Reviewerly/exHarmony huggingface dataset:
L2 Use and Processing of Multi-Word Units

Chapter 3 offers an interdisciplinary overview of research on multi-word units in second language (L2) processing and use. It is motivated by a long-standing puzzle in the field of second language acquisition suggesting that while the idiom principle is the main mechanism of language production in native speakers, non-native speakers cannot benefit from it to a similar degree. The chapter shows that researchers are not unanimous in assessing the degree to which learners operate on the idiom principle and raises a few problems which might be obscuring such operation in commonly used research designs, such as a focus on specific multi-word units rather than co-selection as such, questionable representativeness of reference corpora and inability of statistical measures to capture abstracted associations. The chapter concludes by offering an alternative interpretation based on the concept of approximation adopted from studies of English as a lingua franca.
Noise-Robust Spoken Language Identification Using Language Relevance Factor Based Embedding

State-of-the-art systems for spoken language identification (LID) use i-vector or embedding extracted using a deep neural network (DNN) to represent the utterance. These fixed-length representations are obtained without explicitly considering the relevance of individual frame-level feature vectors in deciding the class label. In this paper, we propose a new method to represent the utterance that considers the relevance of the individual frame-level features. The proposed representation can also preserve the locally available LID-specific information in the input features to some extent. To better utilize the local-level information in the new representation, we propose a novel segment-level matching kernel based support vector machine (SVM) classifier. The proposed representation of the utterance based on the relevance of frame-level features improves the robustness of the LID system to different background noise conditions in the speech. The experiments conducted on speech with different background conditions show that the proposed approach performs better than state-of-the-art approaches in noisy speech and performs similarly to the state-of-the-art systems in clean speech condition.
An Application for Evolutionary Music Composition Using Autoencoders

This paper presents a new interactive application that can generate music according to a user's preferences inspired by the process of biological evolution. The application composes sets of songs that the user can choose from as a basis for the algorithm to evolve new music. By selecting preferred songs over successive generations, the application allows the user to explore an evolutionary musical space. The system combines autoencoder neural networks and evolution with human feedback to produce music. The autoencoder component is used to capture the essence of musical structure from a known sample of songs in a lower-dimensional space. Evolution is then applied over this representation to create new pieces based upon previously generated songs the user enjoys. In this research, we introduce the application design and explore and analyse the autoencoder model. The songs produced by the application are also analysed to confirm that the underlying model has the ability to create a diverse range of music. The application can be used by composers working with dynamically generated music, such as for video games and interactive media.
An Unsupervised Approach for Estimating Depth of Outdoor Scenes from Monocular Image

Majority of the existing deep learning based depth estimation approaches employed for finding depth from monocular image need very accurate ground truth depth information to train a supervised decision framework. However, it is not always possible to get an accurate depth information particularly for diverse outdoor scenes. To address this, a convolutional network architecture is proposed, which comprises of two encoder-decoders for utilizing stereo matching criterion for training. The image reconstruction error measure is employed for optimization of network parameters instead of ground truth depth information. To estimate an accurate disparity map in low textured and occluded regions, a cross based cost-aggregation loss term is proposed along with a novel occlusion detection and filling method in the post-processing stage. The proposed method achieves an improvement of 6.219% RMS error for a depth cap of 80m and 6.15% RMS error for a depth cap of 50 m among the unsupervised approaches on KITTI 2015 dataset. The importance of channel-wise descriptor for training a deep neural network is also established through the performance measure.
Optimization Strategy of University Campus Outdoor Environment in Cold Area

Abstract The paper aims to construct the outdoor space environment which is suitable for the cold climate, meets the campus structure and natural conditions of university, and meets the needs of the activities of teachers and students. On the basis of a large number of actual investigation and case collection, this paper studies the culture, landscape elements and design methods of the outdoor environment of universities in cold areas. The paper analyzes and summarizes the trend and existing problems, provide some feasible suggestions and strategies for the design of outdoor space environment in cold universities during the contemporary economic and social conditions.
Exploring elasticity concept using augmented reality

This era is known as the digital era, where everything is made alive by technology, include in education. To compete in this era, teachers' skills and capabilities are needed that keep up with advances in information technology. One of the applications of technology that can be used in learning is Augmented Reality (AR). This study is an exploring elasticity concept using augmented reality as an answer to the challenges that teachers represent in today's teaching. This study follows the steps that begin with the question, why am I going to do it, what will I do, what will I do, how will I do, and what has been achieved. The study revealed that physics teachers could explore the material such as elasticity and integrate technology into learning media by integrating AR Technology. The material and media expert validation has been carried out on this product. The material expert validation results were 82.35% for the suitability of the material, 100.00% for the consistency of the material, and 85.00% for the language of writing the material. The results of the AR book media expert obtained a percentage score of 81.60% and the AR application obtained a percentage score of 83.33% with very good interpretations for both.
Real-Time Sign Language Interpreter on Embedded Platform

Sign language is the primary way of communication for speech-impaired people. American Sign Language is one of the most popular sign languages and is used by over 250,000 people around the world. However, this language is only decipherable by the people who have the same disabilities, and it is not understood by masses. Hence, a need arises for an interpreter that can convert this language to text and speech. The existing research focuses on effectively converting sign language to text and speech; however, less emphasis is laid on building a system that is not only accurate but efficient as well which could be run on a compact embedded platform. This is necessary so that a portable interpreter can be made which can be easily carried and used by such people for their convenience making their day to day life easier. In this research, we propose an implementation based on MobileNet architecture, that is not only accurate but efficient as well. Moreover; a demonstration of the entire framework has also been shown on Jetson Nano.
Action Recognition in Haze Using an Efficient Fusion of Spatial and Temporal Features

Action recognition in video sequences is an active research problem in Computer Vision. However, no significant efforts have been made for recognizing actions in hazy videos. This paper proposes a novel unified model for action recognition in hazy video using an efficient combination of a Convolutional Neural Network (CNN) for obtaining the dehazed video first, followed by extracting spatial features from each frame, and a deep bidirectional LSTM (DB-LSTM) network for extracting the temporal features during action. First, each frame of the hazy video is fed into the AOD-Net (All-in-One Dehazing Network) model to obtain the clear representation of frames. Next, spatial features are extracted from every sampled dehazed frame (produced by the AOD-Net model) by using a pre-trained VGG-16 architecture, which helps reduce the redundancy and complexity. Finally, the temporal information across the frames are learnt using a DB-LSTM network, where multiple LSTM layers are stacked together in both the forward and backward passes of the network. The proposed unified model is the first attempt to recognize human action in hazy videos. Experimental results on a synthetic hazy video dataset show state-of-the-art performances in recognizing actions.
Volume Preserving Brain Lesion Segmentation

Automatic brain lesion segmentation plays an important role in clinical diagnosis and treatment. Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation due to its accuracy and efficiency. CNNs are generally trained with loss functions that measure the segmentation accuracy, such as the cross entropy loss and Dice loss. However, lesion load is a crucial measurement for disease analysis, and these loss functions do not guarantee that the volume of lesions given by CNNs agrees with that of the gold standard. In this work, we seek to address this challenge and propose volume preserving brain lesion segmentation, where a volume constraint is imposed on network outputs during the training process. Specifically, we design a differentiable mapping that approximates the volume of lesions using the segmentation probabilities. This mapping is then integrated into the training loss so that the preservation of brain lesion volume is encouraged. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, and experimental results show that our method better preserves the volume of brain lesions and improves the segmentation accuracy.
A Random Walk Approach For Determining Classification Errors in Multiple Naïve Bayes Classifiers

Classification situations are ubiquitous and arise in a range of practical applications. Very often classification problems do not occur in isolation but in clusters where several classification problems need to be solved. Examples of these include employee contract renewal in large organizations, patient prognosis in hospitals, and crowdsourced problem solving. With any form of classification, however, there are invariably inaccuracies occurring in different forms, especially when multiple classification tasks need to be performed. Since typical classifiers are not free from errors, classification errors tend to accumulate, and having frequent misclassifications are often unacceptable. Moreover, in unsupervised learning situations, there are typically no pre-determined ground truth classes; in such a situation the ground truth class is determined by the view of the majority of classifiers. In this paper, we study the situation of multiple classification within the Naïve Bayes framework, where the ground truth is determined by the decision of most classifiers, and where there is finite resources requiring decisions to be made within limited budget. Here, we represent the classification tasks as a random walk process, and perform a stochastic analysis of the situation and predict the occurrence of erroneous classification. We obtained closed-form solutions for the probability of classification inaccuracies and derive useful error bounds as a function of the budget. We find that by raising the budget, the probability of error in classification can be controlled, and the extent of the reduction can be quantified. Experimental measurements are also performed which show good agreement with the theoretical results.
Sequential Model-Based Optimization for Natural Language Processing Data Pipeline Selection and Optimization

Natural language processing (NLP) aims to analyze a large amount of natural language data. The NLP computes textual data via a set of data processing elements which is sequentially connected to a path data pipeline. Several data pipelines exist for a given set of textual data with various degrees of model accuracy. Instead of trying all the possible paths, such as random search or grid search to find an optimal path, we utilized the Bayesian optimization to search along with the space of hyper-parameters learning. In this study, we proposed a data pipeline selection for NLP using Sequential Model-based Optimization (SMBO). We implemented the SMBO for the NLP data pipeline using Hyperparameter Optimization (Hyperopt) library with Tree of Parzen Estimators (TPE) model and Adaptive Tree of Parzen Estimators (A-TPE) model for a surface model with expected improvement (EI) acquired function.
Comparisons of Deep Neural Networks in Multi-label Classification for Chinese Recipes

With the highly increasing demands, multi-label classification task has attracted more attention in recent years. However, most of traditional methods usually need tedious handcrafted features. Motivated by the remarkably strong performance of deep neural networks on the practically important tasks of natural language processing, we adopted various popular models, such as CNN, RNN and RCNN, to perform multi-label classification tasks for Chinese recipes. Based on the real Chinese recipe data extracted from websites, we compared the performance of deep neural networks in multi-label classification. We also compared them with the baseline models, such as Naive Bayes, MLKNN and fastText. In order to improve the performance of the these models, we adopted the data augmentation method and then conducted extensive experiments to compare different models in our task. The results showed that RCNN model performs the best and can get the highest score. The models based on deep neural networks all performed better than the baseline models. The results also showed that the data augmentation method is a practical method to improve the performance of all models.
ActRec: A Word Embedding-based Approach to Recommend Movie Actors to Match Role Descriptions

In this work, we propose a novel recommendation problem, actor recommendation (ActRec), based on unstructured text data for the movie industry. Given the text description of a role, we generate a ranking list of actors such that the most proper actors for the role-playing can be at top positions. We propose a word embedding-based approach to solve the ActRec problem. In addition, we compile a multi-source data from Wikipedia, Google Search, and PTT online forum. Experimental results show the promising performance of our method, which encourages future effort on ActRec.
Classification of medicine characteristic using Super Vector Machine (SVM) at Palopo regional public Hospital Sawerigading

Abstract One of the implementation of machine learning in medical world is to analyze medical dataset. Medical dataset used in this research was by using medicine dataset. Support Vector Machine Method is classification method of supervised learning, its algorithm works by using nonlinear mapping to change the data of real training into higher dimension. Selection of SVM Method is as solution to classify the characteristics of medicine. This method has function to make some similar medicines to be a group of certain data. The aim of this research was to obtain classification model which has high accuracy or small error in conducting classification of medicine data. Based on testing conducted, medicine classification using SVM produced accuracy=0,87.
Research on Dynamic Integration and Optimization of Multimedia Educational Resources from the Perspective of Digitalization

With the orderly advancement of various aspects of multimedia education informatization from the perspective of digitization, the integration and sharing of multimedia education information resources in China has become increasingly prominent. Therefore, the optimization method of dynamic integration of multimedia education resources from the digital perspective is optimized. In this paper, a large number of multimedia education resources are integrated, and the regional digital education resources sharing system is constructed to realize the application and sharing of multimedia education resources. Based on the field survey and analysis of the elements of the sharing system, this paper puts forward the mechanism of promoting the integration and sharing of digital education resources from both inside and outside the region, and realizes the dynamic integration and optimization of multimedia education resources from the digital perspective. The results show that the design of educational resources integration method to avoid the loss of educational resources, to achieve the accurate integration of resource information and complete preservation of educational resources.
Inferring Semantic Object Affordances from Videos

Lately there has been an increasing interest in learning object affordances. This is particularly to address human activity understanding and intention recognition for household robots. However, most existing approaches do not concentrate on imbibing new perceptual models of inferring affordances from visual input. Such models are key for object usage by household robots. Towards this goal, this paper introduces a knowledge based approach to inferring semantic object affordances. This is achieved by integrating ontology and qualitative reasoning within a statistical relational learning scheme. Encouraging results are obtained for the CAD-120 dataset consisting of indoor household activity videos involving human-object interactions.
WITHDRAWN: Hybrid-cloud management in secure multi-domain environments

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Simple Methodology for Eye Gaze Direction Estimation

A simple methodology (based on standard detection algorithms and a shallow neural network) is developed for estimating the eye gaze direction in natural (mostly indoor) environments. The methodology is primarily intended for evaluating human interests or preferences (e.g. in interaction with smart systems). First, techniques for detection faces, facial landmarks, eyes and irises are employed. The results are converted into numbers representing resolution-normalized locations of those landmarks. Eventually, six numbers (comprehensively combining the detection results) are used as NN inputs. The NN outputs represent 17 different directions of the eye gaze, where the neutral direction is defined by the virtual line linking the monitoring camera with the face center. Thus, the identified gaze direction results from combined motions of eyeballs and heads. For a feasibility study, a small dataset was created (showing diversified configurations of head and eyeballs orientations of 10 people looking in the specific directions). Data augmentation was used to increase the dataset volume. Using $$67\%$$ for training (in 3-fold validation) we have reached accuracy of $$91.35\%$$ .
Analysis of a method to eliminate fruitless cycles for Pollard’s rho method with skew Frobenius mapping over a Barreto-Naehrig curve

Pollard's rho method is one of the most efficient methods for solving elliptic curve discrete logarithm problem (ECDLP) in elliptic curve cryptography. Pollard's rho method with skew Frobenius mapping can solve ECDLP over a Barreto-Naehrig (BN) curve efficiently. Pollard's rho method may result in an unsolvable cycle called a fruitless cycle. When a random walk pass results in a fruitless cycle, the random walk pass must restart with a different starting point. However, an effective method for eliminating the fruitless cycle has been not proposed yet.This paper proposes a method for eliminating the fruitless cycle in Pollard's rho method with skew Frobenius mapping. In addition, the authors apply the proposed method to a BN curve with 17-bit parameters and confirm the effectiveness.
Enabling Future E- Learning Ecosystem through Design of Internet-Enabled Smart Tablet Devices

The rapid development of smartphones and tablets is significantly impacting the way we interact and use computing devices. Due to the current COVID-19 pandemic situation, many advanced and developing countries are relying heavily on e-learning, smart devices like tablets are finding their way into digital classrooms to assist teachers and enhance the way the students learn. In this paper, we present the outline of a research proposal for design, development and manufacturing of smart tablet and its affiliated software and applications. The main objective of this research is to stimulate the ecosystem for electronics, software and mobile application development and to bring the latest educational technologies in the digital classroom.
Continuous Keystroke Dynamics-Based User Authentication Using Modified Hausdorff Distance

Continuous keystroke dynamics-based user authentication methods are one of the most perspective means of user authentication in computer systems. Such methods do not require specialized equipment and allow detection of user change anytime during a user session. In this paper, we explore new approaches to solving the problem based on Hausdorff distance and its modification, including a new method, the sum of maximum coordinate deviations. We compare proposed methods to existing ones that are based on distance functions defined in feature space, statistical criteria, and neural networks. Based on the experiments, we observe that the proposed method based on the sum of maximum coordinate deviations with k nearest feature vector selection reports the highest accuracy of all reviewed methods.
Mobile Augmented Reality (mAR) blended learning application in animal cells learning

Abstract This paper develops the Augmented Reality (mAR) mobile application by combining augmented reality with mobile learning to exploit the characteristics of both elements using a blended learning approach, augmented mobile application (mAR) is designed to teach the structure and function of animal cells by combining virtual and real environments. The main contribution of this research is to enhance students learning experiences on animal cell material.