Reviewerly/exHarmony huggingface dataset:
Integrity and memory consumption aware electronic health record handling in cloud

Cloud environment greatly necessitates two key factors namely integrity and memory consumption. In the proposed work, an efficient integrity check system (EICS) is presented for electronic health record (EHR) classification. The existing system does not concentrate on storage concerns such as storing and retrieving files in cloud and memory storage overheads. De-duplication is one of the solution, however original information loss might take place. This is mitigated by the suggested research work namely Integrity and Memory Consumption aware De-duplication Method (IMCDM), where health care files are stored in secured and reliable manner. File Indexed table are created for all the files for enhancing de-duplication performance before uploading it into server. Duplication existence can be obtained from the indexing table which comprises of file features and hash values. Support vector machine (SVM) classifier is used in indexing table construction for file feature learning. Labels allotted through SVM classifier is considered as index values. Two level encryption is used followed by indexing construction, and stored in cloud severs. For avoiding redundant data, a decrypted hash index comparison is performed with previously stored contents. Various security key based on individual user’s generation is carried for ensuring security and XOR operation is performed with received encrypted file. The evaluation is performed using the Java simulation tool, which aids in validating the proposed methodology against existing research.
Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary.
IDBP: Image Dehazing Using Blended Priors Including Non-Local, Local, and Global Priors

In this letter, a robust and promising atmospheric scattering model (ASM)-based image dehazing technique called IDBP is developed, which overcomes the intrinsic limitation of available techniques based on single priors. It consists of two modules, i.e., an atmospheric light estimation (ALE) module and a multiple prior constraint (MPC) module. The ALE module is based on a new global brightening strategy of enhancing the brightness of image with minimum information loss. The MPC smartly blends the constrains of non-local prior, local prior, and global prior to shrink the solution space of haze removal, which avoids the limitation of using any single priors. Unlike previous works, IDBP does not require any training process, but is based on multiple priors and minimal information loss principle to impose the ASM, thereby making it easy to implement and ensuring its robustness. Numerous experiments reveal that the proposed IDBP outperforms the state-of-the-art alternates.
Low-pass filtering based polarimetric dehazing method for dense haze removal

Polarimetric dehazing method is very promising in enhancing the quality of images captured in the scattering media. However, it is found that the dehazing results calculated by hazy images are very sensitive to the noise, which may cause the method unstable or even invalid. To overcome this drawback and enhance the capability and stability of the polarimetric dehazing method, digital image processing algorithms or bias parameters need to be added into the method, however, they will make the algorithm complex and time consuming. In this paper, using low pass filter to suppress the noise of the hazy images, a novel polarimetric dehazing method is proposed to enhance the visibility of hazy images, especially for dense haze removal. Experimental results demonstrate that this method is totally automatic and very effective in dense haze processing. This method may have great potential usage in many applications, such as optical surveillance, underwater imaging, and bio-tissue imaging, etc.
SAR-Optical Image Matching by Integrating Siamese U-Net With FFT Correlation

The main difficulty of synthetic aperture radar (SAR)-optical image matching or registration lies in the significant heterogeneous characteristics introduced by the different imaging mechanisms between SAR and optical images. Instead of directly using the raw image pair, transforming the pair into a feature domain, where they have homogeneous feature representation, is believed more effective. Inspired by image segmentation, we develop an end-to-end deep learning model for the SAR-optical matching, based on a siamese U-net with a fast Fourier transform (FFT) correlation layer. First, the siamese U-net with sharing weights extracts the feature maps of the SAR and optical images and projects the heterogeneous images into a homogeneous space. Then, the two feature maps are cross-correlated or normalized cross-correlated by the FFT layer and a similarity heatmap is obtained. Finally, the heatmap is send into a softmax2d classifier to determine the best matching, and thus matching is converted into classification. The nonlinear mapping capability of deep learning can well tackle the intensity variation across the different imaging modals; the encoder–decoder architecture with skip connections in the U-net can take full advantage of the global information and simultaneously preserve the local resolution and position information and thus guarantees high accuracy and robustness; besides, the FFT correlation is helpful for the efficiency improvement and training with large image pairs. Experiments show that the proposed method can achieve a pixel-level matching error.
Emotion recognition using one-shot learning for human-computer interactions

Emotion recognition of special needs individuals is one of the challenges for those involved in the learning and care of such individuals. Misunderstanding of the true emotions of the special-needs individuals may lead to erroneous corrective actions which may further exacerbate their emotions/frustrations. This work aims to provide a solution to be deployed in learning environments with individuals with autism spectrum disorders (considered in this work) to aid the primary caregivers in understanding the emotional states for a group of special-needs individuals, such as those in a learning environment. The system is trained on the emotion data captured of the individuals in the group and then deployed to identify and display the emotions in a pictorial form to the primary caregiver on the attached display. Through this work, we were able to perform the emotion recognition for the selected group individuals with a high degree of accuracy (99.8%) without requiring large datasets (total of 125 training images). Additionally, the learned model was small and robust enough to be deployed on resource constrained platform ie. The raspberry pi, without the need for any cloud-based support. This ensures that the system works completely offline and thus allays any fears of intrusion of privacy.
Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis

Volatility and investor sentiment have been factors for the slow adoption rate of Bitcoin (BTC) that was first recognized in 2008 as a potential store of value, investment vehicle and a hedge alternative to gold during a recession. The purpose of this applied mathematics study will use a multivariate DCC GARCH model. Bitcoin holds its ground in volatility. This study examines Bitcoin as an investment and hedge alternative to gold as well as the major stock index. To perform the research to explore the viability of Bitcoin as an investment and hedge alternative to gold, the authors conducted a DCC GARCH model analysis. The findings of this research paper confirm Bitcoin’s cyclical performance between volatility and adoption. The findings give a strong ground for Bitcoin as the new digital currency, store of value, medium of exchange, and a unit of account and incentivize further research by theorists, scholars and examiners. The significance of this applied mathematics research and analysis will allow an unstoppable, incorruptible, and uncontrollable store of value, and investment vehicle, without governmental or institutional intervention. This study contributes by comparing and contrasting volatility stability based on the return levels of each Bitcoin on major indexes traded with BTC (based on fiat currencies) and gold.
End-to-End Speech Translation via Cross-Modal Progressive Training

End-to-end speech translation models have become a new trend in the research due to their potential of reducing error propagation. However, these models still suffer from the challenge of data scarcity. How to effectively make use of unlabeled or other parallel corpora from machine translation is promising but still an open problem. In this paper, we propose Cross Speech-Text Network (XSTNet), an end-to-end model for speech-to-text translation. XSTNet takes both speech and text as input and outputs both transcription and translation text. The model benefits from its three key design aspects: a self supervising pre-trained sub-network as the audio encoder, a multi-task training objective to exploit additional parallel bilingual text, and a progressive training procedure. We evaluate the performance of XSTNet and baselines on the MuST-C En-De/Fr/Ru datasets. XSTNet achieves state-of-the-art results on all three language directions with an average BLEU of 27.8, outperforming the previous best method by 3.7 BLEU. The code and the models will be released to the public.
Addressing Popular Concerns Regarding COVID-19 Vaccination with Natural Language Argumentation Dialogues

Chatbots have the potential of being used as dialogical argumentation systems for behaviour change applications. They thereby offer a cost-effective and scalable alternative to in-person consultations with health professionals that users could engage in from the comfort of their own home. During events like the global COVID-19 pandemic, it is even more important than usual that people are well informed and make conscious decisions that benefit themselves. Getting a COVID-19 vaccine is a prime example of a behaviour that benefits the individual, as well as society as a whole. In this paper, we present a chatbot that engages in dialogues with users who do not want to get vaccinated, with the goal to persuade them to change their stance and get a vaccine. The chatbot is equipped with a small repository of arguments that it uses to counter user arguments on why the user is reluctant to get a vaccine. We evaluate our chatbot in a study with participants.
Federated learning and next generation wireless communications: A survey on bidirectional relationship

Abstract In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.
Big Data Analytics in Association Rule Mining: A Systematic Literature Review

Due to the rapid impact of IT technology, data across the globe is growing exponentially as compared to the last decade. Therefore, the efficient analysis and application of big data require special technologies. The present study performs a systematic literature review to synthesize recent research on the applicability of big data analytics in association rule mining (ARM). Our research strategy identified 4797 scientific articles, 27 of which were identified as primary papers relevant to our research. We have extracted data from these papers to identify various technologies and algorithms of using big data in association rule mining and identified their limitations in regards to the big data categories (volume, velocity, variety, and veracity).
In-situ measurement of machining part deflection with Digital Image Correlation

In the context of the aeronautics industry, aluminum alloy structural parts are manufactured in several stages, from forming processes and heat treatments to final machining. Some process steps may generate residual stresses. Thus, material removal during machining releases these residual stresses, which induces part deformation. Such deformations can lead to geometric nonconformity of the machined part. It is therefore essential to control this phenomenon. Due to the variability in residual stress distribution in each raw part, the modeling approaches must to be coupled with experimental measurements. This article thus aims to define a reliable experimental technique for measuring in-plane deformation of large aeronautical parts during their machining. The backbone of the technique relies on Digital Image Correlation (DIC), which enables the contactless measurement of part deformation during machining. Moreover, DIC provides a full-field measurement and a direct evaluation of part deformations. This work discusses more specifically problems related to the use of DIC during machining, the latter corresponding to a particularly harsh environment. Indeed, optical systems undergo undesirable movement and metal chips hide areas of the observed part. These unwanted events corrupt the results. In order to control these problems and consistently apply DIC part deformation measurement during machining, specific methods are proposed in this paper. Finally, DIC measurements are performed during the same machining sequence of two parts. The excellent agreement of the two measurements confirms the reliability of the technique. Finally, measurements are discussed, emphasizing the contribution they provide to the machining community.
Learning Time-Varying Graphs From Online Data

This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems. This is possible by phrasing (time-varying) graph learning as a composite optimization problem, where different functions regulate different desiderata, e.g., data fidelity, sparsity or smoothness. Instrumental for the findings is recognizing that the dependence of the majority (if not all) data-driven graph learning algorithms on the data is exerted through the empirical covariance matrix, representing a sufficient statistic for the estimation problem. Its user-defined recursive update enables the framework to work in non-stationary environments, while iterative algorithms building on novel time-varying optimization tools explicitly take into account the temporal dynamics, speeding up convergence and implicitly including a temporal-regularization of the solution. We specialize the framework to three well-known graph learning models, namely, the Gaussian graphical model (GGM), the structural equation model (SEM), and the smoothness-based model (SBM), where we also introduce ad-hoc vectorization schemes for structured matrices (symmetric, hollows, etc.) which are crucial to perform correct gradient computations, other than enabling to work in low-dimensional vector spaces and hence easing storage requirements. After discussing the theoretical guarantees of the proposed framework, we corroborate it with extensive numerical tests in synthetic and real data.
On the Exploitation of 5G Multi-Access Edge Computing for Spatial Audio in Cultural Heritage Applications

This work presents a service for the improvements of cultural heritage experiences, which exploits the advantages coming from the 5G paradigm. Indeed, in a scenario where many users need to be served by a real-time solution which is in turn required to work on different devices, the potentialities of 5G technology show their suitability. In particular, moving the computation to the edge of the network ensures the availability of resources needed for binaural spatial audio rendering in an independent fashion w.r.t. the client device and at the same time it guarantees real-time availability of this data since the core network, with its impairments, is not involved. This work demonstrates how 5G could be a critical enabler for delivering low latency services at guaranteed levels, data-centric services, differentiated customer experiences, improved security and reduced costs to the users.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

Masked language models (MLMs) such as BERT have revolutionized the field of Natural Language Understanding in the past few years.However, existing pre-trained MLMs often output an anisotropic distribution of token representations that occupies a narrow subset of the entire representation space.Such token representations are not ideal, especially for tasks that demand discriminative semantic meanings of distinct tokens.In this work, we propose TaCL (Token-aware Contrastive Learning), a novel continual pre-training approach that encourages BERT to learn an isotropic and discriminative distribution of token representations.TaCL is fully unsupervised and requires no additional data.We extensively test our approach on a wide range of English and Chinese benchmarks.The results show that TaCL brings consistent and notable improvements over the original BERT model.Furthermore, we conduct detailed analysis to reveal the merits and inner-workings of our approach.
ANN-Enhanced IoT Wristband for Recognition of Player Identity and Shot Types Based on Basketball Shooting Motion Analysis

An IoT wristband for basketball shooting analysis, which can provide quantitative shooting guidance for basketball players in a convenient, low-cost manner, was developed. A micro inertial measurement unit (IMU) sensor-based wristband was used to collect the shooting motion data of 20 basketball players with different levels of skills: 15 amateur players and 5 elite players. The wristband was 247 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}\times {}20$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}\times {}11$ </tex-math></inline-formula> mm in size, weighed 12.3 g, and consisted of a power system, a microcontroller, and an IMU sensor (MPU-9250), which can acquire the triaxial data of acceleration, angular velocity, and magnetic field data, and then transmit them to a computing platform via Bluetooth. After correlation analysis, the triaxial data of accelerations and angular velocities were used to identify these 18 shooting movements. Experimental results showed that the four categories of shooting movements (i.e., set shots, layups, jump shots, and tip-ins) could be recognized with an accuracy of 98.0%. The overall recognition accuracy of 18 kinds of shooting movements reached 98.5%. The AI wristband could also be used to distinguish the identity of the participants with a recognition rate of 97.4%. This new technology can be applied to quantitative basketball shot-making guidance and has potential applications for analyzing the motions of other sports such as table tennis, racquetball, volleyball, and soccer.
Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice

Following the recent successful examples of large technology companies, many modern enterprises seek to build Knowledge Graphs to provide a unified view of corporate knowledge, and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modeling, and systems for reasoning with domain knowledge. In this paper, we demonstrate how to perform a broad spectrum of data science tasks in a unified Knowledge Graph environment. This includes data wrangling, complex logical and probabilistic reasoning, and machine learning. We base our work on the state-of-the-art Knowledge Graph Management System Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits such as the Jupyter platform. We argue that this is a significant step forward towards practical, holistic data science workflows that combine machine learning and reasoning in data science.
A Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble

In this article, we present a Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble in order to extract knowledge patterns about it. Our approach is general and can be applied to any past, present and future cryptocurrency speculative bubble. To verify its potential, we apply it to investigate the Ethereum speculative bubble happened in the years 2017 and 2018. We also describe several interesting knowledge patterns about the behaviour of specific categories of users that we obtained from this investigation. Furthermore, we describe how our approach can support the construction of an identikit of the speculators who maneuvered behind the Ethereum bubble analysed. Finally, we show that this capability of supporting the hunting for speculators is intrinsic of our approach and can cover past, present and future bubbles.