Reviewerly/exHarmony huggingface dataset:
Analysis of a Brazilian Indigenous corpus using machine learning methods

In Brazil, several minority languages suffer a serious risk of extinction. The appropriate documentation of such languages is a fundamental step to avoid that. However, for some of those languages, only a small amount of text corpora is digitally accessible. Meanwhile there are many issues related to the identification of indigenous languages, which may help to identify key similarities among them, as well as to connect related languages and dialects. Therefore, this paper proposes to study and automatically classify 26 neglected Brazilian native languages, considering a small amount of training data, under a supervised and unsupervised setting. Our findings indicate that the use of machine learning models to the analysis of Brazilian Indigenous corpora is very promising, and we hope this work encourage more research on this topic in the next years.
Explainability for NLP

This chapter explains the use of ELI5 and SHAP-explainable AI-based Python libraries with natural language processing (NLP) based tasks such as text classification models. The prediction decisions made by machine learning models for supervised learning tasks are of unstructured data. Text classification is a task where you need to consider text sentences or phrases as input and classify them into discrete categories. An example is news classification, where the content is used as the input and the output is classified into politics, business, sports, technology, and so on. A similar use case is spam detection in email classification, where the email content is used as the input and classified into spam or not spam. In this scenario, it is important to know if an email is classified into spam, then why? Which tokens present in the content actually lead to the prediction? This is of interest to the end user. When I say NLP tasks here, there any many tasks, but I’ll limit it to text classification use cases and similar use cases like entity recognition, parts of speech tagging, and sentiment analysis.
Closed-Loop Linear Covariance Framework for Path Planning in Static Uncertain Obstacle Fields

Path planning in an uncertain environment is a key enabler of true vehicle autonomy. Over the past two decades, numerous approaches have been developed to account for errors in the vehicle path while navigating complex and often uncertain environments. An important capability of such planning is the prediction of vehicle dispersion covariances about a candidate path. This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to a wide range of autonomous system architectures. Important features of the developed framework include 1) separation of high-level guidance from low-level control; 2) support for output-feedback controllers with internal states, dynamics, and output; and 3) multi-use continuous sensors for navigation state propagation, guidance, and feedback control. The closed-loop nature of the framework preserves the important coupling between the system dynamics; exogenous disturbances; and the guidance, navigation, and control algorithms. The developed framework is applied to a simplified model of an unmanned aerial vehicle and validated by comparison via Monte Carlo analysis. The utility of the CL-LinCov information is illustrated by its application to path planning in a static, uncertain obstacle field via a modified version of the rapidly exploring random tree algorithm.
Real-time vehicle detection and computer intelligent recognition through improved YOLOv4

Abstract Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for real-time and accuracy. To better balance the requirements, a vehicle detection algorithm based on the You Only Look Once (YOLO) v4 is proposed in this paper. On the one hand, the improved depthwise separable convolution is adopted to ensure the real-time performance. On the other hand, a novel feature fusion network is designed to gather more original feature information of different depth network layer. Experimental results show that the proposed algorithm can reduce the detection time by half while ensuring the accuracy, compared with the pristine YOLOv4.
SANTM: Efficient Self-attention-driven Network for Text Matching

Self-attention mechanisms have recently been embraced for a broad range of text-matching applications. Self-attention model takes only one sentence as an input with no extra information, i.e., one can utilize the final hidden state or pooling. However, text-matching problems can be interpreted either in symmetrical or asymmetrical scopes. For instance, paraphrase detection is an asymmetrical task, while textual entailment classification and question-answer matching are considered asymmetrical tasks. In this article, we leverage attractive properties of self-attention mechanism and proposes an attention-based network that incorporates three key components for inter-sequence attention: global pointwise features, preceding attentive features, and contextual features while updating the rest of the components. Our model follows evaluation on two benchmark datasets cover tasks of textual entailment and question-answer matching. The proposed efficient Self-attention-driven Network for Text Matching outperforms the state of the art on the Stanford Natural Language Inference and WikiQA datasets with much fewer parameters.
Heart Region Segmentation using Dense VNet from Multimodality Images

Cardiovascular diseases (CVD) have been identified as one of the most common causes of death in the world. Advanced development of imaging techniques is allowing timely detection of CVD and helping physicians in providing correct treatment plans in saving lives. Segmentation and Identification of various substructures of the heart are very important in modeling a digital twin of the patient-specific heart. Manual delineation of various substructures of the heart is tedious and time-consuming. Here we have implemented Dense VNet for detecting substructures of the heart from both CT and MRI multimodality data. Due to the limited availability of data we have implemented an on-the-fly elastic deformation data augmentation technique. The result of the proposed has been shown to outperform other methods reported in the literature on both CT and MRI datasets.
An Imaging system for teleteaching support using multi-view video shooted by basketball players

After the COVID-19 pandemic, the importance of teleteaching using networks has been increasing in regular university classes. The authors have been studying on the improvement of athletic performance of college women's basketball as extracurricular activities using information processing from multi-image. In this paper, we propose the use of such a multi-visual system in basketball classes. In this experiment basketball athletes at the gymnasium gallery shooted play of other athletes on the court using smartphones. Furthermore, the effectiveness of this video is discussed in terms of regular university classes. One of the characteristics of this video information system, which was realized on the premise of the existing campus network, is that it is a highly feasible system that does not interfere with other lectures and the daily work of faculty members in the university.
High performance oversampling technique considering intra‐class and inter‐class distances

Summary Oversampling methods are among the most widely used methods to solve imbalanced data analysis due to their simplicity and flexibility. Multi‐class imbalanced problems involve critical issues related to synthetic samples and corresponding consequences on the classification results. The over‐fitting phenomenon, the overlapping regions between classes, and the existence of noisy original samples may lead to a non‐optimal data distribution, which inadequately affects the classifier performance. To avoid these limitations, we propose a novel oversampling technique based on the intra‐distance‐matrix (IntraDM) and the inter‐distance‐matrix (InterDM). The new samples are created to decrease over‐fitting, decline the generation of outliers, and minimize the overlap areas. Applying the proposed method on 14 available datasets shows that it outperforms common oversampling techniques in terms of classification evaluation metrics.
Overcoming the Memory Hierarchy Inefficiencies in Graph Processing Applications

Graph processing participates a vital role in mining relational data. However, the intensive but inefficient memory accesses make graph processing applications severely bottlenecked by the conventional memory hierarchy. In this work, we focus on inefficiencies that exist on both on-chip cache and off-chip memory. First, graph processing is known dominated by expensive random accesses, which are difficult to be captured by conventional cache and prefetcher architectures, leading to low cache hits and exhausting main memory visits. Second, the off-chip bandwidth is further underutilized by the small data granularity. Because each vertex/edge data in the graph only needs 4-8B, which is much smaller than the memory access granularity of 64B. Thus, lots of bandwidth is wasted fetching unnecessary data. Therefore, we present G-MEM, a customized memory hierarchy design for graph processing applications. First, we propose a coherence-free scratchpad as the on-chip memory, which leverages the power-law characteristic of graphs and only stores those hot data that are frequent-accessed. We equip the scratchpad memory with a degree-aware mapping strategy to better manage it for various applications. On the other hand, we design an elastic-granularity DRAM (EG-DRAM) to facilitate the main memory access. The EG-DRAM is based on near-data processing architecture, which processes and coalesces multiple fine-grained memory accesses together to maximize bandwidth efficiency. Putting them together, the G-MEM demonstrates a 2.48 × overall speedup over a vanilla CPU, with 1.44 × and 1.79 × speedup against the state-of-the-art cache architecture and memory subsystem, respectively.
Attentive encoder-decoder networks for crowd counting

Crowd counting that aims to estimate the crowd density has recently made significant progress but remains an unsolved problem due to several challenges. In this paper, we propose an Attentive Encoder-Decoder Network (AEDNet) to overcome the notorious scale-variation problem in crowd counting. Our major contributions can be summarized in three aspects. First, we design an Attentive Feature Refinement (AFR) block in the encoder to adaptively extract multi-scale features. AFR compares the spatial information in different scales through the attention mechanism and then adaptively assign importance weights to each point, which highlights the distinctive roles in multi-scale feature extraction. Second, we develop a Separable Non-local Fusion (SNF) block in the decoder with the self-attention mechanism to aggregate multi-scale features from different layers, which not only achieves the sufficient feature fusion by capturing long-range dependencies, but also vastly reduces the computation cost compared to the original non-local operation. Third, we propose a Regional MSE (R-MSE) loss to tackle the pixel-isolation problems in regular MSE loss. To demonstrate the effectiveness of the proposed AEDNet, we conduct extensive experiments on four widely-used crowd counting datasets, and our AEDNet consistently achieves the state-of-the-art performance.
Presentación

The broadest definition of hybrid education is the articulation of learning opportunities that combine both classroom and digital traditions. However, this conceptual umbrella is very general, since the nuances of the notion of hybrid learning environment are as diverse as the educational needs that generate them and the approaches or designs with which they are sought to be implemented. It is not only about thinking about education + internet, but rather about understanding an educational process that integrates elements of distinct nature within the same environment. As Floridi (2015) argues, information and communication technologies delimit environments that affect, among other aspects, our self-concept, our perception of reality and the interactions derived from it. The Internet draws an environment of action and representation in where people live and learn which combines overlaps or hybridizes with other classic educational environments. According to Wegerif (2013), this would be one of the biggest impacts of the internet on education: having blurred the demarcations of education. Thus, it is urgent to respond to the pedagogical demand of understanding, analyzing and valuing the opportunities and dilemmas that digital technology holds, upon the imminent construction of hybrid learning.
MAIN: Multimodal Attention-based Fusion Networks for Diagnosis Prediction

Predicting the future diagnoses from patients’ historical Electronic Health Records (EHR) is a significant task in healthcare. EHR consist of multiple modal data, each modality has different features and contains a wealth of information of patients. However, most of the existing EHR-based prediction methods either only use unimodal data, or fail to fully explore the correlation between different modalities when fusing multimodal data. To address these challenges, we propose a Multimodal Attention-based fusIon Networks (MAIN) for diagnosis prediction. In this model, we first design different feature extraction modules for each modality. Then, an inter-modal correlation module which contains two layers is applied to capture the intermodal correlation. Finally, a multimodal fusion module based on weighted averaging is utilized to integrate the representations derived from different modalities and their correlation to obtain the patient representation for diagnosis prediction. We evaluate our proposed model on two medical datasets, and the experimental results demonstrate the effectiveness of MAIN.
An Impact of Adoption of the Internet of Things (IoT) on Customer Relationship Management (CRM) in the Banking Sector of India

Technology nowadays has become an important part of our everyday life. Innovations and up-gradation in technology help organizations and businesses to survive the massive global competition. In this paper, the impact of the Internet of things on Customer relationship management (CRM) has been discussed. The paper starts with a purpose to find out the impact of Cost, Convenience, Status, and Privacy on the adoption of the Internet of things (IoT) in banks. Later on, the impact of IoT adoption on CRM has also been checked. The CRM services have been defined with the help of three most important terms, responsible for the smooth CRM in banks, Responsiveness, Satisfaction, and Assurance. The responses of 366 educated customers from public and private banks have been taken for the study through questionnaires comprising 29 items. The Confirmatory Factor Analysis (CFA) and the Structural Equation Modeling (SEM) have been used to test the model fitness, reliability, and validity of the data and to test the hypotheses, respectively. The results reveal that not only Cost and Privacy have a significant impact on the adoption of IoT, but the Adoption of IoT also has a significant impact on CRM of banks. The present paper attempts to help the policymakers and customers who are inclined to know about the 14impact of newer technologies on the application of services in a better and easy way so that these services can be easily adaptable in the public interest.
Geographical Distance Issues and their Mitigation Strategies in GSD: A Systematic Literature Review towards Conceptual Framework

Global Software Development (GSD) practice has been increasingly emerging in the software industry in the last few decades because of its incentives. However, GSD is not a simple process organization also have to face several challenges. Communication between dispersed teams is the most critical risk in GSD. Communication risk is further divided into geographical, temporal, and cultural distance risks. Among these risks, geographical distance risks are critical, and they produce significant problems in GSD projects. Thus, it leads the project toward failure. The main objective of this research is to identify geographical distance risks and their mitigation strategies to alleviate the identified risks through a systematic literature review. A total of 8 geographical distance risks and best practices used to mitigate these risks were identified from the literature.
The use of emoji to establish student wellbeing: does the image reflect the reality?

Abstract Introduction: The impact of COVID-19 social restrictions on mental wellbeing of health professional students during placement is largely unknown. Conventional survey methods do not capture emotional fluctuations. Increasing use of smartphones suggests short message service (SMS) functionality could provide easy, rapid data. This project tested the feasibility and validity of gathering data on Therapeutic Radiography student mental wellbeing during clinical placement via emoji and SMS. Methods: Participants provided anonymous daily emoji responses via WhatsApp to a dedicated mobile phone. Additional weekly prompts sought textual responses indicating factors impacting on wellbeing. A short anonymous online survey validated responses and provided feedback on the method. Results: Participants ( n = 15) provided 254 daily responses using 108 different emoji; these triangulated with weekly textual responses. Feedback concerning the method was positive. ‘Happy’ emoji were used most frequently; social interaction and fatigue were important wellbeing factors. Anonymity and opportunity to feedback via SMS were received positively; ease and rapidity of response engendered engagement throughout the 3-week study. Conclusions: The use of emoji for rapid assessment of cohort mental wellbeing is valid and potentially useful alongside more formal evaluation and support strategies. Capturing simple wellbeing responses from a cohort may facilitate the organisation of timely support interventions.
A New Improved Echo State Network with Multiple Output Layers for Time Series Prediction

Through investigating the traditional echo state network, their output structure has only one output layer using the same output weight learning method, such that the networked prediction results is not always reliable. Therefore, a new echo state network with multiple output layers (MOL-ESN) in parallel configuration is proposed for time series prediction in this paper. For the output structure of MOL-ESN, multiple output layers with different output weight learning methods are built. Considering the multiple output layers are introduced, the computing burden of training the MOL-ESN will be also increased, and thus, on the premise of ensuring the stable network output, the prediction performance of the MOL-ESN need to be improved. Finally, the effectiveness of the proposed network is verified by a simulation example.
Adversarial Separation Network for Text Style Transfer

This article considers the task of text style transfer: transforming a specific style of sentence into another while preserving its style-independent content. A dominate approach to text style transfer is to learn a good content factor of text, define a fixed vector for every style and recombine them to generate text in the required style. In fact, there are a large number of different words to convey the same style from different aspects. Thus, using a fixed vector to represent one style is very inefficient, which causes the weak representation power of the style vector and limits text diversity of the same style. To address this problem, we propose a novel neural generative model called Adversarial Separation Network (ASN), which can learn the content and style vector jointly and the learnt vectors have strong representation power and good interpretabilities. In our method, adversarial learning is implemented to enhance our model’s capability of disentangling the two factors. To evaluate our method, we conduct experiments on two benchmark datasets. Experimental results show our method can perform style transfer better than strong comparison systems. We also demonstrate the strong interpretability of the learnt latent vectors.
Cascade Generalization Based Functional Tree for Website Phishing Detection

The advent of the web and internet space has seen its adoption for rendering various services -from financial to medical services. This has brought an increase in the rate of cybersecurity issues over the years and a prominent one is the phishing attack where malicious websites mimic the appearance and functionalities of another legitimate website to collect users’ credentials required for access to services. Several measures have been proposed to mitigate this attack; blacklisting and variants of machine learning approaches have been employed, yielding good performance results. However, there is a need to increase the rate of identification of phishing attacks and reduce the rate of false positives. This study proposes the use of a functional tree (FT) machine learning approach to mitigate phishing attacks. FT, a hybridization of multivariate decision trees and discriminant function using constructive induction, uses logistic regression for splitting tree nodes and leaf prediction, unlike the conventional decision tree that simply split nodes based on the data. Furthermore, a variant of the FT is proposed based on cascade generalization (CG-FT). Three datasets with varied instance distributions, both balanced and imbalanced, are used in the empirical investigation of the performance of the proposed CG-FT. The results showed that FT has improved performances over some selected baseline classifiers. Relative to FT, the CG-FT techniques showed improvement in the detection of a phishing attack with Area Under the Curve (AUC) and True Positive rate (TP-rate) ranging from 98–99.6% and 92–97% respectively in the datasets. Also, the false-positive rate is reduced with values ranging from 1.7 to 6.1%. The proposed CG-FT showed improvement over all the other reviewed approaches based on studied performance metrics. The use of FT and its hybridization with cascade generalization (CG-FT) showed an improvement in performance in the mitigation of phishing attacks.
AI-Enabled Computational Techniques for Cancer Diagnosis

Cancer is the world's top cause of death, expected to claim roughly 10 million lives by 2020. In clinics, medical practitioners employ a variety of approaches to diagnose cancer, such as physical examinations, laboratory tests, imaging studies, and biopsy. The critical nature of classifying cancer patients as high or low risk has prompted numerous research teams in the biomedical and bioinformatics fields to investigate the application of machine learning (ML) technologies. This research examines artificial intelligence-based methods for various cancer types. AI-enabled automated or computer-assisted approaches are chosen, but they are an excellent fit for precisely and efficiently processing a huge dataset in cancer detection. These systems allow for diagnosis and treatment. According to studies, the majority of these technologies provide exact diagnoses and can resolve the issue if used correctly. Unfortunately, these technologies must overcome a number of hurdles before they can be employed in clinics. Furthermore, data processing must be upgraded in order to be compatible with AI and machine learning. Based on the research findings, this paper concludes that AI-based learning systems offer a tremendous potential for cancer prognosis and treatment.
Inexact Silicon Photonics: From Devices to Applications

We present an overview of our recent work on variation analysis in silicon photonic integrated circuits. We discuss the promise of design-space exploration and approximate communication to enhance robustness in systems employing silicon photonics.