Reviewerly/exHarmony huggingface dataset:
Indian Counterfeit Banknote Detection Using Support Vector Machine

Currency counterfeiting is a serious crime that affects a country's finances. The proposed system will be useful to detect counterfeit banknotes in banking systems. India is facing more serious problems due to the increase in fake notes in the market. To get rid of this problem, various fake note detection methods are available worldwide, but most of them are hardware based and costly. The proposed system focuses on having access to the public to identify counterfeit banknotes. The proposed system can identify the legitimacy of a banknote by checking for specific security features such as watermarks, latent images, security threads, etc. Identification of counterfeit banknotes is done using machine learning techniques. The methodology involves extracting and encoding these security features. Security features are extracted from the input image, feature detection and classification are performed using a support vector machine (SVM). Experimental results shown in the paper shows promising results.
Verifiable and Privacy-Preserving Outsourcing of Matrix Multiplications

Matrix operation is a type of basic operations in many application scenarios. Especially, in the server-assisted applications, the user may need to delegate remote server to perform matrix operations to accomplish the target task. This paper studies the privacy, verifiability and efficiency issues in outsourcing matrix multiplications to an untrusted server. A lightweight outsourcing scheme is constructed, where the user is allowed to request matrix multiplications with privacy protection from the server and verify the correctness of the result. Performance analysis and comparison indicate that our scheme is more efficient than existing solution in each phase, which confirms the practicality of our proposal in supporting applications by resource-limited devices.
Categorical Vector Space Semantics for Lambek Calculus with a Relevant Modality (Extended Abstract)

We develop a categorical compositional distributional semantics for Lambek Calculus with a Relevant Modality, !L * , which has a limited version of the contraction and permutation rules.The categorical part of the semantics is a monoidal biclosed category with a coalgebra modality as defined on Differential Categories.We instantiate this category to finite dimensional vector spaces and linear maps via "quantisation" functors and work with three concrete interpretations of the coalgebra modality.We apply the model to construct categorical and concrete semantic interpretations for the motivating example of !L * : the derivation of a phrase with a parasitic gap.The effectiveness of the concrete interpretations are evaluated via a disambiguation task, on an extension of a sentence disambiguation dataset to parasitic gap phrases, using BERT, Word2Vec, and FastText vectors and Relational tensors.
Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning

Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker. Previous works have made progress on voice conversion with parallel training data and pre-known speakers. However, zero-shot voice style transfer, which learns from non-parallel data and generates voices for previously unseen speakers, remains a challenging problem. In this paper we propose a novel zero-shot voice transfer method via disentangled representation learning. The proposed method first encodes speaker-related style and voice content of each input voice into separate low-dimensional embedding spaces, and then transfers to a new voice by combining the source content embedding and target style embedding through a decoder. With information-theoretic guidance, the style and content embedding spaces are representative and (ideally) independent of each other. On real-world datasets, our method outperforms other baselines and obtains state-of-the-art results in terms of transfer accuracy and voice naturalness.
Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias

Machine Learning typically assumes that training and test sets are independently drawn from the same distribution, but this assumption is often violated in practice which creates a bias. Many attempts to identify and mitigate this bias have been proposed, but they usually rely on ground-truth information. But what if the researcher is not even aware of the bias? In contrast to prior work, this paper introduces a new method, Imitate, to identify and mitigate Selection Bias in the case that we may not know if (and where) a bias is present, and hence no ground-truth information is available. Imitate investigates the dataset's probability density, then adds generated points in order to smooth out the density and have it resemble a Gaussian, the most common density occurring in real-world applications. If the artificial points focus on certain areas and are not widespread, this could indicate a Selection Bias where these areas are underrepresented in the sample. We demonstrate the effectiveness of the proposed method in both, synthetic and real-world datasets. We also point out limitations and future research directions.
Using machine-learning modeling to understand macroscopic dynamics in a system of coupled maps

Machine-learning techniques not only offer efficient tools for modeling dynamical systems from data but can also be employed as frontline investigative instruments for the underlying physics. Nontrivial information about the original dynamics, which would otherwise require sophisticated ad hoc techniques, can be obtained by a careful usage of such methods. To illustrate this point, we consider as a case study the macroscopic motion emerging from a system of globally coupled maps. We build a coarse-grained Markov process for the macroscopic dynamics both with a machine-learning approach and with a direct numerical computation of the transition probability of the coarse-grained process, and we compare the outcomes of the two analyses. Our purpose is twofold: on the one hand, we want to test the ability of the stochastic machine-learning approach to describe nontrivial evolution laws as the one considered in our study. On the other hand, we aim to gain some insight into the physics of the macroscopic dynamics. By modulating the information available to the network, we are able to infer important information about the effective dimension of the attractor, the persistence of memory effects, and the multiscale structure of the dynamics.
What Does CNN Shift Invariance Look Like? A Visualization Study

Feature extraction with convolutional neural networks (CNNs) is a popular method to represent images for machine learning tasks. These representations seek to capture global image content, and ideally should be independent of geometric transformations. We focus on measuring and visualizing the shift invariance of extracted features from popular off-the-shelf CNN models. We present the results of three experiments comparing representations of millions of images with exhaustively shifted objects, examining both local invariance (within a few pixels) and global invariance (across the image frame). We conclude that features extracted from popular networks are not globally invariant, and that biases and artifacts exist within this variance. Additionally, we determine that anti-aliased models significantly improve local invariance but do not impact global invariance. Finally, we provide a code repository for experiment reproduction, as well as a website to interact with our results at https://jakehlee.github.io/visualize-invariance.
Injecting Prior Knowledge into Image Caption Generation

Automatically generating natural language descriptions from an image is a challenging problem in artificial intelligence that requires a good understanding of the visual and textual signals and the correlations between them. The state-of-the-art methods in image captioning struggles to approach human level performance, especially when data is limited. In this paper, we propose to improve the performance of the state-of-the-art image captioning models by incorporating two sources of prior knowledge: (i) a conditional latent topic attention, that uses a set of latent variables (topics) as an anchor to generate highly probable words and, (ii) a regularization technique that exploits the inductive biases in syntactic and semantic structure of captions and improves the generalization of image captioning models. Our experiments validate that our method produces more human interpretable captions and also leads to significant improvements on the MSCOCO dataset in both the full and low data regimes.
Classical Arabic Poetry: Classification based on Era

This paper proposes a CNN-based deep learning model that classifies Arabic poems based on its era, which is not reported before. To build this model, constructing a dataset is the first step, so we propose an updated Arabic Poetry Dataset (2020). We use FastText word embeddings, based on the full corpus of poems (unlabeled). Two classifiers were trained, namely, a supervised deep learning classifier and a FastText-based classifier. We conducted several experiments. First, we implemented a polarity classifier of poems to modern and non-modern eras, which achieved highest accuracy and F1-score of 0.913 and 0.914, respectively, using a deep learning model without frequent terms. In the second experiment, we categorized poems into three eras. The classifier reported an accuracy and F1-score of 0.875 each. Last, the classification of poems into five different eras achieved highest accuracy and F1-score of 0.801 and 0.796, respectively.
EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets

This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.
Testing a calibration-free eye tracker prototype at the Kunsthistorisches Museum in Vienna

Eye tracking research in art viewership is often conducted in a laboratory setting where reproductions must be used in place of original art works and the viewing environment is less natural than in a museum. Recent technological developments have made museum studies possible but head-mounted eye tracking gear and interruptions by researchers still influence the experience of the viewer. In order to find a more ecologically valid way of recording eye movements while viewing artworks, we employed a prototype of a calibrationfree remote eye tracker hidden below selected paintings at the Kunsthistorisches Museum in Vienna. Museum visitors were unaware of the study and informed post hoc that we had registered their viewing behavior and asked to give consent for the use of their data. This article presents the study design as well as results from over 800 participants. While the data quality from the eye tracker prototype was not sufficient to conduct the intended analysis on within-painting gaze movements, this study might serve as a step towards an unobtrusive examination of the art viewing experience. It was possible to analyze time spent viewing paintings and those results show that certain paintings consistently drew significantly more prolonged attention from viewers.
Eye Tracker Evaluation on Google Classroom Using USE Questionnaire

Abstract Eye tracking is an activity of measuring focus or movement of eyesight in viewing web pages. Usability test methods using eye tracker can maximizing usability evaluations. Based on a discussion with eye tracker users that the usage of tools can make discomfort. We need an analysis of the use of eye trackers whether it affects the usability results. Especially the price of the tool is quite expensive. 16 respondents divided by 2 groups, doing the task with the eye tracker and without eye tracker. Then calculate the time for each task, and spread USE questionnaires. The average time of without eye tracker respondents in task 1 = 165 seconds, task 2 = 109.375 seconds and 3 errors occurred in task 2. The average time of respondents using eye tracker on task 1 = 164.75 seconds, task 2 = 83.442 seconds and 1 error occurred in task 2.There is no difference in the data of duration task completion time. There is a difference in the USE questionnaire satisfaction data between those who use eye trackers and without eye trackers for all elements. The level of website user satisfaction witheye tracker is lower than users without eye tracker.
Experimental study on measurement of free-form surface with wavefront reconstruction algorithm

In this paper, a triangulation interpolation wavefront reconstruction algorithm is proposed to measure the full aperture map of the free-form surface. Based on the method we proposed, the free-form surface can be reconstructed with the scanning white light interferometry (SWLI). Applying the triangulation interpolation wavefront reconstruction algorithm, we provide an experimental demonstration by testing a 318 mm * 288 mm free-form surface in a rectangular shape. By comparing reconstruction results with computer-generated hologram (CGH) compensation testing result, it proves that the surface map can be got with high precision by using the wavefront reconstruction algorithm. In addition, we also test the free-form surface with a probe in contact measurement mode. It shows that with the white light scanning interferometry method, the surface map can be reconstructed with greater efficiency.
Study on Automatic Operation of Manual Wheelchair Prototype and Basic Experiments

In nursing homes, repeatedly guiding several carereceivers in wheelchairs before and after meals is one of the factors that increase the burden on caregivers. A solution to this problem is to incorporate autonomous mobility functions into the wheelchair. Although many autonomous electric wheelchairs have been developed in the past, it is not reasonable to introduce them to all users of nursing homes from the standpoint of cost, charging, and maintenance. In this study, we are developing a detachable robot that can operate a manual wheelchair autonomously. The basic concept, target specifications, and design conditions are defined herein, and the results of basic experiments such as straight-line stability tests, obstacle sensor measurement tests, and self-position estimation are reported. The implementation of autonomous driving functions such as path generation and localization will be promoted in the future.
Correction to: Attention-based multimodal contextual fusion for sentiment and emotion classification using bidirectional LSTM

A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10591-y
Globally Optimizing Small Codes in Real Projective Spaces

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 19 December 2019Accepted: 29 October 2020Published online: 18 February 2021Keywordspacking, sum-of-squares, optimizationAMS Subject Headings05B40Publication DataISSN (print): 0895-4801ISSN (online): 1095-7146Publisher: Society for Industrial and Applied MathematicsCODEN: sjdmec
Cell Image Segmentation by Feature Random Enhancement Module

It is important to extract good features using an encoder to realize semantic segmentation with high accuracy. Although loss function is optimized in training deep neural network, far layers from the layers for computing loss function are difficult to train. Skip connection is effective for this problem but there are still far layers from the loss function. In this paper, we propose the Feature Random Enhancement Module which enhances the features randomly in only training. By emphasizing the features at far layers from loss function, we can train those layers well and the accuracy was improved. In experiments, we evaluated the proposed module on two kinds of cell image datasets, and our module improved the segmentation accuracy without increasing computational cost in test phase.
An Empirical Study on the Improvement of English Reading Ability of Chinese College Students Based on Big Data——Based on the Lens Grading System

Abstract The vocabulary system is the most widely used classification technique in the United States. It uses “vocabulary” as a unit to measure students’ reading ability and text difficulty, and has important reference value for teaching materials, test papers and evaluation. This article discusses the REACHE3000 system and scoring method based on the computer-based Lexile framework, and finally demonstrates the improvement of students’ reading ability through the practice of English teaching in a university in Xi’an.
Spatio-Temporal Convolutional Autoencoders for Perimeter Intrusion Detection

In the video surveillance context, a perimeter intrusion detection system (PIDS) aims to detect the presence of an intrusion in a secured perimeter. Existing camera based approaches relies on hand crafted rules, image based classification and supervised learning. In a real world intrusion detection system, we need to learn spatio-temporal features unsupervisely (as annotated data are very difficult to obtain) and use these features to detect intrusions. To tackle this problem, we propose to use a 3D convolutional autoencoder. It is inspired from the DeepFall paper where they use it for an unsupervised fall detection task. In this paper, we reproduce their results on the fall detection task and further extend this model to detect intrusions in a perimeter intrusion dataset. We also provide an extended evaluation scheme which helps to draw essential insights from the results. Our results (The source code is available at https://gitlab.liris.cnrs.fr/dlohani/stcae_pids.) show that we correctly reproduce the results of fall detection task and furthermore our model shows competitive performance in perimeter intrusion detection task. To our knowledge, it is the first time when a PIDS is made in a fully unsupervised manner while jointly learning the spatio-temporal features from a video-stream.
Major Barriers to Manufacturing SMEs Growth and Survival: Evidence From Emerging Economy Pakistan

SMEs play a tremendous role in increased national economic development and consider the backbone of the economy around the world. It contributes a great share in the GDP of Pakistan and a huge number of the workforce involved. However, despite such great importance, the SMEs growth in Pakistan face numerous challenges for access to financial resources, market information, and procurement contract. Hence the main aim behind this study is to investigate the influence of these barriers on manufacturing SMEs' growth and survival. A structured questionnaire was distributed among 172 managerial level employees of manufacturing SMEs to explore the influence of financial resources, market information, and procurement contract on the growth of SMEs by incorporating the Structural Equation Model (SEM). The study shows that the lack of access to financial resources, market information, and procurement contract negatively influence the growth and survival of SMEs. Therefore, it needs a certain action to eliminate such barriers for SMEs' sustainable growth and survival. This study suggestion will help to contribute to policymakers and industry players to overcome such barriers.
Gamma correction by using deep learning

Fringe projection profilometry (FPP) is one of the most important optical non-contact three-dimensional (3D) measurement technologies. However, in order to satisfy human beings’ visual perception, gamma is artificially added to the digital projector. In past decades, researchers have made efforts to compensate gamma nonlinear errors, but how to efficiently and conveniently correct the gamma distortion is still a big challenge. Inspired by the successful application of deep learning in FPP, we propose a deep-learning-based gamma compensation method. Through extensive data set training, the neural network can learn to acquire the distortion-free high-quality phase information from the phase-shifting images with gamma. Experimental results demonstrate that our method can effectively compensate gamma-induced phase errors, and thus improve the measurement accuracy.
Cyber Crime analysis on Social Media

Cyber Crime is a crime which is difficult to detect and stop once occurred leave a long term effect on victims. Cyber Crime is increasing day by day due to over use of social media, online shopping, and internet banking which need sensitive financial and personal data.The development in emerging internet technology and its wide spread knowledge leads to security issues, cyber crime, internet hackers and intruders. The charm of internet enhances the network structure that construct vast online theft, fraud are called as cyber attacks or cyber crime. In order to keep ourselves free from cyber crime we need to know how to protect ourselves from cyber crime. In this paper we propose different mode of cybercrime and the types of its illegal activities over the internet. Either national or international cybercrime is creating huge negative impacts over society. By focusing on various techniques we examine the cybercrime web data due to social media. Social networking websites are used as a medium of interaction and communication among all the people across the world. Due to over use and lack of awareness leads to increase the cyber crimes by several aspects.
2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation

Accurate information of the lung shape analysis and its anatomical variations is very noticeable in medical imaging. The normal variations of the lung shape can be interpreted as a normal lung. In contrast, abnormal variations of the lung shape can be a result of one of the pulmonary diseases. The goal of this study is twofold: (1) represent two lung shape models which are different at the reference points in registration process considering to show their impact on estimating the inter-patient 2D lung shape variations and (2) using the obtained models in lung field segmentation by utilizing active shape model (ASM) technique. The represented models which showed the inter-patient 2D lung shape variations in two different forms are fully compared and evaluated. The results show that the models along with standard principal component analysis (PCA) can be able to explain more than 95% of total variations in all cases using only first 7 principal component (PC) modes for both lungs. Both models are used in ASM-based segmentation technique for lung field segmentation. The segmentation results are evaluated using leave-one-out cross validation technique. According to the experimental results, the proposed method has average dice similarity coefficient of 97.1% and 96.1% for the right and the left lung, respectively. The results show that the proposed segmentation method is more stable and accurate than other model-based techniques to inter-patient lung field segmentation.