Reviewerly/exHarmony huggingface dataset:
Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding

Twitter is the most popular microblogging platform, with millions of users exchanging daily a huge volume of text messages, called “tweets”. This has resulted in an enormous source of unstructured data, Big Data. Such a Big Data can be analyzed by companies or organizations with the purpose of extracting customer perspective about their products or services and monitoring marketing trends. Understanding automatically the opinions behind user-generated content, called “Big Data Analytics”, is of great concern. Deep learning can be used to make discriminative tasks of Big Data Analytics easier and with higher performance. Deep learning is an aspect of machine learning which refers to an artificial neural network with multiple layers and has been extensively used to address Big Data challenges, like semantic indexing, data tagging and immediate information retrieval. Deep learning requires its input to be represented as word embeddings, i.e. as a real-value vector in a high-dimensional space. However, word embedding models need large corpuses for training and presenting a reliable word vector. Thus, there are a number of pre-trained word embeddings freely available to leverage. In effect, these are words and their corresponding n-dimensional word vectors, made by different research teams. In this work, we have made data analysis with huge numbers of tweets taken as big data and thereby classifying their polarity using a deep learning approach with four notable pre-trained word vectors, namely Google’s Word2Vec, Stanford’s Crawl GloVe, Stanford’s Twitter GloVe, and Facebook’s FastText. One major conclusion is that tweet classification using deep learning outperforms the baseline machine learning algorithms. At the same time and with regard to pre-trained word embeddings, FastText provides more consistent results across datasets, while Twitter GloVe obtains very good accuracy rates despite its lower dimensionality.
No-reference image sharpness assessment based on discrepancy measures of structural degradation

The discrepancy between an image and its "reblurred" version indicates the extent of blur in the image. This paper presents a novel no-reference image sharpness evaluator leveraging the discrepancy measures of structural degradation in both the spatial and wavelet domains. Specifically, local structural degradation of an input image is characterized by the discrepancy measures of orientation selectivity-based visual patterns and log-Gabor filter responses between the image and its corresponding reblurred version respectively. Considering the influence of viewing distance on image quality, the global sharpness discrepancy is measured through inter-resolution self-similarities. Finally, the computed discrepancies are utilized as sharpness-aware features and then a support vector regressor is employed to map the feature vectors into quality scores. The performance of the proposed method is evaluated on six public image quality databases, including two real blurred image databases. Experimental results demonstrate that our proposed method achieves state-of-the-art performances across all these databases.
A Survey on 3D Hand Skeleton and Pose Estimation by Convolutional Neural Network

Restoring, estimating the fully 3D hand skeleton and pose from the image data of the captured sensors/cameras applied in many applications of computer vision and robotics: human-computer interaction; gesture recognition, interactive games, Computer-Aided Design (CAD), sign languages, action recognition, etc.These are applications that flourish in Virtual Reality and Augmented Reality (VR/AR) technologies.Previous survey studies focused on analyzing methods to solve the relational problems of hand estimation in the 2D and 3D space: Hand pose estimation, hand parsing, fingertip detection; List methods, data collection technologies, datasets of 3D hand pose estimation.In this paper, we surveyed studies in which Convolutional Neural Networks (CNNs) were used to estimate the 3D hand pose from data obtained from the cameras (e.g., RGB camera, depth(D) camera, RGB-D camera, stereo camera).The surveyed studies were divided based on the type of input data and publication time.The study discussed several areas of 3D hand pose estimation: (i)the number of valuable studies about 3D hand pose estimation, (ii) estimates of 3D hand pose when using 3D CNNs and 2D CNNs, (iii) challenges of the datasets collected from egocentric vision sensors, and (iv) methods used to collect and annotate datasets from egocentric vision sensors.The estimation process followed two directions: (a) using the 2D CNNs to predict 2D hand pose, and (b) using the 3D synthetic dataset (3D annotations/ ground truth) to regress 3D hand pose or using the 3D CNNs to predict the immediacy of 3D hand pose.Our survey focused on the CNN model/architecture, the datasets, the evaluation measurements, the results of 3D hand pose estimation on the available.Lastly, we also analyze some of the challenges of estimating 3D hand pose on the egocentric vision datasets.
Generically Speeding-Up Repeated Squaring Is Equivalent to Factoring: Sharp Thresholds for All Generic-Ring Delay Functions

Despite the fundamental importance of delay functions, repeated squaring in RSA groups (Rivest, Shamir and Wagner '96) is the only candidate offering both a useful structure and a realistic level of practicality. Somewhat unsatisfyingly, its sequentiality is provided directly by assumption (i.e., the function is assumed to be a delay function). We prove sharp thresholds on the sequentiality of all generic-ring delay functions relative to an RSA modulus based on the hardness of factoring in the standard model. In particular, we show that generically speeding-up repeated squaring (even with a preprocessing stage and any polynomial number parallel processors) is equivalent to factoring. More generally, based on the (essential) hardness of factoring, we prove that any generic-ring function is in fact a delay function, admitting a sharp sequentiality threshold that is determined by our notion of sequentiality depth. Moreover, we show that generic-ring functions admit not only sharp sequentiality thresholds, but also sharp pseudorandomness thresholds.
OCR System Framework for MODI Scripts using Data Augmentation and Convolutional Neural Network

Character recognition is one of the most active research areas in the field of pattern recognition and machine intelligence. It is a technique of recognizing either printed or handwritten text from document images and converting it to a machine-readable form. Even though there is much advancement in the field of character recognition using machine learning techniques, recognition of handwritten MODI script, which is an ancient Indian script, is still in its infancy. It is due to the complex nature of the script that includes similar shapes of character and the absence of demarcation between words. MODI was an official language used to write Marathi. Deep learning-based models are very efficient in character recognition tasks and in this work an ACNN model is proposed using the on-the-fly data augmentation method and convolution neural network. The augmentation of the data will add variability and generalization to the data set. CNN has special convolution and pooling layers which have helped in better feature extraction of the characters. The performance of the proposed method is compared with the most accurate MODI character recognition method reported so far and it is found that the proposed method outperforms the other method.
SPARCLE: Stream Processing Applications over Dispersed Computing Networks

In this paper, we propose SPARCLE, a novel scheduling system offering network-aware polynomial-time task assignment and resource allocation algorithms for stream processing applications in dispersed computing networks. In particular, we address two major challenges. The first one concerns the assignment of both computation and transport tasks comprising a stream processing application to computing nodes and communication links of the network, respectively, to maximize the application's processing rate. The second one concerns the resource allocation of multiple stream processing applications to satisfy their requested QoS. Our experimental results on a real image stream processing application and extensive simulations show that SPARCLE can increase the application's processing rate by 9 times and 3 times, compared to the cloud computing case and state-of-the-art algorithms, respectively.
Improvement of underwater colour correction using standard deviation ratio

This Letter presents a simple and new colour correction algorithm for the underwater image. The proposed method corrects the pixels of the weak colour channel based on the ratio of the standard deviation of the weak colour channel to that of the green channel. The corrected image based on the proposed colour balanced algorithm barely produces a reddish artefact and can be applied directly to the existing dehazing approaches without any processing. The experimental results show that the proposed algorithm achieves good colour correction performance, as well as reasonable underwater image enhancement when employed with the existing dehazing methods.
Establishment of the Complete Closed Mesh Model of Rail-Surface Scratch Data for Online Repair

Rail surface scratching occurs with increasing frequency, seriously threatening the safety of vehicles and humans. Online repair of rail-surface scratches on damaged rails with scratch depths >1 mm is of increased importance, because direct rail-replacement has the disadvantages of long operation time, high manpower and high material costs. Advanced online repair of rail-surface scratch using three-dimensional (3D) metal printing technology such as laser cladding has become an increasing trend, desperately demanding a solution for the fast and precise establishment of a complete closed mesh model of rail-surface scratch data. However, there have only been limited studies on the topic so far. In this paper, the complete closed mesh model is well established based on a novel triangulation algorithm relying on the topological features of the point-cloud model (PCM) of scratch-data, which is obtained by implementing a scratch-data-computation process following a rail-geometric-feature-fused algorithm of random sample consensus (RANSAC) performed on the full rail-surface PCM constructed by 3D laser vision. The proposed method is universal for all types of normal-speed rails in China. Experimental results show that the proposed method can accurately acquire the complete closed mesh models of scratch data of one meter of 50 Kg/m-rails within 1 min.
Performance Evaluation of a Patient-Centric Blockchain-based Healthcare Records Management Framework

Healthcare records management system has been revolutionized over the last decade aiming to provide accurate, efficient and enhanced patient care. The existing management system is either based on a client/server approach where each hospital maintains its own database or on a cloud approach where the health records are stored in a cloud server and managed by a third-party cloud service provider. However, these approaches suffer from the issues of security, privacy, data vulnerability and fragmentation. Furthermore, healthcare providers and patients are unable to have a unified view of a patient's medical history from all visited medical care centers. This results in additional treatment costs, repeated medical tests and increased time to diagnosis. The data traceability, immutability, transparency, replication, security and privacy traits of the emerging blockchain technology have a promising potential in the healthcare domain addressing these issues. In this paper, we propose BlockHR, a patient-centric healthcare records management system for efficient medical care at an optimal cost. The system enables healthcare providers to enter the patients' medical record data to the blockchain network and allows patients to enter their social data such as sleeping habits, physical activities, and current location. Consequently, BlockHR provides support to doctors for better diagnosis and prognosis. We evaluate the performance of BlockHR in terms of execution time and the total amount of data transferred for ledger update with an increasing number of hospitals and blocks in the network.
PMSM Vector Control Based on Fuzzy PID Controller

Abstract The traditional motor speed control method is to use PID controller to adjust it, but the accuracy and anti-interference ability of its control effect are difficult to be guaranteed. Because the side effects of its error selection, “weighted sum” strategy may not be the best and integral feedback. In the case of motor load variation, this paper presents a control method based on Fuzzy PID controller. The simulation model of PMSM (permanent-magnet synchronous motor) is built in Matlab / Simulink. The simulation experiment shows that the fuzzy PID controller has higher accuracy and anti-interference ability than the classical double closed-loop PID vector control in PMSM vector control.
Watermarking of HDR Images in the Spatial Domain With HVS-Imperceptibility

This paper presents a watermarking method in the spatial domain with HVS-imperceptibility for High Dynamic Range (HDR) images.The proposed method combines the content readability afforded by invisible watermarking with the visual ownership identification afforded by visible watermarking.The HVS-imperceptibility is guaranteed thanks to a Luma Variation Tolerance (LVT) curve, which is associated with the transfer function (TF) used for HDR encoding and provides the information needed to embed an imperceptible watermark in the spatial domain.The LVT curve is based on the inaccuracies between the non-linear digital representation of the linear luminance acquired by an HDR sensor and the brightness perceived by the Human Visual System (HVS) from the linear luminance displayed on an HDR screen.The embedded watermarks remain imperceptible to the HVS as long as the TF is not altered or the normal calibration and colorimetry conditions of the HDR screen remain unchanged.Extensive qualitative and quantitative evaluations on several HDR images encoded by two widely-used TFs confirm the strong HVSimperceptibility capabilities of the method, as well as the robustness of the embedded watermarks to tone mapping, lossy compression, and common signal processing operations.
2. Toward a Pedagogy of Responsive Attunement for Higher Education

&lt;?page nr="21"?&gt;Abstract In a context of “fake news” and associated disillusionment, this article explores what it means to nurture a capacity to concern ourselves with what matters for our living, among both students and teachers in higher education. In addressing this issue, I highlight responding through attunement to particularities of importance. A contribution of this article* lies in proposing that we direct student learning toward responsive attuning to what matters in the fields or professions to which they aspire, providing renewed direction for pedagogy and curricula. Enhancing such a capacity, moreover, has the potential to broaden the scope of possible ways of being in, and directed to, the communities to which these aspiring professionals belong. An additional contribution of the article is in exploring what responsive attunement can mean for higher education settings, both as a focus for learning by students and a modus operandi for teachers.
Evidence-Based Blockchain: Findings from a Global Study of Blockchain Projects and Start-up Companies

Evidence-based applications of resources remain one of the greatest challenges faced by governments, businesses, and policymakers. The United States Government Accountability Office (GAO) evaluated ten large programs, which together cost more than $10 billion/year, through randomised control trials – the highest standard of evidence-based practice (EBP). The evaluation found that nine of them had ‘weak or no positive effects’ on their participants. Many programs were not evaluated at all. In January 2019, U.S. President signed the ‘Foundations for Evidence-based Policy Making Act’ into law. A USAID (US Agency for International Development) study looked at 43 blockchain projects and companies claiming to have solved various problems using distributed ledgers. The study found that almost no company was willing to share their results and MERL (monitoring, evaluation, research and learning) processes. Other observational data revealed that 80–90% of blockchain-based token offering projects failed to deliver on their promises, a prediction also made by Vitalik Buterin, the founder of Ethereum blockchain, in 2017. The concept of evidence-based blockchain (EBB) was first introduced by Naqvi in 2018. We conducted an evaluation of 517 blockchain firms against PCIO framework of evidence-based practice: Problem – Comparison – Intervention and Outcomes. We define the fundamentals of EBB (Ask, Acquire, Appraise, Apply, Assess), provide a review of the literature on EBB, report findings of our study and propose an Assessment Framework of Evidence Based Blockchain.
Analysis of Algorithms for User Authentication using Keystroke Dynamics

In the present scenario, security is the biggest concern in any domain of applications. The latest and widely used system for user authentication is a biometric system. This includes fingerprint recognition, retina recognition, and voice recognition. But these systems can be bypassed by masqueraders. To avoid this, a combination of these systems is used which becomes very costly. To overcome these two drawbacks keystroke dynamics were introduced in this field. Keystroke dynamics is a biometric authentication-based system on behavior, which is an automated method in which the identity of an individual is identified and confirmed based on the way and the rhythm of passwords typed on a keyboard by the individual. The work in this paper focuses on identifying the best algorithm for implementing an authentication system with the help of machine learning for user identification based on keystroke dynamics. Our proposed model which uses XGBoost gives a comparatively higher accuracy of 93.59% than the other algorithms for the dataset used.
Method Engineering as Design Science

In this paper, we motivate, devise, demonstrate, and evaluate an approach for the research-based development of information systems development methods (ISDMs). This approach, termed “method engineering as design science” (ME-DS), emerged from the identified need for scholars to develop ISDMs using proper research methods that meet the standards of both rigor and relevance. ISDMs occupy a position of central importance to information systems development and scholars have therefore invested extensive resources over the years in developing such methods. The method engineering (ME) discipline has developed different frameworks and methods to guide such development work and, for that purpose, they are well-suited. Still, there remains a need for applications and evaluations of ISDMs based on the demands for knowledge justification. Unfortunately, in many cases, scholars come up short with regard to how ISDMs are generated and empirically validated. While design science (DS) stresses knowledge justification, prominent DS approaches seem to be biased toward the development of IT artifacts, making this approach ill-suited for the development of method artifacts. We therefore propose eight principles that marry ME and DS, resulting in a process model with six activities to support research-based development of ISDMs. We demonstrate and evaluate ME-DS by assessing three existing research papers that propose ISDMs. These retrospectives show how ME-DS directs attention to certain aspects of the research process and provides support for future ISDM development.
Multilingual and Multitarget Hate Speech Detection in Tweets

Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated. This paper proposes a supervised approach to hate speech detection from a multilingual perspective. We focus in particular on hateful messages towards two different targets (immigrants and women) in English tweets, as well as sexist messages in both English and French. Several models have been developed ranging from feature-engineering approaches to neural ones. Our experiments show very encouraging results on both languages.
DataStates: Towards Lightweight Data Models for Deep Learning

A key emerging pattern in deep learning applications is the need to capture intermediate DNN model snapshots and preserve or clone them in explore a large number of alternative training and/or inference paths. However, with increasing model complexity and new training approaches that mix data, model, pipeline and layer-wise parallelism, this pattern is challenging to address in a scalable and efficient manner. To this end, this position paper advocates for rethinking how to represent and manipulate DNN learning models. It relies on a broader notion of data states, a collection of annotated, potentially distributed data sets (tensors in the case of DNN models) that AI applications can capture at key moments during the runtime and revisit/reuse later. Instead explicitly interacting with the storage layer (e.g., write to a file), users can “tag” DNN models at key moments during runtime with metadata that expresses attributes and persistency/movement semantics. A high-performance runtime is the responsible to interpret the metadata and perform the necessary actions in the background, while offering a rich interface to find data states of interest. Using this approach has benefits at several levels: new capabilities, performance portability, high performance and scalability.
Rotation-Invariant Feature Learning in VHR Optical Remote Sensing Images via Nested Siamese Structure With Double Center Loss

Rotation-invariant features are of great importance for object detection and image classification in very-high-resolution (VHR) optical remote sensing images. Though multibranch convolutional neural network (mCNN) has been demonstrated to be very effective for rotation-invariant feature learning, how to effectively train such a network is still an open problem. In this article, a nested Siamese structure (NSS) is proposed for training the mCNN to learn effective rotation-invariant features, which consists of an inner Siamese structure to enhance intraclass cohesion and an outer Siamese structure to enlarge interclass margin. Moreover, a double center loss (DCL) function, in which training samples from the same class are mapped closer to each other while those from different classes are mapped far away to each other, is proposed to train the proposed NSS even with a small amount of training samples. Experimental results over three benchmark data sets demonstrate that the proposed NSS trained by DCL is very effective to encounter rotation varieties when learning features for image classification and outperforms several state-of-the-art rotation-invariant feature learning algorithms even when a small amount of training samples are available.
On the Monotonicity of Smooth Fuzzy Systems

This article addresses the monotonicity property of the smooth fuzzy systems (SFSs). We have derived the sufficient conditions for the monotonicity of SFS and could show that, since the smooth fuzzy compositions are differentiable to the membership functions (MFs), irrespective to the shape of the membership function, one can obtain the monotonic SFS by the proper selection of the inference scheme and the consequent parts. This gives more flexibility that the MFs can have discontinuity in the universe of discourse or switch their values and the SFS still keep to be monotonic. This property is useful for the control and modeling of switched and hybrid dynamic systems.