Reviewerly/exHarmony huggingface dataset:
Align Voting Behavior with Public Statements for Legislator Representation Learning

Ideology of legislators is typically estimated by ideal point models from historical records of votes.It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior.However, it fails to capture more specific attitudes of legislators toward emerging issues and is unable to model newly-elected legislators without voting histories.In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators.In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter.In practice, we construct a heterogeneous graph for the legislative context and use relational graph neural networks to learn the representation of legislators with the guidance of historical records of their voting and hashtag usage.Experiment results indicate that our model yields significant improvements for the task of roll call vote prediction.Further analysis further demonstrates that legislator representation we learned captures nuances in statements.
Learning to Enhance Visual Quality via Hyperspectral Domain Mapping (Student Abstract)

Deep learning based methods have achieved remarkable success in image restoration and enhancement, but a majority of such methods rely on RGB input images. These methods fail to take into account the rich spectral distribution of natural images. We propose a deep architecture, SpecNet which computes spectral profile to estimate pixel-wise dynamic range adjustment of a given image. First, we employ an unpaired cycle-consistent framework to generate hyperspectral images (HSI) from low-light input images. HSI are further used to generate a normal light image of the same scene. In order to infer a plausible HSI from a RGB image we incorporate a self-supervision and a spectral profile regularization network. We evaluate the benefits of optimizing the spectral profile for real and fake images in low-light conditions on the LOL Dataset.
An Examination on Cloud Computing Future, Privacy of Data and Possibilities

Cloud computing is the theoretical basis for future computing. All the global frameworks are now looking up to architecture which is purely based on cloud. Being the core of such a large web of network, it is important to consider the security aspects in a cloud based computing environment. This has resulted in a new research trend on the security issues of cloud. Cloud is a popular paradigm with extreme abilities and benefits for trending ICT environment. On the other end the major concern came in terms of security and privacy while adopting the cloud technology. This article is an effort to cover the challenges in fields like storage, virtualization and communication in cloud .Also it is a try to elaborate relevance of current cryptographic approach in order to increase security of cloud in ICT.
Quantum tricritical behavior and multistable macroscopic quantum states in generalized Dicke model

In this article, we obtain the multistable macroscopic quantum states and quantum tricritical behavior in generalized Dicke model (DM) analytically by means of spin-coherent-state (SCS) variational method for a finite atom number N. The quantum phase transition (QPT) is characterized by the ground state from the normal phase (NP) to the superradiant phases (SP). The second-order QPT turns to the first-order one at the tricritical point of phase boundary. The tricritical behavior of QPT emerges when the coupling strength of the symmetry breaking mechanism increases to a certain extent, which leads to an additional dimension of phase diagram. Beyond the ground state the symmetry-breaking coupling can manipulate the pseudospin flip of higher-level macroscopic states. The critical property of QPT is also demonstrated from the average photon-number, the atomic population as well as Berry phase.
MEDIA LITERACY AS ONE OF THE COMPONENTS OF THE PROCESS OF PREPARATION OF A MODERN TEACHER OF VOCATIONAL TRAINING

The formation of media information literacy and media culture in accordance with their age, individual and other characteristics is defined as one of the priority areas of media education in Ukraine. That is why in the process of integration of Ukrainian education into the European space and providing the state with measures for information security of the country, the issues of media literacy of modern teachers, including professional teachers, become especially acute and relevant. The purpose of this article is to determine the content of the concept of «media literacy», analysis of the legal framework for the introduction of media education in Ukraine and foreign and domestic experience in the formation of media literacy in future teachers of professional training. The following research methods were used in the work: empirical method of collecting and systematizing materials related to the study of media literacy in future teachers of professional training; terminological analysis, as a result of which the definitions of a number of terms were specified: media literacy, media competence, media education, media pedagogy, professional training, teacher of professional training. The presented material of the article allowed to make the following conclusions: the analysis of foreign and domestic experience of formation of media literacy at future teachers of vocational training gave the chance to define the maintenance of concept «media literacy of the modern teacher of professional training». The analysis of the legal framework for the introduction of media education in Ukraine showed that the media educator, who is included in the media education system and has media literacy skills, is a key figure at all levels of modern education: from preschool to higher.
Pengenalan Wajah Menggunakan PCA dengan Memperhatikan Jumlah Data Latih dan Vektor Eigen

To find out if an employee is present, attendance is usually used. Attendance can be done in several ways, one of which is by filling in the attendance list that has been provided (manual attendance). However, this method is less effective because there is a possibility that employees who are not present will entrust attendance to employees who are present. Therefore, other ways are needed so that this does not happen. In this study, attendance was carried out using facial recognition. Face recognition is one of the fields used to recognize someone. A person's face usually has special characteristics that are easily recognized by people. These special characteristics are also called features. In this study, these features can be searched using the Principle Component Analysis (PCA) method. The PCA method is one of the methods used to produce features by reducing dimensions using eigenvectors from facial images (eigenface). The facial image used in this study consisted of 40 people with each person having 10 facial images with various expressions. Image data is divided into two parts, namely training data and test data. In this study, it is proposed to pay attention to the amount of training data and the number of eigenvectors used to get the best level of accuracy. From the research results, the highest level of accuracy occurs when the training data for each person is 7 and the test data for each person is 3 with an accuracy rate of 96.67%.
Does handwriting impact learning on math tutoring systems?

Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers' keyboard and mouse.However, students usually solve math tasks using paper and pen.Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load, impairing their learning.Our study investigates the impact of the data input method on students' learning and fluency in solving equations using step-based math ITSs.More specifically, we have considered the standard typing and handwriting input methods.We hypothesized that the students would be more fluent using their handwriting with online recognition to solve math equations than using the typing input method.This fluency indicates a reduction in cognitive load, freeing working memory for logical reasoning instead of interface preconditions, leading to improved learning.We have conducted an experiment with 55 seventh-grade students from a private school to validate the hypothesis, randomly assigned to control and experimental groups.Each group used one of the input methods on two different devices (desktop computers and tablets).Although students using handwriting solved more equations and were faster than students who typed their equations, we could not find statistically significant differences in the learning between students that used typing or handwriting.Additionally, we have found that the input method used in a not ideal device (e.g., handwriting with a computer's mouse instead of using a touch screen device) can negatively affect the students' performance.
Survey towards Masked Face Detection for Pandemic

Covid19 has given another personality for wearing a veil.It is significant when these covered countenances are recognized precisely and proficiently.As an exceptional face recognition task, face veil identification is substantially more troublesome on account of outrageous impediments which prompt the deficiency of face subtleties.Furthermore, there is basically no current enormous scope precisely marked concealed face dataset, which increments the trouble of face veil discovery.The framework urges to utilize CNN-based profound learning calculations which have done tremendous advancement towards explores in face identification.In this paper, propose a novel CNN-based technique that is shaped by three convolutional neural organizations to recognize the face veil.Plus, in view of the deficiency of face veiled preparing tests, propose another dataset called" face cover dataset" to tweak CNN models.Assess proposed face veil recognition calculation on the face cover testing set, and it accomplishes agreeable execution.
Blockchain and Bitcoin Scripts

Blockchain technology is an innovation that enables distribution of digitized information and was originally designed for the bitcoin cryptocurrency. The authors Don and Alex Tapscott of “Blockchain Revolution” defined blockchain as a scrupulous digital ledger of economic transactions that is programmed to record not only financial transactions but everything that has a value.
Study of Sentiment on Google Play Store Applications

This paper proposes an approach to perform sentiment analysis on Google play store’s applications considering Kaggle dataset. The dataset includes 10,000 applications with their ratings, reviews, number of download and other application related parameters. The dataset is pre-processed using a number of data cleaning steps, including data reduction, tokenization, stemming etc. For classification of sentiment polarity, a logistic regression model has been proposed. The logistic regression model has been extended to a tri-polar classifier. Accuracy of 81.1% was achieved for document-level classification of reviews. The top two applications, ‘Candy Crush Saga’ and ‘Clash of Clans’, have been selected for the analysis based on popularity. Even though the ‘family’ category has a higher number of applications on the store, games are much more popular on Google's play store. Results show that these apps, though popular, receive a higher number of negative reviews. The sentiment of play store users gives us an idea of how the applications market reacts, what are the needs and how to succeed in the android market. The opinion of the audience is an essential component for every business. These reviews prove to be useful for further development and improvement of the applications.
Accelerating Neural Machine Translation with Partial Word Embedding Compression

Large model size and high computational complexity prevent the neural machine translation (NMT) models from being deployed to low resource devices (e.g. mobile phones). Due to the large vocabulary, a large storage memory is required for the word embedding matrix in NMT models, in the meantime, high latency is introduced when constructing the word probability distribution. Based on reusing the word embedding matrix in the softmax layer, it is possible to handle the two problems brought by large vocabulary at the same time. In this paper, we propose Partial Vector Quantization (P-VQ) for NMT models, which can both compress the word embedding matrix and accelerate word probability prediction in the softmax layer. With P-VQ, the word embedding matrix is split into two low dimensional matrices, namely the shared part and the exclusive part. We compress the shared part by vector quantization and leave the exclusive part unchanged to maintain the uniqueness of each word. For acceleration, in the softmax layer, we replace most of the multiplication operations with the efficient looking-up operations based on our compression to reduce the computational complexity. Furthermore, we adopt curriculum learning and compact the word embedding matrix gradually to improve the compression quality. Experimental results on the Chinese-to-English translation task show that our method can reduce 74.35% of parameters of the word embedding and 74.42% of the FLOPs of the softmax layer. Meanwhile, the average BLEU score on the WMT test sets only drops 0.04.
Candidate box fusion based approach to adjust position of the candidate box for object detection

The method of object detection has been applied to all aspects in our lives. Although object detection methods based on deep learning have been widely used in various fields, there are still some overlooked problems in the candidate box selection stage. The detection results of traditional candidate box selection methods can only select a relatively optimal maximum candidate box. If the maximum candidate box is still not accurate enough, this type of methods will not be able to do adjust it. To solve this problem, an object detection method based on the multiple candidate box fusion is proposed. The method can not only retain the maximum candidate box and delete the non-maximum candidate box, but also adjust the position of the maximum candidate box again. Thereby a more accurate maximum candidate box can be obtained. In order to verify the generalization ability of the method, the candidate box fusion method is combined with the two object detection frameworks: faster R-CNN model and YOLOv3 model. The results of these experiments prove that the proposed method can achieve higher detection accuracy and complete the object detection task more effectively.
No more hand-tuning rewards : masked constrained policy optimization for safe reinforcement learning

In safe Reinforcement Learning (RL), the agent attempts to find policies which maximize the expectation of accumulated rewards and guarantee its safety to remain above a given threshold. Hence, it is straightforward to formalize safe RL problems by both a reward function and a safety constraint. We define safety as the probability of survival in environments where taking risky actions could lead to early termination of the task. Although the optimization problem is already constrained by a safety threshold, reward signals related to unsafe terminal states influence the original maximization objective of the task. Selecting the appropriate value of these signals is often a time consuming and challenging reward engineering task, which requires expert knowledge of the domain. This paper presents a safe RL algorithm, called Masked Constrained Policy Optimization (MCPO), in which the learning process is constrained by safety and excludes the risk reward signals. We develop MCPO as an extension of gradient-based policy search methods, in which the updates of the policy and the expected reward models are masked. Our method benefits from having a high probability of satisfying the given constraints for every policy in the learning process. We validate the proposed algorithm in two continuous tasks. Our findings prove the proposed algorithm is able to neglect risk reward signals, and thereby resolving the desired safety-performance trade-off without having the need for hand-tuning rewards.
Towards an Automatic Generation of Persuasive Messages

In the last decades, the Natural Language Generation (NLG) methods have been improved to generate text automatically. However, based on the literature review, there are not works on generating text for persuading people. In this paper, we propose to use the SentiGAN framework to generate messages that are classified into levels of persuasiveness. And, we run an experiment using the Microtext dataset for the training phase. Our preliminary results show 0.78 of novelty on average, and 0.57 of diversity in the generated messages.
Optimal Dynamic Regret in Exp-Concave Online Learning

We consider the problem of the Zinkevich (2003)-style dynamic regret minimization in online learning with \emph{exp-concave} losses. We show that whenever improper learning is allowed, a Strongly Adaptive online learner achieves the dynamic regret of $\tilde O^*(n^{1/3}C_n^{2/3} \vee 1)$ where $C_n$ is the \emph{total variation} (a.k.a. \emph{path length}) of the an arbitrary sequence of comparators that may not be known to the learner ahead of time. Achieving this rate was highly nontrivial even for square losses in 1D where the best known upper bound was $O(\sqrt{nC_n} \vee \log n)$ (Yuan and Lamperski, 2019). Our new proof techniques make elegant use of the intricate structures of the primal and dual variables imposed by the KKT conditions and could be of independent interest. Finally, we apply our results to the classical statistical problem of \emph{locally adaptive non-parametric regression} (Mammen, 1991; Donoho and Johnstone, 1998) and obtain a stronger and more flexible algorithm that do not require any statistical assumptions or any hyperparameter tuning.
Multi-Proxy Wasserstein Classifier for Image Classification

Most widely-used convolutional neural networks (CNNs) end up with a global average pooling layer and a fully-connected layer. In this pipeline, a certain class is represented by one template vector preserved in the feature banks of fully-connected layer. Yet, a class may have multiple properties useful for recognition while the above formulation only captures one of them. Therefore, it is desired to represent a class by multiple proxies. However, directly adding multiple linear layers turns out to be a trivial solution as no improvement can be observed. To tackle this problem, we adopt optimal transport theory to calculate a non-uniform matching flow between the elements in the feature map of a sample and the proxies of a class in a closed way. By doing so, the models are enabled to achieve partial matching as both the feature maps and the proxy set can now focus on a subset of elements from the counterpart. Such formulation also enables us to embed the samples into the Wasserstein metric space, which has many advantages over the original Euclidean space. This formulation can be achieved by a lightweight iterative algorithm, which can be easily embedded into the automatic differentiation framework. Empirical studies are performed on two widely-used classification datasets, CIFAR, and ILSVRC2012, and the substantial improvements on these two benchmarks demonstrate the effectiveness of our method.
Creation and Use of Medical Histology‐Pathology Electronic Flashcards

To facilitate medical students' learning of the newly integrated histology‐pathology course components of the undergraduate medical school curriculum at the University of British Columbia (UBC), we created electronic study flashcards. Modelled after the histopathology curriculum, the flashcards are designed to utilize spaced repetition learning and to facilitate the recall of normal histologic structures and associated prototypical pathological changes in tissues and/or cells. Nearly nine hundred Anki software‐based flashcards were created, with content focussed on annotated histology and pathology virtual slides. Distributed to and used by students online, each flashcard opens with arrows indicating unlabelled structures and asks students to “name the following” prior to clicking on “show answer” to confirm answers. There are also second‐order flashcards prompting students to recall relevant aspects related to the unidentified tissues. Survey results demonstrate that the flashcards provide an effective means of augmenting students' learning and developing long‐term understanding and recall: 89% of responding medical students agreed that the flashcards are helpful for improving knowledge and exam preparation. This UBC histology‐pathology initiative is engaging and efficacious in enhancing student learning and is being used to promote learning in two core foundational medical sciences (histology and pathology). The software and its uses can easily be extrapolated to all of the foundational sciences and therefore it will be of interest to students and faculty in any of the medical and allied fields. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Representing Syntax and Composition with Geometric Transformations

The exploitation of syntactic graphs (SyGs) as a word's context has been shown to be beneficial for distributional semantic models (DSMs), both at the level of individual word representations and in deriving phrasal representations via composition. However, notwithstanding the potential performance benefit, the syntactically-aware DSMs proposed to date have huge numbers of parameters (compared to conventional DSMs) and suffer from data sparsity. Furthermore, the encoding of the SyG links (i.e., the syntactic relations) has been largely limited to linear maps. The knowledge graphs' literature, on the other hand, has proposed light-weight models employing different geometric transformations (GTs) to encode edges in a knowledge graph (KG). Our work explores the possibility of adopting this family of models to encode SyGs. Furthermore, we investigate which GT better encodes syntactic relations, so that these representations can be used to enhance phrase-level composition via syntactic contextualisation.
Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation

The success of emotional conversation systems depends on sufficient perception and appropriate expression of emotions. In a real-world conversation, we firstly instinctively perceive emotions from multi-source information, including the emotion flow of dialogue history, facial expressions, and personalities of speakers, and then express suitable emotions according to our personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, we propose a heterogeneous graph-based model for emotional conversation generation. Specifically, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. After that, we employ an Emotion-Personality-Aware Decoder to generate a response not only relevant to the conversation context but also with appropriate emotions, by taking the encoded graph representations, the predicted emotions from the encoder and the personality of the current speaker as inputs. Experimental results show that our model can effectively perceive emotions from multi-source knowledge and generate a satisfactory response, which significantly outperforms previous state-of-the-art models.