Reviewerly/exHarmony huggingface dataset:
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to predict sentiment of language text with the help of other modalities, such as vision and acoustic features.Previous studies focused on learning the joint representation of multiple modalities, ignoring some useful knowledge contained in language modal.In this paper, we try to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features.Our method consists of two components: shallow fusion part and aggregation part.For the shallow fusion part, we use crossmodal coattention mechanism to obtain bidirectional context information of each two modals to get the fused shallow representations.For the aggregation part, we design a multitask of sentimental words classification to help and guide the deep fusion of the three modalities and obtain the final sentimental words aware fusion representation.We carry out several experiments on CMU-MOSI, CMU-MOSEI and YouTube datasets.The experimental results show that introducing sentimental words prediction as a multitask can really improve the fusion representation of multiple modalities.
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

Although neural table-to-text models have achieved remarkable progress with the help of largescale datasets, they suffer insufficient learning problem with limited training data.Recently, pretrained language models show potential in few-shot learning with linguistic knowledge learnt from pretraining on large-scale corpus.However, benefiting table-to-text generation in few-shot setting with the powerful pretrained language model faces three challenges, including (1) the gap between the task's structured input and the natural language input for pretraining language model.(2) The lack of modeling for table structure and (3) improving text fidelity with less incorrect expressions that are contradicting to the table.To address aforementioned problems, we propose TableGPT for table-to-text generation.At first, we utilize table transformation module with template to rewrite structured table in natural language as input for GPT-2.In addition, we exploit multi-task learning with two auxiliary tasks that preserve table's structural information by reconstructing the structure from GPT-2's representation and improving the text's fidelity with content matching task aligning the table and information in the generated text.By experimenting on Humans, Songs and Books, three few-shot table-to-text datasets in different domains, our model outperforms existing systems on most few-shot settings.
Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm.
Towards optimal hydro-blasting in reconfigurable climbing system for corroded ship hull cleaning and maintenance

The operation of a ship in the ocean depends crucially on the quality of routine offshore dry dock maintenance. Automation by robotics is an efficient solution to address the issues of saving water, energy, time, and easing the labour workload when conducting hydro-blasting hulls in the dry dock ship maintenance industry. In this paper, the automated hydro-blasting in corroded ship hull cleaning by a novel robot platform with reconfigurable manipulators named Hornbill is proposed. The robot is able to maneuver smoothly on a vertical surface by permanent magnetic force, to carry the heavy load, to clean the corroded ship hull by hydro-blasting, and to self-evaluate hydro-blasting task by leveraging the Deep Convolutional Neural Network (DCNN) to synthesis the corrosion level map of the blasted workspace. We also propose an optimal complete waypoint path planning (CWPP) framework to help the robot re-blast the benchmarked workspace. The optimal CWPP problem, including objective functions of the shortest travel distance, the least upward moving direction to reduce water, energy spent while ensuring the visiting of the robot to all uncleaned waypoints defined by benchmarking output, is modeled as the classic Travel Salesman Problem (TSP). The evolutionary-based optimization techniques, including Genetic Algorithm (GA) and Ant Colony Optimization (ACO), are explored to derive the Pareto-optima solution for given TSP. The experimental results show that the magnetic force and motors torque are synchronized to enable the proposed system to navigate smoothly on the vertical surfaces tested with different corrosion levels. The proposed corrosion level benchmarking achieves a mean accuracy of 0.956 with an execution time of 30 fps. Besides, the proposed CWPP enables the proposed robot to yield about 15%, 26%, and 5% the energy, water, and time, respectively, less than the conventional methods when the experiments are conducted in various workspaces on the real ship hull.
Constructing a virtual two-qubit gate by sampling single-qubit operations

We show a certain kind of non-local operations can be simulated by sampling a set of local operations with a quasi-probability distribution when the task of a quantum circuit is to evaluate an expectation value of observables. Utilizing the result, we describe a strategy to decompose a two-qubit gate to a sequence of single-qubit operations. Required operations are projective measurement of a qubit in Pauli basis, and $\pi/2$ rotation around x, y, and z axes. The required number of sampling to get an expectation value of a target observable within an error of $\epsilon$ is roughly $O(9^k/\epsilon^2)$, where $k$ is the number of "cuts" performed. The proposed technique enables to perform "virtual" gates between a distant pair of qubits, where there is no direct interaction and thus a number of swap gates are inevitable otherwise. It can also be utilized to improve the simulation of a large quantum computer with a small-sized quantum device, which is an idea put forward by [Peng, et al., arXiv:1904.00102]. This work can enhance the connectivity of qubits on near-term, noisy quantum computers.
An Empirical Review of Automated Machine Learning

In recent years, Automated Machine Learning (AutoML) has become increasingly important in Computer Science due to the valuable potential it offers. This is testified by the high number of works published in the academic field and the significant efforts made in the industrial sector. However, some problems still need to be resolved. In this paper, we review some Machine Learning (ML) models and methods proposed in the literature to analyze their strengths and weaknesses. Then, we propose their use—alone or in combination with other approaches—to provide possible valid AutoML solutions. We analyze those solutions from a theoretical point of view and evaluate them empirically on three Atari games from the Arcade Learning Environment. Our goal is to identify what, we believe, could be some promising ways to create truly effective AutoML frameworks, therefore able to replace the human expert as much as possible, thereby making easier the process of applying ML approaches to typical problems of specific domains. We hope that the findings of our study will provide useful insights for future research work in AutoML.
Workflow Scheduling using Improved Moth Swarm Optimization Algorithm in Cloud Computing

Cloud computing is the advancing technology that aspires at providing services to customers with the available resources in the cloud environment.While the cloud server receives the multiple user requests, it is essential for the appropriate resources scheduling to accomplish appropriate customer fulfillment.Hence, this work exploits a developed Improved Moth Swarm Optimization (IMSO) method to carry out multi-objective task scheduling in the cloud environment.The developed method that schedules tasks based on a fitness parameter.The fitness parameter based upon the three most important constraints such as the Quality of Service (QoS), energy, and resources utilization.The developed approach schedules the task based on the three constraints like cost and task execution time in virtual machines is low.A competent task scheduling based upon the minimum fitness parameter.The comparative analysis exploiting the investigational setups shows that the developed model carries out fine while evaluated with the conventional techniques.
Assessing online education during the COVID-19 pandemic: a survey of lecturers in Ukraine

Abstract The winter and spring of 2020 was a challenging time worldwide. The COVID-19 pandemic seriously affected all spheres of life: from an industrial decline to educational transformations. In order to continue the 2019/2020 academic year, higher educational institutions had to adjust in-class learning to online. This paper aims at assessing the satisfaction and acceptance rate of Ukrainian lecturers with online education, as well as indicating problems and benefits they had singled out. The technical side of online education was also of interest. Therefore, the study employed an online survey to investigate technical support and tools used during the quarantine, advantages and disadvantages of online teaching, the level of satisfaction and acceptance. The findings from the study suggest that lecturers got different levels of technical support and used an assortment of tools to organise online education. The study revealed that the main advantage was time efficiency, while internet connection and technical problems were the most commonplace problems. Other findings of the survey are that lecturers were partially satisfied with online education but showed low acceptance of online education.
A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.
Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network

Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs. Most of the existing methods focus mainly on the extraction of spatio-temporal visual features without exploiting text or contextual information to further improve the recognition accuracy. Moreover, the ability of deep generative models to effectively model data distribution has not been investigated yet in the field of sign language recognition. To this end, a novel approach for context-aware continuous sign language recognition using a generative adversarial network architecture, named as Sign Language Recognition Generative Adversarial Network (SLRGAN), is introduced. The proposed network architecture consists of a generator that recognizes sign language glosses by extracting spatial and temporal features from video sequences, as well as a discriminator that evaluates the quality of the generator’s predictions by modeling text information at the sentence and gloss levels. The paper also investigates the importance of contextual information on sign language conversations for both Deaf-to-Deaf and Deaf-to-hearing communication. Contextual information, in the form of hidden states extracted from the previous sentence, is fed into the bidirectional long short-term memory module of the generator to improve the recognition accuracy of the network. At the final stage, sign language translation is performed by a transformer network, which converts sign language glosses to natural language text. Our proposed method achieved word error rates of 23.4%, 2.1% and 2.26% on the RWTH-Phoenix-Weather-2014 and the Chinese Sign Language (CSL) and Greek Sign Language (GSL) Signer Independent (SI) datasets, respectively.
2D-SCMCI Hyperchaotic Map for Image Encryption Algorithm

Chaos has unpredictability and initial condition sensitivity, which known as a best candidate for cryptography application.However, there are many defects for the existing chaotic cryptography systems due to the use of chaotic maps that without complexity dynamic properties.To overcome these weaknesses, this work designed a 2D-SCMCI hyperchaotic map based on Cascade Modulation Couple (CMC) and two 1Dchaotic map.The dynamic characteristics of the 2D-SCMCI hyperchaotic map are analyzed through attractor trajectory, 0-1 test, bifurcation diagram, Lyapunov exponents and spectrum entropy (SE) complexity.The results of analysis indicate that 2D-SCMCI hyperchaotic map has rich dynamic performance and randomness, which illustrates that it is more suitable for image encryption algorithm.Therefore, an image encryption algorithm is proposed by 2D-SCMCI hyperchaotic map.In encryption algorithm, the image is scrambled by row and column, forward and backward diffusion are used to diffuse image pixel values.The security performances analysis results indicate that the introduced algorithm has better security characteristics.
Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. Multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on few sample input texts to showcase the effectiveness and interpretability of our model.
System Immersion in Virtual Reality-Based Rehabilitation of Motor Function in Older Adults: A Systematic Review and Meta-Analysis

Background: As the elderly population continues to grow, so does the demand for new and innovative solutions to tackle age-related chronic diseases and disabilities. Virtual Reality (VR) has been explored as a novel therapeutic tool for numerous health-related applications. Although findings frequently favors VR, methodological shortcomings prevent clinical recommendations. Moreover, the term “VR” is frequently used ambiguously to describe e.g., video games; the distinction remains vague between immersive VR (IVR) systems and non-immersive VR (NVR). With no distinct demarcation, results of outcome measures are often pooled in meta-analyses, without accounting for the immersiveness of the system. Objective: This systematic review focused on virtual reality-based rehabilitation of older adults (+60) in motor rehabilitation programs. The review aims to retrospectively classify previous studies according to the level of immersion, in order to get an overview of the ambiguity-phenomenon, and to utilize meta-analyses and subgroup analyses to evaluate the comparative efficacy of system immersion in VR-based rehabilitation. Methods: Following PRISMA guidelines, we conducted a systematic search for randomized controlled trials, describing virtual rehabilitation or video games interventions for older adults (+60). Main outcomes were pain, motivation, mobility, balance, and adverse events. Results: We identified 15 studies which included 743 patients. Only three studies utilized IVR. The rest used various NVR-equipment ranging from commercial products (e.g., Nintendo Wii), to bespoke systems that combine tracking devices, software, and displays. A random effects meta-analysis of 10 studies analyzed outcome measures of mobility, balance, and pain. Protocols and dosage varied widely, but outcome results were in favor of immersive and non-immersive interventions, however, dropout rates and adverse events were mostly in favor of the control. Conclusions: We initialize a call-for-action, to distinguish between types of VR-technology and propose a taxonomy of virtual rehabilitation systems based on our findings. Most interventions use NVR-systems, which have demonstrably lower cybersickness-symptoms than IVR-systems. Therefore, adverse events may be under-reported in RCT-studies. An increased demand for IVR-systems highlight this challenge. Care should be given, when applying the results of existing NVR tools to new IVR-technologies. Future studies should provide more detail about their interventions, and future reviews should differentiate between NVR and IVR.
How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase

Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model-information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.
Dual Attention on Pyramid Feature Maps for Image Captioning

Generating natural sentences from images is a fundamental learning task for visual-semantic understanding in multimedia. In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences. Specifically, with the full consideration of the contextual information provided by the hidden state of the RNN controller, the pyramid attention can better localize the visually indicative and semantically consistent regions in images. On the other hand, the contextual information can help re-calibrate the importance of feature components by learning the channel-wise dependencies, to improve the discriminative power of visual features for better content description. We conducted comprehensive experiments on three well-known datasets: Flickr8K, Flickr30 K and MS COCO, which achieved impressive results in generating descriptive and smooth natural sentences from images. Using either convolution visual features or more informative bottom-up attention features, the composite model can boost the performance of image-to-sentence translation, with a limited computational resource overhead. The proposed pyramid attention and dual attention methods are highly modular, which can be inserted into various image captioning modules to further improve the performance.