Reviewerly/exHarmony huggingface dataset:
Revisiting Online Quantum State Learning

In this paper, we study the online quantum state learning problem which is recently proposed by Aaronson et al. (2018). In this problem, the learning algorithm sequentially predicts quantum states based on observed measurements and losses and the goal is to minimize the regret. In the previous work, the existing algorithms may output mixed quantum states. However, in many scenarios, the prediction of a pure quantum state is required. In this paper, we first propose a Follow-the-Perturbed-Leader (FTPL) algorithm that can guarantee to predict pure quantum states. Theoretical analysis shows that our algorithm can achieve an O(√T) expected regret under some reasonable settings. In the case that the pure state prediction is not mandatory, we propose another deterministic learning algorithm which is simpler and more efficient. The algorithm is based on the online gradient descent (OGD) method and can also achieve an O(√T) regret bound. The main technical contribution of this result is an algorithm of projecting an arbitrary Hermitian matrix onto the set of density matrices with respect to the Frobenius norm. We think this subroutine is of independent interest and can be widely used in many other problems in the quantum computing area. In addition to the theoretical analysis, we evaluate the algorithms with a series of simulation experiments. The experimental results show that our FTPL method and OGD method outperform the existing RFTL approach proposed by Aaronson et al. (2018) in almost all settings. In the implementation of the RFTL approach, we give a closed-form solution to the algorithm. This provides an efficient, accurate, and completely executable solution to the RFTL method.
Relaxed Locally Correctable Codes with Nearly-Linear Block Length and Constant Query Complexity

Locally correctable codes (LCCs) are codes C : Σ k → Σ n which admit local algorithms that can correct any individual symbol of a corrupted codeword via a minuscule number of queries.One of the central problems in algorithmic coding theory is to construct O(1)-query LCC with minimal block length.Alas, state-of-the-art of such codes requires exponential block length to admit O(1)-query algorithms for local correction, despite much attention during the last two decades.This lack of progress prompted the study of relaxed LCCs, which allow the correction algorithm to abort (but not err) on small fraction of the locations.This relaxation turned out to allow constant-query correction algorithms for codes with polynomial block length.Specifically, prior work showed that there exist O(1)-query relaxed LCCs that achieve nearly-quartic block length n = k 4+α , for an arbitrarily small constant α > 0.We construct an O(1)-query relaxed LCC with nearlylinear block length n = k 1+α , for an arbitrarily small constant α > 0. This significantly narrows the gap between the lower bound which states that there are no O(1)-query relaxed LCCs with block length n = k 1+o(1) .In particular, this resolves an open problem raised by Gur, Ramnarayan, and Rothblum (ITCS 2018).
Perancangan Aplikasi E-Learning Berbasis Web dengan Model Prototype pada SMPN 7 Kota Tangerang Selatan

Learning is the process of student interaction with educators and learning resources in a learning environment that includes teachers and students who exchange information. Therefore, learning must go well so that information can be delivered. Educators who are unable to attend will disrupt the delivery of learning material. Likewise, for students who are unable to attend will certainly miss the learning material provided by educators. With E-Learning making it easy in the process of teaching and learning activities in schools, teachers can provide materials and assignments anywhere, students are easy to get learning materials, and make it easy for students to do the assignments given by the teacher, without being hindered by the dimensions of space and time. The system development method used by the authors in conducting this research is the Prototype method. The Prototype method is prototyping software or life cycle using prototyping. One method of system life cycle is based on the concept of a working model. Where the system developer and client can see and conduct experiments since the beginning of the development process. In the Prototype method there are three stages (3) development cycle, namely: Requirement Collection Phase, Prototype Building Phase, and Trial Phase.
Causally Denoise Word Embeddings Using Half-Sibling Regression

Distributional representations of words, also known as word vectors, have become crucial for modern natural language processing tasks due to their wide applications. Recently, a growing body of word vector postprocessing algorithm has emerged, aiming to render off-the-shelf word vectors even stronger. In line with these investigations, we introduce a novel word vector postprocessing scheme under a causal inference framework. Concretely, the postprocessing pipeline is realized by Half-Sibling Regression (HSR), which allows us to identify and remove confounding noise contained in word vectors. Compared to previous work, our proposed method has the advantages of interpretability and transparency due to its causal inference grounding. Evaluated on a battery of standard lexical-level evaluation tasks and downstream sentiment analysis tasks, our method reaches state-of-the-art performance.
Webvr Human-Centered Indoor Layout Design Framework Using a Convolutional Neural Network and Deep Q-Learning

With the rapid development of Web Virtual Reality (WebVR) technology, increasing focus has been placed on this domain. WebVR indoor scenario design studies have been of important value in both academia and industry. However, many bottlenecks still need to be overcome, such as the weak computing capacity and limited memory space available in web browsers. In particular, there are many deficiencies in virtual scenes, i.e., lack of fidelity, low automation and poor scene interactability. In this paper, we propose a novel WebVR indoor furniture layout design framework to enhance the capabilities of automatic furniture layout and significantly enhance the interactiveness of virtual scenarios. In particular, we present a hand-drawn sketch recognition scheme based on the ResNet convolutional neural network (CNN), which can strongly improve scenario interactiveness by allowing the user to conveniently add new furniture by means of free-hand drawing operations rather than tedious manual drag-and-pull operations. In addition, based on a deep Q-learning network (DQN), the best positions (states) for these pieces of furniture (agents) in virtual scenarios can be automatically determined, making it easy to satisfy popular design principles. Finally, we report experiments conducted to validate the feasibility of our proposed framework, and the results fully demonstrate that this framework is completely feasible.
Multi-Class Learning from Label Proportions for Bank Customer Classification

In this paper, we study multi-class learning from label proportions, and apply it to bank customer classification, in order to provide advice for banks to better manage customer relationships. We attempt to apply the multi-class extreme learning machine (ELM) to learning from label proportions (LLP). With a structure similar to neural network, ELM possesses higher computational speed and better generalization ability. As a result, it is suitable to deal with large-scale and multi-class problems. LLP is a learning problem, which classifies training data into bags where only the label proportions of each class in each bag is available. Furthermore, in order to maintain the stable model accuracy when the bag sizes increasing, we manage to add small number of labeled samples in our model, called LLP-ELM with in a semi-supervised learning framework. The experiments prove that our improvement has advantages in the case of large bag sizes. In practical, it is worth to consider to apply the proposed algorithm to multi-class learning from label proportions for bank customer classification and other multi-class scenarios.
Semi-Supervised Learning for Maximizing the Partial AUC

The partial area under a receiver operating characteristic curve (pAUC) is a performance measurement for binary classification problems that summarizes the true positive rate with the specific range of the false positive rate. Obtaining classifiers that achieve high pAUC is important in a wide variety of applications, such as cancer screening and spam filtering. Although many methods have been proposed for maximizing the pAUC, existing methods require many labeled data for training. In this paper, we propose a semi-supervised learning method for maximizing the pAUC, which trains a classifier with a small amount of labeled data and a large amount of unlabeled data. To exploit the unlabeled data, we derive two approximations of the pAUC: the first is calculated from positive and unlabeled data, and the second is calculated from negative and unlabeled data. A classifier is trained by maximizing the weighted sum of the two approximations of the pAUC and the pAUC that is calculated from positive and negative data. With experiments using various datasets, we demonstrate that the proposed method achieves higher test pAUCs than existing methods.
Age and Gender Differences in Evaluating the Pedagogical Usability of E-Learning Materials

The purpose of the study is to examine the pedagogical usability of interactive e-learning materials for foreign language practice. It is based upon two studies of the expected between-group and within-group differences among participants in the educational process. The sample consists of two groups – lecturers and students, a total of 100 participants, each evaluating four materials specifically prepared for this study. Two consecutive repeated measures ANOVA were conducted in which the gender/age, the position of the participants in the educational process, and usability dimensions were the independent variables. Results indicated that all independent variables and their interactions have a significant effects on the evaluations of the pedagogical usability. Women tend to assign higher values than men. Аge groups generally differ in their evaluations, although there is a tendency to give similar ratings for the individual dimensions of pedagogical usability. The 31-40 years age group evaluates the materials higher while the lowest evaluations are given by the groups of 21-30 and 50+ year old participants. Students tend to rate the pedagogical usability systemically higher than the lecturers. Usability dimensions also have a significant effect on evaluations. The most prominent feature of the materials, by a great margin, is their Applicability. The findings corroborate previous research which show age and gender differences in web usability do exist. We conclude that these differences exist as much in pedagogical usability as in technical usability. Further investigations are suggested to explore more deeply the differences in the perceived pedagogical value of e-learning materials as this has implications for instructional designers, teachers and learners alike.
Integrating Overlapping Datasets Using Bivariate Causal Discovery

Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dataset. In many cases this assumption fails. Previous approaches to overcoming this shortcoming devised algorithms that returned all joint causal structures consistent with the conditional independence information contained in each individual dataset. But, as conditional independence tests only determine causal structure up to Markov equivalence, the number of consistent joint structures returned by these approaches can be quite large. The last decade has seen the development of elegant algorithms for discovering causal relations beyond conditional independence, which can distinguish among Markov equivalent structures. In this work we adapt and extend these so-called bivariate causal discovery algorithms to the problem of learning consistent causal structures from multiple datasets with overlapping variables belonging to the same generating process, providing a sound and complete algorithm that outperforms previous approaches on synthetic and real data.
Improving visual relationship detection using linguistic and spatial cues

Detecting visual relationships in an image is important in an image understanding task. It enables higher image understanding tasks, that is, predicting the next scene and understanding what occurs in an image. A visual relationship comprises of a subject, a predicate, and an object, and is related to visual, language, and spatial cues. The predicate explains the relationship between the subject and object and can be categorized into different categories such as prepositions and verbs. A large visual gap exists although the visual relationship is included in the same predicate. This study improves upon a previous study (that uses language cues using two losses) and a spatial cue (that only includes individual information) by adding relative information on the subject and object of the extant study. The architectural limitation is demonstrated and is overcome to detect all zero-shot visual relationships. A new problem is discovered, and an explanation of how it decreases performance is provided. The experiment is conducted on the VRD and VG datasets and a significant improvement over previous results is obtained.
Readiness of first-year students of Russian universities to study using massive open online courses

The article discusses issues related to the possibility of introducing mass open online courses (МООС) in the educational process of first-year full-time students majoring in technical directions. The results of the analysis of the experience of using MOOС in the educational process of Russian universities with identified advantages and disadvantages are presented. MOOC-technology is presented as an element of connectivism theory. The author conducted a study with the purpose to study the readiness of students to learn using MOOCs. The results of the study allow to conclude that the modern first-year student has an insufficient level of motivation, independence, responsibility, self-discipline and self-control for the effective use of МООС in the educational process, but they are quite productive in mastering the material of the discipline in a combined form.
Adaptive Random Testing for XSS Vulnerability

XSS is one of the common vulnerabilities in web applications. Many black-box testing tools may collect a large number of payloads and traverse them to find a payload that can be successfully injected, but they are not very efficient. And previous research has paid less attention to how to improve the efficiency of black-box testing to detect XSS vulnerability. To improve the efficiency of testing, we develop an XSS testing tool. It collects 6128 payloads and uses a headless browser to detect XSS vulnerability. The tool can discover XSS vulnerability quickly with the ART(Adaptive Random Testing) method. We conduct an experiment using 3 extensively adopted open source vulnerable benchmarks and 2 actual websites to evaluate the ART method. The experimental results indicate that the ART method can effectively improve the fuzzing method by more than 27.1% in reducing the number of attempts before accomplishing a successful injection.
Fast and Adaptive Boosting Techniques for Variational Based Image Restoration

Variational based problems are an important class of problems and have a space of improvement in image processing.Boosting techniques have been shown capable of improving many image restoration algorithms.This paper considers four fast and adaptive boosting techniques for variational based image restoration.The adaptive boosting frameworks can compute the existing image restoration algorithm iteratively.The primary idea is to get an enhanced result by using the output of the current step as a part of the input for the next step.Our techniques can boost variational based regularization models like total variation (TV) and total generalized variation (TGV).For image restoration, we used an adaptive regularization parameter selection, which produces signals with more details and preserves tiny objects.For efficient numerical optimization, we implement the alternating direction method of multipliers (ADMM) and demonstrate the effectiveness of the proposed techniques with a variety of experimental results.The simulation results show that the proposed boosting techniques achieve a better restoration performance on comparisons with TV and TGV in terms of quality metrics such as signal to noise ratio (SNR) and structure similarity (SSIM).
Multi-lingualWikipedia Summarization and Title Generation On Low Resource Corpus

MultiLing 2019 Headline Generation Task on Wikipedia Corpus raised a critical and practical problem: multilingual task on low resource corpus.In this paper we proposed Quality-Diversity Automatic Summarization(QDAS) model enhanced by sentence2vec and try to apply transfer learning based on large multilingual pre-trained language model for Wikipedia Headline Generation task.We treat it as sequence labeling task and develop two schemes to handle with it.Experimental results have shown that large pre-trained model can effectively utilize learned knowledge to extract certain phrase using low resource supervised data.
DEVELOPING SOFTWARE ENGINEERING STUDENTS’ READINESS FOR PROFESSIONAL MOBILITY THROUGH BLENDED LEARNING

The article aims to study the formation of the software engineering students’ readiness for professional mobility in the educational and information environment of a technical university. The hypothesis of our research is the assumption that the formation of students’ readiness for professional mobility depends on the following organisational and pedagogical conditions: 1) the development of students’ positive intrinsic motivation for training and future professional activity using interactive methods and IT); 2) integration of the structure and content of foreign language with professional training by modelling professional activities through blended learning; 3) synergy of psychological and pedagogical process taking into account the components of the teacher’s professional competence (a tutor, a moderator, a scientist, a designer, and a director). The pedagogical experiment was conducted at the National Aviation University and included three stages: preparatory, formative and final. In total, 158 students of I-V years of study majoring in software engineering took part in various stages of the experiment. 26 students made up an experimental group (EG), 24 – a control group (CG). The criteria and indicators for assessing the students’ readiness for professional mobility and the methods of evaluation were determined. The results of the experiment have proven the proposed hypothesis. The indicators of the productive and creative levels of readiness by all the criteria considerably increased in the EG, while in the CG a slight positive dynamics can be seen. Promising directions for further research can be related to the development of an integrative ESP Moodle course for future software engineers.
A Recurrent Model for Collective Entity Linking with Adaptive Features

The vast amount of web data enables us to build knowledge bases with unprecedented quality and coverage. Named Entity Disambiguation (NED) is an important task that automatically resolves ambiguous mentions in free text to correct target entries in the knowledge base. Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models. However, deep learning models are more complex, requiring large amount of training data and lengthy training and parameter tuning time. In this paper, we revisit traditional machine learning techniques and propose a light-weight, tuneable and time-efficient method without using deep learning or deep learning generated features. We propose novel adaptive features that focus on extracting discriminative features to better model similarities between candidate entities and the mention's context. We learn a local ranking model based on traditional and the new adaptive features based on the learning-to-rank framework. While arriving at linking decisions individually via the local model, our method also takes into consideration the correlation between decisions by running multiple recurrent global models, which can be deemed as a learned local search method. Our method attains performances comparable to the state-of-the-art deep learning-based methods on NED benchmark datasets while being significantly faster to train.
Information System Ordering Online Restaurant Menu At Hover Cafe

The rapid development of technology requires every business person to upgrade his technology to more sophisticated. Many business people have upgraded their systems to be computerized and even online, one of the business people is in the culinary field. The culinary field which is meant for example is the cafe One thing that can support the quality of ordering in a cafe is the ordering process. In the hover cafe the menu ordering system is still conventional, using a menu book. This of course can be a problem because the menu book used can be damaged or lost so that it can slow down the ordering process, not to mention if the customer requests additional orders, the waiter must come to the customer's table to record additional orders again. The method used in this research is the method of data collection, the method of observation, the method of interviewing, and the method of literature study conducted to obtain accurate data. Then the data are analyzed with SWOT to find out the company's weaknesses and strengths. In describing the flow of the system using UML (Unified Modeling Language) and PHP (Hypertext Preprocessor) as a programming language with a MySQL database as a database. Therefore we need a ordering media that utilizes information technology as a medium for selecting food and beverage (E-Menu) which can provide information about order menu details to facilitate customers in making and ordering food or beverage menus.
On Knowledge Dependence in Weighted Description Logic

We study a family of operators (called ‘Tooth’ operators) that combine Description Logic concepts via weighted sums. These operators are intended to capture the notion of instances satisfy- ing “enough” of the concept descriptions given. We examine two variants of these operators: the “knowledge-independent” one, that evaluates the concepts with respect to the current interpretation, and the “knowledge-dependent” one that instead evaluates them with respect to a specified knowledge base, comparing and contrasting their properties. We furthermore discuss the connections between these operators and linear classification models.