Reviewerly/exHarmony huggingface dataset:
Implementation of Alexa-Based Intelligent Voice Response System for Smart Campus

Retrieving information from a source is one of the hassle works to do in this world. Right from the manual intervention to computer world, information retrieval is known for its trouble factors. Conveying rightful information to the rightful person may be a guideline to face consequences. Though the information retrieval was changed digitally, it also requires a manual intervention to take look at that information. Our idea was to retrieve information of students through virtual voice assistant where it requires zero manual intervention except for asking a query. There are various virtual voice assistants in the market like Google Assistant, Cortana, Alexa, and Siri. In this, the Alexa can be chosen for a specific reason as it works with voice processing cloud, and it has scalable database. Amazon Developer helps the developers to customize the Alexa device by creating a new skill sets according to the need. Alexa works with three different devices, and they are Amazon’s Echo Dot, Echo, and Echo Plus. The skill set that is developed is compatible in all three devices, and Echo Dot is compact and portable for testing. Therefore, users can ask Alexa for specific information, and Alexa would reply them at ease. The following paper consists of development of skill set for the Alexa device that would retrieve information about the students if it is properly fed in its database.
Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually, the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this, we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network, which integrates both global information and local information of an image. We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality. In order to better adapt to the quality assessment of the entire image, we use two different loss functions in the training and validation phases. The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.
Breast Cancer Identification and Diagnosis Techniques

Identification of disease in humans accurately is very difficult and also important for further treatment. One of the major tasks for a doctor is the identification of the disease. Once the disease is identified then it is very easy to perform diagnosis for the patient. In this chapter, we reviewed and presented various machine learning and deep learning algorithms for disease identification. Mainly we are presenting on one of the most occurring diseases in women, that is breast cancer. Here we are presenting the various methodology and algorithms for the identification of breast cancer. Some of the methodologies and algorithms are Support Vector Machine (SVM), Biclustering mining and Adaboost Algorithm, Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Breast Imaging reporting and data system (BI-RADS), ICD-9 diagnosis codes from an existing EHR data repository, Hierarchical Attention bidirectional networks (HA-BiRNN), Clinical decision support system, Outlier Detection Algorithm (ODA).
Society 5.0: A brave new world

Society 5.0 is Japan's concept of a technology-based, humancentred society. It is essentially an impressive upgrade on existing society that will better human existence. It will emerge from the fourth industrial revolution and will see humans and machines coexisting in harmony. Technology such as Artificial Intelligence (AI) will permeate all areas of life; including, for example, healthcare, the environment, scientific research and ethics.
Revisiting Loss Landscape for Adversarial Robustness

The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Among them, adversarial training is the most promising one, based on which, a lot of improvements have been developed, such as adding regularizations or leveraging unlabeled data. However, these improvements seem to come from isolated perspectives, so that we are curious about if there is something in common behind them. In this paper, we investigate the surface geometry of several well-recognized adversarial training variants, and reveal that their adversarial loss landscape is closely related to the adversarially robust generalization, i.e., the flatter the adversarial loss landscape, the smaller the adversarially robust generalization gap. Based on this finding, we then propose a simple yet effective module, Adversarial Weight Perturbation (AWP), to directly regularize the flatness of the adversarial loss landscape in the adversarial training framework. Extensive experiments demonstrate that AWP indeed owns flatter landscape and can be easily incorporated into various adversarial training variants to enhance their adversarial robustness further.
Unsupervised Few-shot Learning via Distribution Shift-based Augmentation

Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current works heavily rely on a large-scale labeled auxiliary set to train their models in an episodic-training paradigm. Such a kind of supervised setting basically limits the widespread use of few-shot learning algorithms. Instead, in this paper, we develop a novel framework called Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation (ULDA), which pays attention to the distribution diversity inside each constructed pretext few-shot task when using data augmentation. Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model learn more robust feature representations. In ULDA, we systemically investigate the effects of different augmentation techniques and propose to strengthen the distribution diversity (or difference) between the query set and support set in each few-shot task, by augmenting these two sets diversely (i.e., distribution shifting). In this way, even incorporated with simple augmentation techniques (e.g., random crop, color jittering, or rotation), our ULDA can produce a significant improvement. In the experiments, few-shot models learned by ULDA can achieve superior generalization performance and obtain state-of-the-art results in a variety of established few-shot learning tasks on Omniglot and miniImageNet. The source code is available in this https URL.
Interactive Voice Application-Based Amazigh Speech Recognition

This paper aims to build an interactive speaker-independent automatic Amazigh speech recognition system. The proposed system offers a methodology to extract data remotely from a distance database using the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies. We describe our experience to design an interactive speech system based on hidden Markov models (HMMs), Gaussian mixture models (GMMs) and Mel frequency spectral coefficients (MFCCs) based on ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.
Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset

Abstract Imbalanced data causes misclassification because the majority of the dominant data is in the minority data, which results in a decrease in the value of accuracy. UCI dataset is a public dataset that can be used as a dataset in machine learning. This study aims to evaluate the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods on data imbalances in MWMOTE. MWMOTE is used in resolving Imbalanced cases through weighting and grouping. This goal is achieved by evaluating the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods in MWMOTE to produce more representative synthetic data and increase the accuracy value. The results obtained from this study indicate that the Decision Tree has higher evaluations of recall, precision, F-measure, and accuracy compared to K-NN, Naive Bayes, and Support Vector Machine for data that are balanced with MWMOTE.
Dynamic evaluation of development of microscopic strain distribution by digital image correlation with motion video taken by scanning electron microscopy

To analyse the dynamic development of microscopically and heterogeneously distributed plastic strain in metallic materials during deformation, a digital image correlation (DIC) technique was used on a motion video taken by scanning electron microscopy (SEM) for observing in situ tensile deformation of stable austenitic stainless steel. A denoising technique using deep learning-based image processing enabled the application of SEM video to DIC (dynamic SEM-DIC). The dynamic SEM-DIC analysis revealed that multi-slip deformation occurred after a certain period of primary slip deformation. The rate of the primary slip was then delayed by the activation of the secondary slip system.
A multi-objective optimization method for intelligent swarm robotic control model with changeable parameters

引言近年来, 针对群体机器人(swarm robotics, SR)的研
A Systematic Review of Health Dialog Systems

Abstract Background Health dialog systems have seen increased adoption by patients, hospitals, and universities due to the confluence of advancements in machine learning and the ubiquity of high-performance hardware that supports real-time speech recognition, high-fidelity text-to-speech, and semantic understanding of natural language. Objectives This review seeks to enumerate opportunities to apply dialog systems toward the improvement of health outcomes while identifying both gaps in the current literature that may impede their implementation and recommendations that may improve their success in medical practice. Methods A search over PubMed and the ACM Digital Library was conducted on September 12, 2017 to collect all articles related to dialog systems within the domain of health care. These results were screened for eligibility with the main criteria being a peer-reviewed study of a system that includes both a natural language interface and either end-user testing or practical implementation. Results Forty-six studies met the inclusion criteria including 24 quasi-experimental studies, 16 randomized control trials, 2 case–control studies, 2 prospective cohort studies, 1 system description, and 1 human–computer conversation analysis. These studies evaluated dialog systems in five application domains: medical education (n = 20), clinical processes (n = 14), mental health (n = 5), personal health agents (n = 5), and patient education (n = 2). Conclusion We found that dialog systems have been widely applied to health care; however, most studies are not reproducible making direct comparison between systems and independent confirmation of findings difficult. Widespread adoption will also require the adoption of standard evaluation and reporting methods for health dialog systems to demonstrate clinical significance.
Real-time Super Resolution CNN Accelerator with Constant Kernel Size Winograd Convolution

This paper presents a super-resolution CNN de-signed for real-time hardware processing and the associated hardware architecture. Previous networks typically contain numerous layers, various kernel sizes and deconvolution layers, making it hard for hardware implementation. In this paper, we present a CNN only consisting of 3×3 convolution, replacing the deconvolution by pixel shuffling. Such regularity of kernel size enables us to employ Winograd convolution to implement the whole network. The proposed architecture achieves output resolution of 1920×1080 (FHD) at 60 fps while working at a clock frequency of 200 MHz. It also outperforms other 12K-parameter networks in image quality.
Screenplay Summarization Using Latent Narrative Structure

Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often perform a smart selection of sentences from the beginning of the document. When summarizing long narratives, which have complex structure and present information piecemeal, simple position heuristics are not sufficient. In this paper, we propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays (i.e., extract an optimal sequence of scenes). Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode and improve summarization performance over general extractive algorithms leading to more complete and diverse summaries.
CohEval: Benchmarking Coherence Models

Although coherence modeling has come a long way in developing novel models, their evaluation on downstream applications has largely been neglected. With the advancements made by neural approaches in applications such as machine translation, text summarization and dialogue systems, the need for standard coherence evaluation is now more crucial than ever. In this paper, we propose to benchmark coherence models on a number of synthetic and downstream tasks. In particular, we evaluate well-known traditional and neural coherence models on sentence ordering tasks, and also on three downstream applications including coherence evaluation for machine translation, summarization and next utterance prediction. We also show model produced rankings for pre-trained language model outputs as another use-case. Our results demonstrate a weak correlation between the model performances in the synthetic tasks and the downstream applications, motivating alternate evaluation methods for coherence models. This work has led us to create a leaderboard to foster further research in coherence modeling.
A transaction log analysis of EBSCO Discovery Service using Google Analytics: the methodology

Purpose The article presents a methodology that can be used to analyze data from the transaction log of EBSCO Discovery Service searches recorded in Google Analytics. It explains the steps to follow for exporting the data, analyzing the data, and recreating searches. The article provides suggestions to improve the quality of research on the topic. It also includes advice to vendors on improving the quality of transaction log software. Design/methodology/approach Case study Findings Although Google Analytics can be used to study transaction logs accurately, vendors still need to improve the functionality so librarians can gain the most benefit from it. Research limitations/implications The research is applicable to the usage of Google Analytics with EBSCO Discovery Service. Practical implications The steps presented in the article can be followed as a step-by-step guide to repeating the study at other institutions. Social implications The methodology in this article can be used to assess how library instruction can be improved. Originality/value This article provides a detailed description of a transaction log analysis process that other articles have not previously described. This includes a description of a methodology for accurately calculating statistics from Google Analytics data and provides steps for recreating accurate searches from data recorded in Google Analytics.
EXPLORING UNDERGRADUATE STUDENTS’ PERCEPTION TOWARD THE USE OF DUOLINGO IN LEARNING ENGLISH

Purpose of the study: This study was aimed at investigating the students’ attitudes and perception toward the use of Duolingo in Learning English Methodology: A mixed-method design (quantitative-qualitative) was employed in this study. 60 undergraduate students of Syiah Kuala University participated in this study. They were chosen by using purposive sampling techniques. In obtaining the data, questionnaires and interviews were implemented in this study. In the question area, there were 16 close-ended questions adapted from Yang (2012a) and Garcia Botero and Questier (2016) designed using the Likert scale method. Besides, 3 interview questions were asked to 10 chosen students to deeply reveal their perception in using the Duolingo. Main Findings: The result indicated that most students have a positive perception of the use of Duolingo in learning English (Mean=4.0). This study has found that students view Duolingo as a useful and helpful application and a motivational tool in learning English. One of the more significant findings to emerge from this study is the inconsistency between the students’ perception and their acts in using the application. Their positive view on the use of Duolingo was contrary to the fact that they are lacking time in using the application. Applications of this study: This study is intended to shed some lights on how the students perceive their experiences in learning English after using the Duolingo as a part of their out of class engagement. Novelty/Originality of this study: The students’ motivation to learn English outside of the classroom can be enhanced through the use of the Duolingo in mobile phones.
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space

Most of the successful and predominant methods for bilingual lexicon induction (BLI) are mapping-based, where a linear mapping function is learned with the assumption that the word embedding spaces of different languages exhibit similar geometric structures (i.e., approximately isomorphic). However, several recent studies have criticized this simplified assumption showing that it does not hold in general even for closely related languages. In this work, we propose a novel semi-supervised method to learn cross-lingual word embeddings for BLI. Our model is independent of the isomorphic assumption and uses nonlinear mapping in the latent space of two independently trained auto-encoders. Through extensive experiments on fifteen (15) different language pairs (in both directions) comprising resource-rich and low-resource languages from two different datasets, we demonstrate that our method outperforms existing models by a good margin. Ablation studies show the importance of different model components and the necessity of non-linear mapping.
Basic to Compound: A Novel Transfer Learning Approach for Bengali Handwritten Character Recognition

Transfer learning is widely used in various character recognition tasks. In this paper, we propose a transfer learning approach with convolutional neural network (CNN) for Bengali handwritten character recognition. When children learn the Bengali scripts, they first learn basic characters (vowels and consonants) and then go for compound characters (consonant conjuncts). Without prior knowledge of basic characters, it would be quite difficult for them to learn compound characters. In our approach, the machine mimics this human child learning process. Our study shows that CNN trained on basic characters is well capable of recognizing compound characters with minimal retraining. It performs better and also trains much faster than CNN fully trained on compound characters. Similarly, CNN trained on digits easily recognizes basic characters with a short period of training. Furthermore, pretrained CNN consistently outperforms the randomly initialized CNN while training only last few layers.
Neural i-vectors

Deep speaker embeddings have been demonstrated to outperform their generative counterparts, i-vectors, in recent speaker verification evaluations. To combine the benefits of high performance and generative interpretation, we investigate the use of deep embedding extractor and i-vector extractor in succession. To bundle the deep embedding extractor with an i-vector extractor, we adopt aggregation layers inspired by the Gaussian mixture model (GMM) to the embedding extractor networks. The inclusion of GMM-like layer allows the discriminatively trained network to be used as a provider of sufficient statistics for the i-vector extractor to extract what we call neural i-vectors. We compare the deep embeddings to the proposed neural i-vectors on the Speakers in the Wild (SITW) and the Speaker Recognition Evaluation (SRE) 2018 and 2019 datasets. On the core-core condition of SITW, our deep embeddings obtain performance comparative to the state-of-the-art. The neural i-vectors obtain about 50% worse performance than the deep embeddings, but on the other hand outperform the previous i-vector approaches reported in the literature by a clear margin.
Towards Accessibility in VR - Development of an Affordable Motion Platform for Wheelchairs

In virtual reality research, extensive effort has been made towards thoughtfully investigating various locomotion and interaction techniques in order to constitute an immersive experience for the user. Mandated by design, seated applications are obliged to utilize alternate means of locomotion due to the inability to naturally move through the environment. Whilst complex devices and techniques are proposed in state-of-the art applications [3], [9], the subset of physically handicapped people is often disregarded by these.
POLITICAL AND ECONOMIC INSTITUTIONS AND HUMAN DEVELOPMENT: POST-COMMUNIST NATIONS

The literature has extensively investigated the relationships between country economic and political institutions and human well-being. I assume that economic institutions can be the determinants of human development and provide a more robust explanation of the latter under the influence of political institutions in the 2SLS instrumental variable estimation than in the ordinary least square (OLS) estimation. I also assume that most of the Post-Communist Nations (PCN) were transitioning their economic and social systems focusing on human development, among other critical reforms and programs. Data confirm this assumption for 22 countries out of 25. Results indicate that there were causal relationships between human development and business, monetary, and investment freedom, and freedom from corruption and government spending. Only government spending had a negative effect on human development. Economic, labor, trade, fiscal, and financial freedom were not endogenous variables with strong instruments. Therefore, these institutions did not have a stronger impact on human development. External validation of the estimates using data from the rest of the world confirms the results. All the mentioned economic institutions had a significant impact on human development in the rest of the world under the influence of the same instrumental variables: political rights, civil liberties, property rights, the median age of the population, and geographic location. Government spending again had a significant and negative effect on human development.