Reviewerly/exHarmony huggingface dataset:
Sequential spatially balanced sampling

Abstract Sequential sampling occurs when an entire population is unknown in advance and data are received one by one or in groups of units. This article proposes a new algorithm to sequentially select a balanced sample. The algorithm respects equal and unequal inclusion probabilities. The method can also be used to select a spatially balanced sample if the population of interest contains spatial coordinates. A simulation study is proposed, and the results show that the proposed method outperforms other methods.
Research on Time-frequency Energy Distribution Characteristics of PSWFs Signals Based on WVD

The Prolate Spheroidal Wave Functions signal is a kind of non-sinusoidal signal waveform with great application potential, and its basic characteristics, especially the time-frequency energy distribution characteristics, are the premise of time-frequency detection. With the help of WVD, this paper first analyzes the time-frequency energy density distribution of PSWFs signals, and then analyzes the time-frequency energy concentration degree of PSWFs signals according to the time and frequency edge properties of WVD. Relevant conclusions complete the research work on the time-frequency characteristics of PSWFs signals, and can provide good theoretical support for applications such as time-frequency detection of PSWFs signals.
Influence of Artificial Intelligence Technology on Animation Creation

Abstract The advent of the era of artificial intelligence has brought unprecedentedly technological changes and breakthroughs to animation creation, which makes animation art creation gradually move towards the field of intelligence, and avoid the cumbersome and intensive work mode, thereby making the creation focus on the creative innovation. This paper deeply discusses the relationship between artificial intelligence and animation creation, and clarifies the advantages of artificial intelligence in improving the efficiency of animation creation. At the same time, it also analyses that the root of animation creation in this period is still human nature, so the creator should create animation based on human nature itself.
The Representation of Syrian Refugees in Canadian Online News Media: A Focus on the Topos of Burdening

Background: This study focuses on the representation of Syrian refugees in Canadian online news media. It examines 375 articles selected from three of the most visited Canadian news sites, namely the Toronto Star, which favours the Liberal Party, and the Toronto Sun and National Post, which favour the Conservative Party. Analysis: The basis of this research is a topoi analysis, whereby instances of the topos of burdening are identified, examined, and categorized as either positive or negative. Conclusion and implications: A distinction is drawn between the depiction of Syrian refugees in conservative- and liberal-leaning news sources. The findings aim to provide some insight into the possible impact of media representation on both the Syrian refugees and the Canadian public.
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration

Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices. However, prior pruning schemes have limited application scenarios due to accuracy degradation, difficulty in leveraging hardware acceleration, and/or restriction on certain types of DNN layers. In this article, we propose a general, fine-grained structured pruning scheme and corresponding compiler optimizations that are applicable to any type of DNN layer while achieving high accuracy and hardware inference performance. With the flexibility of applying different pruning schemes to different layers enabled by our compiler optimizations, we further probe into the new problem of determining the best-suited pruning scheme considering the different acceleration and accuracy performance of various pruning schemes. Two pruning scheme mapping methods—one -search based and the other is rule based—are proposed to automatically derive the best-suited pruning regularity and block size for each layer of any given DNN. Experimental results demonstrate that our pruning scheme mapping methods, together with the general fine-grained structured pruning scheme, outperform the state-of-the-art DNN optimization framework with up to 2.48 \( \times \) and 1.73 \( \times \) DNN inference acceleration on CIFAR-10 and ImageNet datasets without accuracy loss.
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression

Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able to operate reliably. Besides varying amounts of non-IID data at participants, the asynchronous setting models heterogeneous client participation due to available computational power and battery constraints and also accounts for delayed communications between clients and the server. To reduce the communication overhead associated with asynchronous online federated learning (ASO-Fed), we use the principles of partial-sharing-based communication. In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible. We prove the convergence of the proposed ASO-Fed and provide simulations to analyze its behavior further. The simulations reveal that, in the asynchronous setting, it is possible to achieve the same convergence as the federated stochastic gradient (Online-FedSGD) while reducing the communication tenfold.
Effective resource scheduling using hybrid gradient descent cuckoo search algorithm and security enhancement in cloud via blockchain for healthcare 4.0

The Modern Healthcare System (MHS) has become a revolutionized domain over the past decade and has extemporized from version 1.0 to 4.0 with an amalgamation of IoT and its allied technologies. The record sustaining in the MHS is the superlative task in Healthcare 4.0 and the need for maintaining the integrity and resource availability congregate challenges like cyber attacks, lack of resources, etc. However, these challenges can be subdued through augmented resource sharing and blockchain technology. Blockchain technology has proven to be more promising in preserving data security and maintaining the integrity of sensitive data. The conventional healthcare system is slowly cruising towards the modern healthcare domain for the ease of process by the users and for adopting rapid sharing and accessing of healthcare data by the authorized users. Cloud computing offers a major role in modernizing the conventional healthcare system as the patient’s sensitive data were shared and stored in the cloud resources so that to access across the world. Even with the preoccupation of technology like cloud computing, there exists certain challenge like resource availability and data security and to address the aforementioned challenges this paper introduces an integrated model of Hybrid Gradient Decent Cuckoo Search (HGDCS) algorithm for effective resource sharing and Blockchain technology for affording inherent security and high level of integrity for the sensitive data.
Improving Readability of Scratch Programs with Search-based Refactoring

Block-based programming languages like SCRATCH have become increasingly popular as introductory languages for novices. These languages are intended to be used with a “tinkering” approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, SCRATCH lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve SCRATCH code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of SCRATCH programs. By automating these transformations it is possible to explore the space of possible variations of SCRATCH programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given SCRATCH program and therefore form refactorings. Evaluation on a random sample of 1000 SCRATCH programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4% of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.
Distilling Contextual Embeddings Into A Static Word Embedding For Improving Hacker Forum Analytics

Hacker forums provide malicious actors with a large database of tutorials, goods, and assets to leverage for cyber-attacks. Careful research of these forums can provide tremendous benefit to the cybersecurity community through trend identification and exploit categorization. This study aims to provide a novel static word embedding, Hack2Vec, to improve performance on hacker forum classification tasks. Our proposed Hack2Vec model distills contextual representations from the seminal pre-trained language model BERT to a continuous bag-of-words model to create a highly targeted hacker forum static word embedding. The results of our experimental design indicate that Hack2Vec improves performance over prominent embeddings in accuracy, precision, recall, and F1-score for a benchmark hacker forum classification task.
Generation of Music With Dynamics Using Deep Convolutional Generative Adversarial Network

Following the rapid advancement of Artificial Intelligence and transition into the era of Big Data, researchers have started to explore the possibility of using machine learning in creative domains such as music generation. However, most research were focused on musical composition and removed expressive attributes during data pre-processing, which resulted in mechanical-sounding generated music. To address this issue, music elements, such as pitch, time and velocity, were extracted from MIDI tracks and encoded with piano-roll data representation. With the piano-roll data representation, Deep Convolutional Generative Adversarial Network (DCGAN) learned the data distribution from the given dataset and generated new data derived from the same distribution. The generated music was evaluated based on its incorporation of music dynamics and a user study. The evaluation results verified that DCGAN could generate expressive music comprising of music dynamics and syncopated rhythm.
A frequency-aware management strategy for virtual machines in DVFS-enabled clouds

It is demanding to curtail energy consumption of virtual-machine-powered data centers, because modern data centers have been significantly scaling up in capacity in past decades. In this study, we propose a frequency-aware management strategy, which controls dynamic power and static power of processors running virtual machines in data centers. Unlike existing dynamic voltage and frequency scaling schemes, our strategy simply incorporates frequency requirements rather than task execution times. This salient feature is practical, because task execution times in a raft of real-world applications are unknown in a priori. We build a frequency-aware model, which is adept at deriving an optimal frequency ratio that minimizes processors’ energy consumption. With our model in place, the energy efficiency of a data center can be maximized by adjusting the processor's frequency to meet the optimal frequency ratio. We design a management approach to judiciously adjust frequency ratio to conserve energy without violating the frequency requirements imposed by virtual machines. After analyzing the correlations between frequency ratio and energy consumption, we show that a small static power proportion gives rise to high energy-saving performance. The results demonstrate that our model lays out a solid theoretical foundation catering to the development of power management software in DVFS-enabled clouds.
Anomaly detection in streaming data with gaussian process based stochastic differential equations

This paper characterises streaming data as the evolution of a stochastic differential equation, with the aim of extracting information that can be used to detect anomalies in the stream. Gaussian process regression provides a flexible approach to approximating components of the stochastic differential equation, allowing for complex modelling of underlying data generation dynamics. The proposed algorithm displays superior discriminative power over different time-series anomaly detection methods for both synthetic and NYC taxi datasets, whilst the introduced bootstrapping method for setting the detection threshold provides control over the false alarm rate of the anomaly detector.
Application of QR Code Online Testing Technology in Nursing Teaching in Colleges and Universities

In view of the current situation that the nursing teaching content is lacking pertinence and cannot effectively cultivate students’ critical thinking, this paper puts forward the application analysis of two-dimensional code online testing technology in nursing teaching in colleges and universities. Through the analysis of two-dimensional code recognition technology architecture and common application fields, an intelligent nursing teaching platform for two-dimensional code online testing technology was designed. Systematically, we summarize the advantages of intelligent nursing teaching platform, use flash and 3D technology to complete the virtualization of nursing teaching scene, and use XML technology to update and save teaching resources; we further build an intelligent nursing teaching platform by using the basic plate, nursing station plate, ward plate, dispensing room plate, and case and operation review plate. The experimental results show that under the two-dimensional code online test technology, the nursing teaching method in colleges and universities has strong teaching resource processing efficiency, improves the operation level of students’ nursing technology, and effectively cultivates the academic thinking of nursing students, which is of great significance to the progress of nursing teaching.
Memes, Emojis, and Text: The Semiotics of Differentiation in Sri Lankan Tamil Digital Publics

This article draws on Judith T. Irvine’s theorizing of the semiotic processes of differentiation to investigate how Sri Lankan Tamils and Muslims configure similarity and difference in multimodal social media interactions. I analyze Facebook discussions around memes of Tamil‐language blunders in trilingual public signs, which are widely taken to represent the incomplete implementation of Tamil as a co‐official language. Insider status in groups is not contingent on code use, but on expressing particular alignments toward the memes as tokens of a type. By virtue of their metapragmatic ambiguity, emojis are powerful in enabling participants to create shared affective stances around the memes, but they are also useful in demarcating difference between Tamil speakers and Sinhalas. I contribute to studies of social media communication by examining how different linguistic and non‐linguistic forms of expression are used to delineate transnational Tamil digital publics.
Review on Service Composition

The increasing number of cloud services, Web services, and IoT services can meet the functional and nonfunctional needs of users, which has motivated researchers to provide more efficient ways to combine available services. Service composition is to organize a certain number of existing services to form a service set with comprehensive functions for meeting some users' needs. Our goal is to summarize the progress of research in service composition in the last 15 years. In this paper, we classify service compositions into three categories: Cloud Service Compositions, Web Service Compositions and IoT Service Compositions, and categorize the literature on existing composition approaches in these three categories according to the year and concrete approaches, and finally, we also discuss future research trends and challenges for these three categories of service compositions.
Improved Spam Email Filtering Architecture Using Several Feature Extraction Techniques

Research on spam email filtering is drawing experts from all over the world, as these junk email messages continue to affect people’s daily lives, whether consciously or unconsciously. The overwhelming use of irritating, destructive, and misleading emails appears to have damaged the values of email which prompted us to perform this research to construct a model for spam filtering with faster training time and enhanced accuracy. We have proposed two voting architectures built upon machine learning models and ensemble classifiers, respectively. In our work, we have also analyzed the performance of several individually applied classifiers and ensemble techniques with various feature retrieval strategies. Additionally, we have compared the training time of the proposed models with the deep LSTM-CNN hybrid model. Both of our suggested models have performed adequately, while the ML-based voting model (Type 1) produces the most accurate filtering (98%) taking bag of words for feature extraction and can be trained above 200 times faster than the LSTM-CNN model.
Effective document image binarization via a convex variational level set model

Document image binarization is a significant stage in the optical character recognition system. Different from previous binarization approaches, in this paper, we propose a novel and convex variational level set model for document image binarization. Our energy functional is comprised of two terms: data term and fidelity term. We prove that our model is strictly convex and has a unique global minimum solution, which enables us to set the evolution termination criterion by the normalized step difference energy that measures the convergence state of level set function. We experimentally demonstrate the advantage of the fidelity term and show the merit of level set initialization robustness. In addition, the convergence of the alternative minimization algorithm for solving our model is analyzed. Extensive experiments are conducted on JM dataset, representative degraded document images and DIBCO series datasets to evaluate our model qualitatively and quantitatively. The experiment results verify that our model is effective to deal with most kinds of degraded images. Compared with four evolution and six non-evolution based binarization methods, our model could achieve better or competitive performance in terms of four metrics.
Assessing Automatic Approach-Avoidance Behavior in an Immersive Virtual Environment

The use of virtual reality (VR) promises enormous potential for studying human behavior. While approach and avoidance tendencies have been explored in various areas of basic and applied psychology, such as attitude and emotion research, basic learning psychology, and behavior therapy, they have rarely been studied in VR. One major focus of this research is to understand the psychological mechanisms underlying automatic behavioral tendencies towards and away from positively or negatively evaluated stimuli. We implemented a whole-body movement stimulus-response compatibility task to explore approach-avoidance behavior in an immersive virtual environment. We chose attitudinal stimuli—spiders and butterflies—on which people widely agree in their general evaluations (in that people evaluate spiders negatively and butterflies positively), while there is still substantial inter-individual variance (i. e., the intensity in which people dislike spiders or like butterflies). We implemented two parallel approach-avoidance tasks, one in VR, one desktop-based. Both tasks revealed the expected compatibility effects that were positively intercorrelated. Interestingly, however, the compatibility effect in the VR measure was unrelated to participants’ self-reported fear of spiders and stimulus evaluations. These results raise important implications about the usage of VR to study automatic behavioral tendencies.
Chart classification

Charts are powerful tools for visualizing and comparing data. Representation of information through charts grows with time due to its easy and aesthetically attractive structure. With the increase in the number of documents with various chart types, chart classification has become an important task for downstream applications such as chart data recovery, chart replenishment, etc. Though there have been various studies reported in the literature on chart classification using different classification methods, three of the important concerns are small dataset size, a small number of chart types, and inconsistencies in the performance reported in different studies. Motivated by the above concerns, this paper curates a large dataset of real chart images (110k samples) with a large number of chart types (24 charts types) and evaluates 21 different machine learning models. To the best of our knowledge, this is the largest (in sample size and chart types) real chart dataset reported in the literature to date. We further study - (i) the effect of dataset size on the classification model, (ii) the nature of chart noises and their influences on classification performance, and (iii) confusing chart pairs leading to misclassification.
A Novel Design of Mobile Robotic System for Opening and Transitioning Through a Watertight Ship Door

Recent offshore drilling activities have dramatically bloomed oil and gas production. Due to extreme weather, such as hurricanes and tsunamis, offshore oil platforms may need to be constantly monitored in case of unexpected dangers. Using robots to monitor and prevent these dangerous situations is a cost-effective and safer solution compared to any human involvement. However, one major drawback for robotic intervention is the ability of the robot to operate water tight ship doors designed for marina facilities. Current studies have been focused on robots manipulating normal doors in people's daily life. However their presented methodology may not be suitable for opening heavy watertight ship doors since they have a special locking structure which needs much larger torque to open. This study explores the possibility of opening heavy watertight ship doors with an ordinary mobile robot under remote control. Given the watertight ship door’s unique structure, a door opening tool with a pawl mechanism is designed for a mobile robot, which consists of a robotic arm and a wheeled platform, to complete the task. Experimental results have shown that the mobile robot can operate the tool to open the door and transition through the door.