (∼ 6.5% increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.
Language that Captivates the Audience : Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task

The aim of the paper is twofold: (1) to automatically predict the ratings assigned by viewers to 14 categories available for TED talks in a multi-label classification task and (2) to determine what types of features drive classification accuracy for each of the categories. The focus is on features of language usage from five groups pertaining to syntactic complexity, lexical richness, register-based n-gram measures, information-theoretic measures and LIWC-style measures. We show that a Recurrent Neural Network classifier trained exclusively on within-text distributions of such features can reach relatively high levels of overall accuracy (69%) across the 14 categories. We find that features from two groups are strong predictors of the affective ratings across all categories and that there are distinct patterns of language usage for each rating category.
Question Answering from Brain Activity Data via Decoder Based on Neural Networks

We build a model that can estimate what subjects recognize from functional magnetic resonance imaging (fMRI) data via a visual question answering (VQA) model. The VQA model can generate an answer to a question about an image. We convert fMRI signals into image features via an fMRI decoder based on the relationship between the fMRI signals and the image features extracted from the gazed image. Then this allows the VQA model to answer a visual question from the fMRI signals measured while the subject is gazing at the image. Though brain decoding, which interprets what humans recognize, has become overwhelmingly popular in neuroscience, they often suffer from the small datasets of brain activity data. To overcome the small size of datasets of fMRI signals, we introduce an fMRI decoder based on neural networks that have a high expressive ability. Even when we do not have enough fMRI signals, the proposed method derives the answer to what a person is looking at from fMRI signals. Experimental results on several datasets show that our method allows us to answer a question about gazed images from fMRI signals.
Geochemical Classification and Geotectonic Setting of Granitic Gneisses from Southeastern Margin of Western Nigeria Basement

Chemical whole-rock major oxides and some trace element analyses were done on granitic gneiss rocks located on the southeastern margin of western Nigeria Basement Complex, exposed in parts of Dagbala-Atte District, southwestern Nigeria. This was meant to classify the rocks and to understand the tectonic setting in order to evaluate their crustal evolution. The chemical analyses were done using inductively-coupled plasma mass spectrometer. From the results obtained, these rocks classified into calc-alkaline to shoshonite series with metaluminous to peraluminous varieties, they are I-type granitoids of feroan composition. The granitic gneisses formed from metamorphism of granite and granodiorite. Tectonically, most of the rock samples plotted in the field of island arc, continental arc and continental-collisional granitoids, which indicated that the protolith granite and granodiorite are orogenic and are arc related inferring arc tectonic setting.
Role of AI in Banking

The research paper entitled –“Role of AI in Banking” elucidates how in a promptly digitizing world, Artificial Intelligence has emerged as the future of banking, propagating the power of advanced data analytics in order to withstand fraudulent transactions and improve deference. It incorporates deep learning, predictive analytics, as well as machine learning for enabling an enhanced banking experience. AI assists in fraud detection, credit risk assessment, reduction of costs as well as risk management. Alongside these aspects, the sector is also leveraging for battling with frauds and hacks while simultaneously abiding with KYC and AML compliance regulations.
Analytical Traditions and Game Music: <i>Super Mario Galaxy</i> as a Case Study

Ludomusicologists generally agree that cinema and television represent the nearest siblings to video games, and so therefore adopt many methodologies familiar to film music scholarship in their work. For example, the influential concepts of diegetic and non-diegetic, which respectively describe sounds that exist either within or outside a narrative frame,1 feature prominently in many accounts of game audio, and represent one axis of Karen Collins’s model for the uses of game audio, the other being dynamic and non-dynamic, where dynamic audio can be further subcategorized as adaptive or interactive.2 Ludomusicologists generally also agree that the interactive nature of video games marks its primary distinction from other forms of multimedia, and so a fundamental point of entry into studying game audio is to examine how composers and sound designers create scores and soundtracks that can adapt to indeterminate player actions.
Media Pembelajaran Nyuwun Pirsa untuk Meningkatkan Hasil Belajar Bahasa Jawa Siswa Setingkat Sekolah Dasar

The lack of innovation in the use of instructional media is one of the reasons students get low scores in Javanese language subjects so this research is designed to (1) develop learning media products for Javanese subjects with uploading base material that can run on the Android platform and desktop. (2) Testing the feasibility and effectiveness of video games, and (3) Determining the comparison of results between using conventional learning methods and using interactive multimedia. The method used is: Research and Development (R&D) using the ADDIE development model, as for the stages of development, namely: (1) Analysis, (2) Design, (3) Development, (4) Implementation, (5) Evaluation. The data source involved two media experts to test the feasibility of the media, one material expert and a research subject of 25 respondents from Totosari Elementary School students by dividing 13 experimental group children and 12 control group children, for testing media usability and measuring the comparison of student learning outcomes. The results of this study are: (1) the development of instructional media for Javanese language subjects with uploading base material that can run on the Android and desktop platforms. (2) the feasibility and effectiveness test with the feasibility value of 87,7% and the effectiveness of 75.677% included in the very feasible and effective category. (3) Hypothesis test results show significant differences in student learning outcomes with a significance value of 0.00. The conclusion of this research is the learning media Nyuwun Pirsa is feasible, effective and influential in improving student learning outcomes in Javanese subjects.
Pedagogy in the Time of Pandemic:From Localisation to Glocalisation

Pandemic-the global spread of an initially local disease like COVID-19-bluntly forces us to stop.How do we respond in higher education to such stopping?Whether at the government level, for our universities, or in our classrooms, no existing playbook prescribes the pathway for dealing with a global pandemic of this magnitude, even now as we emerge from total lockdown to the potential for a new tomorrow.What we have done at this juncture is to capture anecdotal responses within and across countries that may highlight trends for later consideration.Unlike a globalised response that would adopt one approach internationally, our study considers adaptations for local differences in a glocalised set of responses in an attempt to identify new paradigms that reconceptualise not only
Intelligent Visualization Interfaces

Visualization transforms large quantities of data into pictures in which relations, patterns, or trends of interest in the data reveal themselves to effectively guide the user in the data reasoning and discovery process. Visualization has become an essential tool in many areas of study that use a data-driven approach to problem solving and decision making. However, when the data is large relational or high-dimensional, it can take both novices and experts substantial effort to derive and interpret visualization results from the data. Following the resurgence of AI and machine learning technology in recent years, in the field of visualization, there is also the growing interest and opportunity in applying AI and machine learning to perform data transformation and to assist in the generation and interpretation of visualization, aiming to strike a balance between cost and performance. In this talk, I will present designs made by my group effectively making use of machine learning for general data visualization and analytics tasks [1, 2, 3, 4, 5, 6], resulting in better visualization interfaces into the data.
Amrita@LT-EDI-EACL2021: Hope Speech Detection on Multilingual Text

Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM) with the attention-based approach, in solving the hope speech detection problem. Using this approach an F1-score of 0.73 (9thrank) in the Malayalam-English data set was achieved from a total of 31 teams who participated in the competition.
Multi-granularity Deep Local Representations for Irregular Scene Text Recognition

Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most networks capture the sequence only from a single-granularity visual representation, which to some extent limits the performance of recognition. In this article, we propose a hierarchical attention network to capture multi-granularity deep local representations for recognizing irregular scene text. It consists of several hierarchical attention blocks, and each block contains a Local Visual Representation Module (LVRM) and a Decoder Module (DM). Based on the hierarchical attention network, we propose a scene text recognition network. The extensive experiments show that our proposed network achieves the state-of-the-art performance on several benchmark datasets including IIIT-5K, SVT, CUTE, SVT-Perspective, and ICDAR datasets under shorter training time.
Improving Students’ Reading Comprehension Skill Using Fable Story Text:

This study is to examine: (1) to describe the implementation of using fable story to improve reading comprehension skill at the first grade of Mts Miftahul Ulum Gondang Rejo Gondang Wetan Pasuruan. (2) To know the students’ responses in reading comprehension skill by using fable story at first grade of MTs Miftahul Ulum Gondangrejo Gondangwetan Pasuruan. The researcher is carried out at Mts Miftahul Ulum Gondangrejo Gondangwetan Pasuruan on 23 April 2019, the method used for the research is descriptive qualitative. The data collection technique which is used in this research is observation checklist; it is used to obtain the data of the implementation of the fabled story to improve reading comprehension skill. The result of students responses that are taken from the interview, most of them gave positive reactions in reading comprehension skill by using fable story. And from the score of daily assessment, there are 13 students from 20 students answer the researcher’s question by the excellent answer in the 1st meeting and 15 students from 20 students respond in the 2nd meeting. It's mean that the responses students of MTs Miftahul Ulum Gondangrejo Gondangwetan Pasuruan are interested in the implementation of fable story to be the source of learning reading comprehension skill. Finally, the research can support further researcher. And it is recommended for the English teacher to apply fable story as a source in teaching reading comprehension skill. Moreover, students can comprehend simple text easily
Computing with words for student peer assessment in oral presentation

Peer assessment in an oral presentation can motivate and give more sense of responsibility to students. In recent years, various methods have been proposed to evaluate peers. In this paper, a novel peer online assessment method is proposed for oral presentation using perceptual computing. The output of the proposed system can be a numerical score for the overall assessment of a student in the presentation, which allows comparison and ranking of student performance. Furthermore, a linguistic evaluation that describes the student's performance is obtained from the system. A case study has been conducted to show the effectiveness of the proposed method; then the results are analyzed and reviewed.
Simultaneous Image Reconstruction and Feature Learning with 3D-CNNs for Image Set–Based Classification

Image set–based classification has attracted substantial research interest because of its broad applications. Recently, lots of methods based on feature learning or dictionary learning have been developed to solve this problem, and some of them have made gratifying achievements. However, most of them transform the image set into a 2D matrix or use 2D convolutional neural networks (CNNs) for feature learning, so the spatial and temporal information is missing. At the same time, these methods extract features from original images in which there may exist huge intra-class diversity. To explore a possible solution to these issues, we propose a simultaneous image reconstruction with deep learning and feature learning with 3D-CNNs (SIRFL) for image set classification. The proposed SIRFL approach consists of a deep image reconstruction network and a 3D-CNN-based feature learning network. The deep image reconstruction network is used to reduce the diversity of images from the same set, and the feature learning network can effectively retain spatial and temporal information by using 3D-CNNs. Extensive experimental results on five widely used datasets show that our SIRFL approach is a strong competitor for the state-of-the-art image set classification methods.
Augmented Reality Guided Retrosigmoid Approach

While medical imaging data has traditionally been viewed on 2D displays, medical augmented reality (AR) allows physicians to project the medical imaging data on patient's bodies. An important application of medical AR is intra-operative surgical guidance by providing the physician with the ability to “see through” the patient's skin and locate important anatomy.
Algebraic Supports and New Forms of the Hidden Discrete Logarithm Problem for Post-quantum Public-key Cryptoschemes

This paper introduces two new forms of the hidden discrete logarithm problem defined over a finite non-commutative associative algebras containing a large set of global single-sided units. The proposed forms are promising for development on their base practical post-quantum public key-agreement schemes and are characterized in performing two different masking operations over the output value of the base exponentiation operation that is executed in framework of the public key computation. The masking operations represent homomorphisms and each of them is mutually commutative with the exponentiation operation. Parameters of the masking operations are used as private key elements. A 6-dimensional algebra containing a set of p3 global left-sided units is used as algebraic support of one of the hidden logarithm problem form and a 4-dimensional algebra with p2 global right-sided units is used to implement the other form of the said problem. The result of this paper is the proposed two methods for strengthened masking of the exponentiation operation and two new post-quantum public key-agreement cryptoschemes. Mathematics subject classification: 94A60, 16Z05, 14G50, 11T71, 16S50.
NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers

The increasing accessibility of the internet facilitated social media usage and encouraged individuals to express their opinions liberally. Nevertheless, it also creates a place for content polluters to disseminate offensive posts or contents. Most of such offensive posts are written in a cross-lingual manner and can easily evade the online surveillance systems. This paper presents an automated system that can identify offensive text from multilingual code-mixed data. In the task, datasets provided in three languages including Tamil, Malayalam and Kannada code-mixed with English where participants are asked to implement separate models for each language. To accomplish the tasks, we employed two machine learning techniques (LR, SVM), three deep learning (LSTM, LSTM+Attention) techniques and three transformers (m-BERT, Indic-BERT, XLM-R) based methods. Results show that XLM-R outperforms other techniques in Tamil and Malayalam languages while m-BERT achieves the highest score in the Kannada language. The proposed models gained weighted f_1 score of 0.76 (for Tamil), 0.93 (for Malayalam ), and 0.71 (for Kannada) with a rank of 3rd, 5th and 4th respectively.
The Hilbert–Schmidt norm as a measure of entanglement in spin-1/2 Heisenberg chain: generalized Bell inequality and distance between states

Entanglement calculations have received renewed interest with the advances observed in the field of quantum computing. Alternative methods to calculate entanglement that can be applied to broader classes of problems have become increasingly necessary. In this letter, we show that the measure of entanglement using the generalized Bell inequality and the distance between states coincide when we use the Hilbert–Schmidt norm. Our conclusions apply to the spin-1/2 Heisenberg chains with the interaction between the first neighbours.
RETRACTED: Prediction Method of Innovation and Entrepreneurship Intention of Computer Majors Based on Artificial Intelligence Technology

Abstract With the rapid development of artificial intelligence technology, the cycle of transforming scientific and technological achievements into actual productivity is getting shorter and shorter, and technological progress and innovation and entrepreneurship have become decisive factors in economic and social development. However, the current stage of innovation and entrepreneurship education in my country has not been integrated into computer majors, which has reduced the effectiveness of innovation and entrepreneurship for computer majors. In this regard, the purpose of this article is to study the method of predicting innovation and entrepreneurial intentions of computer majors based on artificial intelligence technology. This article first understands and analyzes the current situation of innovation and entrepreneurship education in computer majors through questionnaires, visits and analysis, and analyzes the problems faced by innovation and entrepreneurship education in computer majors at this stage. After empirical analysis of the collected data, the experimental results show that the theoretical model based on artificial intelligence technology can effectively analyze the influencing factors of college students’ innovation and entrepreneurship intentions, and plan and guide them, and the degree of recognition of college students’ innovation and entrepreneurship has also been obtained. An improvement of about 20%, which has important guiding significance for the innovation and entrepreneurship of computer majors.