Reviewerly/exHarmony huggingface dataset:
A Case for Guided Machine Learning

Involving humans in the learning process of a machine learning algorithm can have many advantages ranging from establishing trust into a particular model to added personalization capabilities to reducing labeling efforts. While these approaches are commonly summarized under the term interactive machine learning (iML), no unambiguous definition of iML exists to clearly define this area of research. In this position paper, we discuss the shortcomings of current definitions of iML and propose and define the term guided machine learning (gML) as an alternative.
Additive Voronoi Cursor: Dynamic Effective Areas Using Additively Weighted Voronoi Diagrams

We present Additive Voronoi Cursor (AVC) – a new cursor technique for target selection by dynamically resizing the area cursor based on the analysis of the two different phases of mouse movement: the ballistic and the correction phases during target selection. On-screen Targets can be divided into respective areas dynamically based on both target distribution and cursor velocity. We assumed that to select a target, a user will first perform ballistic/fast cursor movement aiming to the target roughly, then correct the cursor position with slower movement towards the desired target. Therefore, after the ballistic movement, the desired target would locate within the local region closed to the cursor. We defined Additive Weighted Voronoi Diagrams with selectable targets by assigning larger weights to the nearby objects right after the ballistic cursor movement. Therefore, the effective areas of the nearby objects are enlarged, and they can be selected more easily and quickly. We had compared our cursor technique with recent developed area-cursor methods. The results showed that our method performed significantly better on certain configurations.
E-Sharing: Web Based Online Donation System

Nowadays there are so many people who are surviving on only one meal per day. Especially in developing countries like India, it is one of the major problems. On the other hand, there is so much wastage of food every day. Some poor people need clothes and vessels, and children need some books and study kits. Solution to this is that we only need to donate the leftover food to needy people, charities, and our old stuff. For that to happen, we need some sort of platform. This could be any online platform like a website/web application. An India, there are so many people who are capable of making donations and also there are so many NGOs which are helping poor and needy people of India. But to connection gap is not as blur as it should be. There has to be some simple, fast, intuitive and secure way of doing such online donations so that users can donate easily with just a click. Keywords : Smartphone, online donation, password, research, waste management Cite this Article Kanu Patel, Meshwa Patel, Sagar Dudhaiya. E-Sharing: Web Based Online Donation System. Journal of Web Engineering & Technology . 2019; 6(1): 25–36p.
Linguistics Analytics in Data Warehouses Using Fuzzy Techniques

A data warehouse is used intensively in many industry domains to gain competitive advantage over its competitors. In modern data warehouses, linguistic analytics is an important aspect, so that it has the ability to take more precious decisions. In most of the data warehouse implementations, it is designed for crisp analysis. Crisp analysis has its own limitations and boundaries with the major assumptions that every situation belongs to one state and denial to other states. Hence, crisp data warehouse does not allow to carry out linguistic analytics. When a fuzzy data warehouse is implemented, because of the fuzzy nature of the data warehouse, linguistic analytics can be done to a certain extent. In this research, non-functional requirements such as performance and configuration are also covered so that this method can be implemented in the real world.
Metamorphosis Knowledge Probing of Guild Data through Chat Bot Using NLP

Artificial intelligence conversational entities, are called as chat-bots. Chat-bots are computer programs capable to carry out conversation between human and computer. This application can run on local computer, but it has to be accessed through the internet. Chat Bots conversation system is an intelligent human machine interaction using natural language which will be built using voice recognition techniques and artificial intelligence algorithms that will analyze user's queries which was given by the user and understand user's input [13]. This system is an application which will provide answers to the queries of the students. The machine has been installed with learning to distinguish the sentences and settling on a choice itself as a reaction to respond to an inquiry. Natural language processing algorithms are used for Noise Removal, Term frequency, Word Embedding and Cosine similarity of the content of the complaint. The biggest advantage is that student does not have to go to college for the enquiry and anyone can access this chat bot either through voice or text. Students just have to put their query to the bot by voice or text. Questions asked to the bot, which is not understood is further processed using a third-party expert, Admin and the response is archived, improving the artificial brain capabilities for future generation of responses.
Two-Pass Bi-Directional Optical Flow Via Motion Vector Refinement

Bi-directional optical flow (BDOF) is an efficient coding tool that has been recently adopted into Versatile Video Coding (VVC) standard. With BDOF, bi-predictive prediction samples of one coding block are enhanced via higher-precision motion vectors (MVs) derived from its two reference blocks. In this way, the energy of prediction error could be reduced, resulting in better coding performance. In VVC, the derived motion information is only used to enhance prediction samples. In this paper, it is proposed to use the derived motion information to also refine decoded MVs. The refined MVs may be used as spatial motion vector prediction (MVP) for the following coding units (CUs), as the temporal MVP for the subsequent pictures, and in the deblocking filtering process. Furthermore, the refined MVs can be used to perform motion compensation (MC) again to further improve the quality of the prediction samples. Simulation results show that the proposed methods can achieve -1.18% BD-rate saving in average under the random access configuration on top of the existing BDOF design in VVC.
Research and Practice on Reforming Higher Mathematics Teaching in Independent Colleges — Based on the South China Business College of Guangdong University of Foreign Studies

Based on analysis of the factors affecting the teaching and reform of higher mathematics in independent collages, this study proposes basic rationale and measures for reforming the teaching of higher mathematics in independent colleges and clarifies the reform objectives to be achieved.
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging

We present two new algorithms, ASYNCADA and HEDGEHOG, for asynchronous sparse online and stochastic optimization. ASYNCADA is, to our knowledge, the first asynchronous stochastic optimization algorithm with finite-time data-dependent convergence guarantees for generic convex constraints. In addition, ASYNCADA: (a) allows for proximal (i.e., composite-objective) updates and adaptive step-sizes; (b) enjoys any-time convergence guarantees without requiring an exact global clock; and (c) when the data is sufficiently sparse, its convergence rate for (non-)smooth, (non-)strongly-convex, and even a limited class of non-convex objectives matches the corresponding serial rate, implying a theoretical “linear speed-up”. The second algorithm, HEDGEHOG, is an asynchronous parallel version of the Exponentiated Gradient (EG) algorithm for optimization over the probability simplex (a.k.a. Hedge in online learning), and, to our knowledge, the first asynchronous algorithm enjoying linear speed-ups under sparsity with non-SGD-style updates. Unlike previous work, ASYNCADA and HEDGEHOG and their convergence and speed-up analyses are not limited to individual coordinate-wise (i.e., “box-shaped”) constraints or smooth and strongly-convex objectives. Underlying both results is a generic analysis framework that is of independent interest, and further applicable to distributed and delayed feedback optimization
Cascaded Residual Density Network for Crowd Counting

Crowd counting is a challenging task due to the issues such as scale variation and perspective variation in real crowd scenes. In this paper, we propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine approach to generate the high-quality density map for crowd counting more accurately. (1) We estimate the residual density maps by multi-scale pyramidal features through cascaded residual density modules. It can improve the quality of density map layer by layer effectively. (2) A novel additional local count loss is presented to refine the accuracy of crowd counting, which reduces the errors of pixel-wise Euclidean loss by restricting the number of people in the local crowd areas. Experiments on two public benchmark datasets show that the proposed method achieves effective improvement compared with the state-of-the-art methods.
Multi-level Engagement in Augmented Reality Children’s Picture Books

We demonstrate our AR enhanced picture book that provides multiple levels of interaction and engagement. Holding the camera at a range of heights facilitates reader exploration of layered book features and content. These multi-level enhancements extend the traditional learning possibilities of books while providing increased opportunities for both shared and individual reading.
Data Masking for Recommender Systems: Prediction Performance and Rating Hiding

Data science challenges allow companies, and other data holders, to collaborate with the wider research community. In the area of recommender systems, the potential of such challenges to move forward the state of the art is limited due to concerns about releasing user interaction data. This paper investigates the potential of privacy-preserving data publishing for supporting recommender system challenges. We propose a data masking algorithm, Shuffle-NNN, with two steps: Neighborhood selection and value swapping. Neighborhood selection preserves valuable item similarity information. The data shuffling technique hides (i.e., changes) ratings of users for individual items. Our experimental results demonstrate that the relative performance of algorithms, which is the key property that a data science challenge must measure, is comparable between the original data and the data masked with Shuffle-NNN.
Towards Empathic Deep Q-Learning

As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics. As a contribution to these fields, this paper introduces an extension to Deep Q-Networks (DQNs), called Empathic DQN, that is loosely inspired both by empathy and the golden rule (Do unto others as you would have them do unto you). Empathic DQN aims to help mitigate negative side effects to other agents resulting from myopic goal-directed behavior. We assume a setting where a learning agent coexists with other independent agents (who receive unknown rewards), where some types of reward (e.g. negative rewards from physical harm) may generalize across agents. Empathic DQN combines the typical (self-centered) value with the estimated value of other agents, by imagining (by its own standards) the value of it being in the other's situation (by considering constructed states where both agents are swapped). Proof-of-concept results in two gridworld environments highlight the approach's potential to decrease collateral harms. While extending Empathic DQN to complex environments is non-trivial, we believe that this first step highlights the potential of bridge-work between machine ethics and RL to contribute useful priors for norm-abiding RL agents.
Visualization Analysis of Research of English for Specific Purposes in China Based on CiteSpace (2000-2017)

Based on the data from CNKI database, this study used CiteSpace to analyze the research hotspots and development trends of studies on English for Specific Purposes in China from 2000 to 2017. The results show that research hotspots mainly include college English ESP teaching reform, ESP teaching methods and models, needs analysis and ESP, ESP curriculum provision and curriculum design. The domestic studies on ESP experienced slow development in controversy, rapid development in practice and deep development with modern educational technology. Domestic ESP research has yielded fruitful results, but in the future, research on ESP evaluation system, ESP teachers, ESP corpora development, and ESP textbook construction should be deeply explored.
Spam Detection using Sentiment Analysis of Text

Nowadays, a big part of people rely on available content in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks.I.
Joint-transform Correlator Multiple-image Encryption System Based on Quick-response Code Key

A method for joint-transform correlator (JTC) multiple-image encryption based on a quick-response (QR) code key is proposed. The QR codes converted from different texts are used as key masks to encrypt and decrypt multiple images. Not only can Chinese text and English text be used as key text, but also symbols can be used. With this method, users have no need to transmit the whole key mask; they only need to transmit the text that is used to generate the key. The correlation coefficient is introduced to evaluate the decryption performance of our proposed cryptosystem, and we explore the sensitivity of the key mask and the capability for multiple-image encryption. Robustness analysis is also conducted in this paper. Computer simulations and experimental results verify the correctness of this method.
Estimating the Students’ Skill in Reciting and Writing Alqur’an at Faculty of Tarbiyah and Teacher Training IAIN Sultan Amai Gorontalo

&lt;p class="ABSTRACTJUDUL"&gt;The purpose of this study was to estimate the instrument of reading and writing capability of the Qur’an in the practicum program students of the Faculty of Tarbiyah and Teacher Training IAIN Sultan Amai Gorontalo. This type of research is quantitative with an analysis approach to the generalization theory variance through the G-Study concept with a multifacet design p x r x I three facet variations. The results of the study prove the instrument of reading and reading ability of the Koran was tested, namely the estimated coefficient of reliability of the combined scores of the reading and writing assessment of the Koran from 20 students who were rated by 4 rater of 10 items which were judged by the magnitude of generalizability coefficient value of 0.82749 . The magnitude of this value indicates that the true score of the assessment results is quite high compared to the minimum reliability criteria, which is 0.70 fulfilling the reliable criteria.&lt;/p&gt;&lt;p class="ABSTRACTJUDUL"&gt; &lt;/p&gt;&lt;p class="ABSTRACTJUDUL"&gt;&lt;strong&gt;Abstrak&lt;/strong&gt;&lt;/p&gt;&lt;p class="ABSTRACT"&gt;Tujuan penelitian ini untuk mengestimasi instrument kemampuan baca tulis alqur’an pada program praktikum baca tulis Alqur’an mahasiswa Fakultas Ilmu Tarbiyah dan Keguruan IAIN Sultan Amai Gorontalo. Jenis penelitian ini menggunakan kuantitatif dengan pendekatan analisis varians teori generalisibilitas melalui konsep G-Study dengan desain multifacet p x r x I tiga variasi facet. Hasil penelitian membuktikan instrumen penilaian kemampuan baca tulis al-Qur’an teruji, yakni estimasi koefisien reliabilitas skor gabungan penilaian kemampuan baca tulis Alqur’an dari 20 mahasiswa yang dinilai oleh 4 rater terhadap 10 item yang dinilai diperoleh besarnya nilai koefisien generalizabilitas sebesar 0,82749. Besaran nilai tersebut menunjukkan bahwa true score hasil penilaian cukup tinggi dibandingkan kriteria reliabilitas minimal yaitu 0,70 memenuhi kriteria reliabel.&lt;/p&gt;&lt;strong&gt;&lt;span lang="EN-US"&gt;&lt;br /&gt;&lt;/span&gt;&lt;/strong&gt;
METHOD-MIX FOR THE DEVELOPMENT OF INTERCULTURAL USER INTERFACES EXEMPLIFIED

In this paper, a method-mix for intercultural user interface design (IUID) is explained and exemplified by an application example to elucidate why and how cultural aspects play a role in UI design and usability/UX engineering.The readers are sensitized to the challenges of intercultural usability/UX engineering and intercultural UI design and will be equipped with methodological knowledge relevant to the derivation of recommendations for user interface design in their desired cultural contexts.
Terminology-Aware Segmentation and Domain Feature for the WMT19 Biomedical Translation Task

In this work, we give a description of the TALP-UPC systems submitted for the WMT19 Biomedical Translation Task.Our proposed strategy is NMT model-independent and relies only on one ingredient, a biomedical terminology list.We first extracted such a terminology list by labelling biomedical words in our training dataset using the BabelNet API.Then, we designed a data preparation strategy to insert the terms information at a token level.Finally, we trained the Transformer model (Vaswani et al., 2017) with this termsinformed data.Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.
Learning to Screen

Imagine a large firm with multiple departments that plans a large recruitment. Candidates arrive one-by-one, and for each candidate the firm decides, based on her data (CV, skills, experience, etc), whether to summon her for an interview. The firm wants to recruit the best candidates while minimizing the number of interviews. We model such scenarios as an assignment problem between items (candidates) and categories (departments): the items arrive one-by-one in an online manner, and upon processing each item the algorithm decides, based on its value and the categories it can be matched with, whether to retain or discard it (this decision is irrevocable). The goal is to retain as few items as possible while guaranteeing that the set of retained items contains an optimal matching. We consider two variants of this problem: (i) in the first variant it is assumed that the $n$ items are drawn independently from an unknown distribution $D$. (ii) In the second variant it is assumed that before the process starts, the algorithm has an access to a training set of $n$ items drawn independently from the same unknown distribution (e.g. data of candidates from previous recruitment seasons). We give tight bounds on the minimum possible number of retained items in each of these variants. These results demonstrate that one can retain exponentially less items in the second variant (with the training set). Our algorithms and analysis utilize ideas and techniques from statistical learning theory and from discrete algorithms.
Which Neural Network Architecture matches Human Behavior in Artificial Grammar Learning?

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feed-forward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can 'learn' (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, our results suggest that explicit learning is best modeled by recurrent architectures, whereas feedforward networks better capture the dynamics involved in implicit learning.
Movie Recommendation to Friends Using Whale Optimization Algorithm

Background:: Recent advances in the World Wide Web and semantic networks have amplified social networking platforms, where the users share their photos, hobbies, location, interests, and experiences such as movie or restaurants. Social media platforms such as Facebook, twitter and LinkedIn are used to recommend the users the things of their interests such as movie, food, locations and friends. Objective:: A novel method for the recommendation of movies to a friend using whale optimization has been introduced. Ratings given by friends of various movies are employed to recommend movies. Method:: Different evolutionary-based optimization methods have been applied for movie recommendation. The proposed method has been tested on movie-lense dataset and results are compared with 5 other methods namely, K-means, PCA K- means, SOM, PCA-SOM, PSO and ABC in terms of mean absolute error, precision and recall. Result:: The experimental results demonstrate that proposed method outperformed all considered methods for 88.5% clusters centers in terms of precision, recall and mean absolute error. Conclusion:: A novel recommendation system based on users rating has been designed to recommend movies to friends. It leverages the strengths of whale optimization to find the optimal solution.
The Role of Mobile Phones As Effective Tools For Language Learning By Libyan EFL Learners

The current technological era has witnessed a great advancement in the world of mobile technology. Mobiles gadgets have now turned out to be an integral element of the daily life. Given the fact that mobile devices can be used to serve a number of functions, EFL learners can utilize them regularly to enhance their process of learning and attain good learning outcome. This article intends to explore the effectiveness of using mobile phones in language learning by Libyan EFL learners. To further investigate this issue, questionnaires were administered to 151 Libyan EFL undergraduate students from the English department of Sebha University and focus group discussions (FGDs) were carried out with 18 students. The results revealed that using mobile phones in learning was effective to a great extent. Besides, the majority of Libyan EFL learners utilized their mobile phones mostly to check meaning of words, to listen to English songs and to develop reading, listening, speaking and writing skills. The findings also demonstrated that Libyan EFL learners exhibited positive perceptions towards using mobile phones in learning English.