Reviewerly/exHarmony huggingface dataset:
Bayesian Optimization with Output-Weighted Importance Sampling.

In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy estimations. We approach the problem from the perspective of importance-sampling theory, and advocate the use of the likelihood ratio to guide the search algorithm towards regions of the input space where the objective function assumes abnormally large or small values. The likelihood ratio acts as a sampling weight and can be computed at each iteration without severely deteriorating the overall efficiency of the algorithm. In particular, it can be approximated in a way that makes the approach tractable in high dimensions. The likelihood-weighted acquisition functions introduced in this work are found to outperform their unweighted counterparts in a number of applications.
Ship Detection Based on Faster R-CNN in SAR Imagery by Anchor Box Optimization

Object detection in synthetic aperture radar (SAR) imagery is a fundamental and challenging problem in the field of SAR imagery analysis for many fields like military, intelligence, commercial applications, etc. Object detection based on faster-regions convolutional neural network (Faster R-CNN) has lesser running time as compared to a convolutional neural network (CNN) in the detection process. Nowadays, anchor boxes are widely used in the detection model. This paper aims to provide an anchor box optimization method to improve ship detection accuracy in SAR imagery. By using Residual Network (ResNet-50) as a backbone in Faster R-CNN and it's compatible anchor sets better mean Average Precision (mAP) achieved. We compared the mAP with the two sets of anchor parameters, we found that in the ship detection process mAP achieves more than 4.29 % significant improvement.
Solving Arithmetic Word Problems with a Templatebased Multi-Task Deep Neural Network (T-MTDNN)

Solving arithmetic word problem automatically has been a challenge both in terms of attaining robustness to unseen problems and achieving high problem-solving accuracy. In this paper, we propose a Template based - Multi-Task Deep Neural Network (T-MTDNN) framework, which utilizes two types of techniques. First, by generating normalized equation templates, we achieve robustness by enabling a more general language representation of a given linguistic task. Second, by applying MTDNN [1], which uses BERT with number and operator classification as multi-tasks, we gain higher problem solving accuracy compared to T-RNN [2], which is the state-of-the-art model. Specifically, with MAWPS dataset, the accuracy of T-MTDNN is 78.88% compared to the accuracy of T-RNN at 66.8%. With Math23K dataset, the accuracy of T-MTDNN is 72.6% compared to the accuracy of T-RNN at 66.9%.
The Semiotics of Emoji and The Emoji Revolution: a comparative review

Emoji are popular digital pictograms that appear in texts, messages, emails, and on social media platforms. They are generally regarded as a light-hearted and comedic form of communication. With tw...
Effectiveness of preprocessing techniques over social media texts for the improvement of machine learning based classifiers

The language present in the context of social networks is usually more informal than the one used in traditional sources. The researches that take such content as input for machine learning based classifying algorithms, perform, as a first step, a cleaning and standardization process. The goal of the latter is to improve the accuracy of the classification. In this paper, several cleaning tasks are defined and executed over a dataset of comments extracted from the social network Facebook. The goal is to verify if the corrections, made by such tasks, produce a significant improvement in the accuracy reached by the classifying algorithms. The results obtained, indicate that, over this type of dataset, preprocessing tasks with a reasonably good performance in the correction of errors, do not necessarily produce a noteworthy improvement in the classification accuracy reached by the algorithms.
Secure Banking Transactions Using RSA and Two Fish Algorithms

A bank is the only secure medium for money transfer in the whole world. Internet Banking has become a trend nowadays which eases the process of transfer of money from anywhere in the world to anyone in the world. With modern technology, one can transfer as much amount as they want to anyone with the required level of authentication. Since even this technology is made by humans some errors are being taken advantage of by intruders often referred to as Man in the Middle. To make the transactions more secure we propose to use cryptographic techniques. Cryptographic techniques include encrypting a text and only the authenticated user can decrypt it. We hereby propose a combination of two such algorithms that are RSA and Two fish algorithms. RSA is an asymmetric cryptographic algorithm which means that there are a pair of keys that are public and private keys. Two Fish Algorithm uses a block cipher in which it uses a key length of256 bits and is said to be efficient even when it is used in companies having a small processor. It allows implementers to trade off encryption speed, key setup time and code sized to balance performance.
Curriculum based dropout discriminator for domain adaptation

Domain adaptation is essential to enable wide usage of deep learning based networks trained using large labeled datasets. Adversarial learning based techniques have shown their utility towards solving this problem using a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate, it would be useful if a distribution based discriminator could be used to bridge this gap. This could be achieved using multiple classifiers or using traditional ensemble methods. In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator could suffice to obtain the distribution based discriminator. Specifically, we propose a curriculum based dropout discriminator that gradually increases the variance of the sample based distribution and the corresponding reverse gradients are used to align the source and target feature representations. The detailed results and thorough ablation analysis show that our model outperforms state-of-the-art results.
Interpretable Complex Question Answering

We will review cross-community co-evolution of question answering (QA) with the advent of large-scale knowledge graphs (KGs), continuous representations of text and graphs, and deep sequence analysis. Early QA systems were information retrieval (IR) systems enhanced to extract named entity spans from high-scoring passages. Starting with WordNet, a series of structured curations of language and world knowledge, called KGs, enabled further improvements. Corpus is unstructured and messy to exploit for QA. If a question can be answered using the KG alone, it is attractive to ‘interpret’ the free-form question into a structured query, which is then executed on the structured KG. This process is called KGQA. Answers can be high-quality and explainable if the KG has an answer, but manual curation results in low coverage. KGs were soon found useful to harness corpus information. Named entity mention spans could be tagged with fine-grained types (e.g., scientist), or even specific entities (e.g., Einstein). The QA system can learn to decompose a query into functional parts, e.g., “which scientist” and “played the violin”. With increasing success of such systems, ambition grew to address multi-hop or multi-clause queries, e.g., “the father of the director of La La Land teaches at which university?” or “who directed an award-winning movie and is the son of a Princeton University professor?” Questions limited to simple path traversals in KGs have been encoded to a vector representation, which a decoder then uses to guide the KG traversal. Recently the corpus counterpart of such strategies has also been proposed. However, for general multi-clause queries that do not necessarily translate to paths, and seek to bind multiple variables to satisfy multiple clauses, or involve logic, comparison, aggregation and other arithmetic, neural programmer-interpreter systems have seen some success. Our key focus will be on identifying situations where manual introduction of structural bias is essential for accuracy, as against cases where sufficient data can get around distant or no supervision.
Financial C.R.I.M.Es in small businesses: causes and consequences

Purpose The purpose of this paper is to examine the causes and consequences of financial crimes perpetrated against New Zealand small businesses. Design/methodology/approach A random sample of 200 court cases was selected from 2010 to 2017. A total of 12 cases involving 14 small businesses were analysed. Findings The results reveal that financial crime is a systemic problem and involves people with diverse demographics, and the victims are not restricted to any specific type of small business. The offenders are mostly middle-level managers. The length of offence varied from 1 year to 12.5 years. Most of them funnelled the stolen money into their personal accounts. The common motive is “keeping up appearance”. The management placed immense trust in their employees and did not vet candidates before employment. The losses suffered by small entities ranged from $6,000 to $590,000 and liquidated one business. The severity of the actual court cases indicates the necessity of an employee screening as the first line of defence in these businesses. Research limitations/implications The small sample of court cases is a limitation, but the study contributes to the fraud auditing literature by examining actual court cases involving small businesses. Small businesses as victims of employee fraud and their lack of internal controls are known but under-researched to promote thought about fraud risk severity in these businesses. Originality/value The C.R.I.M.E model has yet been tested on fraud cases involving small businesses.
Synergistic interaction of education, science, and industry

Abstract Economic and social development of a country rely on advanced science and technology, skilled, educated workforce, extensive growth of innovation and population density. The process of innovation creation can be long and winding and should take into account the needs of technological development and definitely bring benefits to business. The concept of a science park has not been new. The United States of America were pioneers who in early 1950s suggested a brand new model for bridging the gap between academia and industry. Nowadays developed countries employ that model, though its objectives, funds, inputs and outcomes may vary. This paper will focus of the issue of real interaction of institutions of education, science and industry and will raise a number of controversial issues in the correction of the vector for socio-economic modernization. The authors analyzed a set of legislation acts on regulating the cooperation of academia and business. The paper gives a graphical model of reciprocity of institutions of education, science, and industry taking into account the interests of public authorities, and offers a set of additional indicators of evaluation of the efficiency of functioning of an innovation structure in the country (region).
Challenges of Migrating Legacies Web to Mobile: A Systematic Literature Review

The multi-device era comes thanks to mobile computing which gives remote access to resources from anywhere changing the people's life and opening new business opportunities.However, the legacy systems do not render the content properly in mobile devices because they were thought to be only compliant with Web browsers.Economic availability is often the reason why these have not been modernized.This work proposes a systematic literature review about the approaches used for the portabilization or modernization of web 1.0 business applications to mobile devices in the period 2006-2017, from SCOPUS, IEEE, and ACM.The search obtained 824 articles, where 37 were selected based on the inclusion criteria.The research questions were: PI1.What have been the contribution to the portability of business applications to mobile devices?PI2.What methods have been used?PI3.What is the nature of the contribution found?PI4.What are the objectives that are pursued in the research work?.The conclusions were obtained through the classification of selected papers with respect to focus, scope, type of research and type of contribution to give an answer to research questions.Then, the state of the art determines the main approaches such as model-driven development, content reorganization, augmentation, and specific migrations.However, certain research gaps were detected and detailed which represent exploration opportunities.
When does data augmentation help generalization in NLP

Neural models often exploit superficial (weak) features to achieve good performance, rather than deriving the more general (strong) features that we'd prefer a model to use. Overcoming this tendency is a central challenge in areas such as representation learning and ML fairness. Recent work has proposed using data augmentation--that is, generating training examples on which these weak features fail--as a means of encouraging models to prefer the stronger features. We design a series of toy learning problems to investigate the conditions under which such data augmentation is helpful. We show that augmenting with training examples on which the weak feature fails (counterexamples) does succeed in preventing the model from relying on the weak feature, but often does not succeed in encouraging the model to use the stronger feature in general. We also find in many cases that the number of counterexamples needed to reach a given error rate is independent of the amount of training data, and that this type of data augmentation becomes less effective as the target strong feature becomes harder to learn.
Unsupervised and Active Learning Using Maximin-Based Anomaly Detection

Unsupervised anomaly detection is commonly performed using a distance or density based technique, such as K-Nearest neighbours, Local Outlier Factor or One-class Support Vector Machines. One-class Support Vector Machines reduce the computational cost of testing new data by providing sparse solutions. However, all these techniques have relatively high computational requirements for training. Moreover, identifying anomalies based solely on density or distance is not sufficient when both point (isolated) and cluster anomalies exist in an unlabelled training set. Finally, these unsupervised anomaly detection techniques are not readily adapted for active learning, where the training algorithm should identify examples for which labelling would make a significant impact on the accuracy of the learned model. In this paper, we propose a novel technique called Maximin-based Anomaly Detection that addresses these challenges by selecting a representative subset of data in combination with a kernel-based model construction. We show that the proposed technique (a) provides a statistically significant improvement in the accuracy as well as the computation time required for training and testing compared to several benchmark unsupervised anomaly detection techniques, and (b) effectively uses active learning with a limited budget.
Semi-supervised Feature-Level Attribute Manipulation for Fashion Image Retrieval

With a growing demand for search by image, many works have studied task of fashion instance-level image retrieval (FIR). Furthermore, recent works introduce a concept of fashion attribute manipulation (FAM) which manipulates a specific attribute (e.g color) of a fashion item while maintaining rest of attributes (e.g shape, and pattern). In this way, users can search not only the same items but also similar items with desired attributes. FAM is a challenging task in that attributes are hard to define, and unique characteristics of a query are hard to be preserved. Although both FIR and FAM are important in real-life applications, most of previous studies have focused on only one of these problem. In this study, we aim to achieve competitive performance on both FIR and FAM. To do so, we propose a novel method that converts a query into a representation with desired attributes. We introduce a new idea of attribute manipulation at feature level, by matching distribution of manipulated features with real features. In this fashion, attribute manipulation can be done independently from learning a representation from image. By introducing feature-level attribute manipulation, previous methods for FIR can perform attribute manipulation without sacrificing their retrieval performance.
Mobile-Assisted Task-Based Language Learning, Writing Competency, And Motivation

This quasi-experimental study aimed at investigating the effect of mobile-assisted task-based learning on students’ writing competency and students’ motivation in writing. Post-test only control group design was employed in this study. The experimental group was taught by mobile-assisted task-based learning while the control group was taught by non mobile-assisted task-based learning. A writing post-test and questionnaire were used to obtain the data and were analyzed using one-way ANOVA and MANOVA. The findings of the study revealed that (1) there was a significant effect of Mobile-assisted Task-based Learning on the students’ writing competency; (2) there was a significant effect of Mobile-assisted Task-based Learning on the students’ writing motivation; and (3) there was a simultaneous effect of Mobile-assisted Task-based Learning on the students’ writing competency and students’ motivation in writing. This study concluded that the use of mobile devices as a medium in the learning process is effective to enhance the students’ writing competency and increase students’ motivation in writing.
Research on image processing algorithm of immune colloidal gold test paper detection

In order to better solve the problem of automatic identification of quality control line and detection line in the detection of gold standard test strip, this paper proposes to collect the image information of gold standard test strip after color rendering through CMOS sensor, preprocess the obtained information, transform RGB image into gray image, build cloud model in the CIELAB/HSV/HSL space, and apply the improved AdaBoost algorithm to determine the position of detection line and quality control line Place. Compared with the traditional template matching method, it improves the accuracy and accuracy of recognition.
Group based deep shared feature learning for fine-grained image classification

Fine-grained image classification has emerged as a significant challenge because objects in such images have small inter-class visual differences but with large variations in pose, lighting, and viewpoints, etc. Most existing work focuses on highly customized feature extraction via deep network architectures which have been shown to deliver state of the art performance. Given that images from distinct classes in fine-grained classification share significant features of interest, we present a new deep network architecture that explicitly models shared features and removes their effect to achieve enhanced classification results. Our modeling of shared features is based on a new group based learning wherein existing classes are divided into groups and multiple shared feature patterns are discovered (learned). We call this framework Group based deep Shared Feature Learning (GSFL) and the resulting learned network as GSFL-Net. Specifically, the proposed GSFL-Net develops a specially designed autoencoder which is constrained by a newly proposed Feature Expression Loss to decompose a set of features into their constituent shared and discriminative components. During inference, only the discriminative feature component is used to accomplish the classification task. A key benefit of our specialized autoencoder is that it is versatile and can be combined with state-of-the-art fine-grained feature extraction models and trained together with them to improve their performance directly. Experiments on benchmark datasets show that GSFL-Net can enhance classification accuracy over the state of the art with a more interpretable architecture.
Application of Convolutional Neural Networks for Multimodal Identification Task

Currently, biometric identification systems are often used in mobile applications, banking systems, access control and management systems as well as for the management of mobile robots. In this paper, we consider the problem of personality recognition using facial images and audio signals with speech recordings. The results of the research will be used to create a system of multimodal biometric identification. Since convolutional neural networks demonstrate the highest results regarding the problems of detection, segmentation and classification of objects, this paper also proposes an approach to person identification based on convolutional neural networks. The research was carried out using modern audiovisual database VoxCeleb1. To decrease the computational capability of the experiment, the researchers reduced the number of classes from 1,251 to 200. The development results showed the possibility of using the proposed algorithm as a part of a multimodal identity identification system.
VIRTUAL REALITY AND ITS IMPACT ON LEARNING SUCCESS

For elementary schools, the question arises as to whether the use of Virtual Reality (VR) can lead to better learning success for pupils.A field experiment was chosen as the methodology for investigating this question.A total of 82 pupils from four first and third secondary school classes were available for this field experiment.They were randomly assigned to a group per class, taking gender into account.Both groups attended a pre-test one week before the respective course unit in order to identify any previous knowledge.One week after the course unit, both groups again completed the post-test.The tests were designed in such a way that equal weight was attached to each learning objective of the learning units.During the evaluation, the individual learning success, i.e. the difference between the post-test and the pre-test, was calculated for each pupil.No significant differences in learning success between the traditional and the VR based units could be found.However, the VR learning unit showed a positive effect on the learning success and was widely accepted by the pupils.From an methodological point of view it is very difficult to design equivalent learning units and to compare them fairly.
Tracking Multiple Collocated HTC Vive Setups in a Common Coordinate System

Multiple collocated HTC Vive setups sharing the same base stations and room calibration files still track their devices in different coordinate systems. We present a procedure for mapping the tracking data of multiple users to a common coordinate system and show that it enables spatially consistent interactions of collocated collaborators.