Reviewerly/exHarmony huggingface dataset:
Flow Analysis and Relative Humidity (RH) Measurement in the Horizontal Plane of a Front Opening Unified Pod (FOUP)

Moisture removal techniques such as purge have been developed for the front opening unified pod (FOUP) in the semiconductor industry. The FOUP purge can significantly increase the quality of integrated circuit (IC) products. An experimental model of a simulated clean room, was designed at Taipei Tech. A mini environment (ME) and a FOUP were installed inside the model. Smoke flow visualization technique was performed with the assistance of an innovative laser light sheet generation system. A homemade smoke generator simulates the moist air flow pattern into the FOUP. The FOUP top view flow pattern was recorded by a Hamamatsu digital camera. The particle image velocimetry (PIV) in the FOUP horizontal plane was analyzed. Moreover, the relative humidity (RH) was monitored inside the FOUP. The results show that a purge flow rate of 200 LPM is the optimum purge flow rate for moisture removal performance inside the FOUP. The findings of the present study could significantly increase the quality of chip products in semiconductor manufacturing, decreasing product defect rates and significantly reducing energy consumption.
Deep Learning on Graphs for Natural Language Processing

This tutorial of Deep Learning on Graphs for Natural Language Processing (DLG4NLP) will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing). In addition, a handson demonstration session will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library - Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.
Sentiment Analysis of Online Product Reviews Based On SenBERT-CNN

Sentiment analysis, also known as opinion mining, is an important area of research to analyze people's opinions. In online e-commerce marketplace like Taobao, customers are allowed to comment on different products, brands and services using text and numerical ratings. Such reviews towards a product are valuable for the improvement of the product quality as they influence consumers' purchase decisions. In this paper, we introduce a novel model, SenBERT-CNN, to analyze customer's review. In order to capture more sentiment information in sentences, SenBERT-CNN model combines a pre-trained Bidirectional Encoder Representations from Transformers (BERT) network with Convolutional Neural Network (CNN). Specifically, we use BERT structure to better express sentence semantics as a text vector, and then further extract the deep features of the sentence through a Convolutional Neural Network. The effectiveness of the proposed method is validated through a collected product reviews of mobile phone from the e-commerce website, JD.com.
Federated Learning toward Data Preprocessing: COVID-19 Context

During this last decade, in the digital era, online and real-time data management becomes essential and primordial in several scenarios. In the health domain, and especially for remote healthcare monitoring systems, the management of data in real time becomes a requirement. Indeed, care providers need to access the vital information and sometimes the processed data in order to take timely the adequate decision. However, many issues arise in the data processing regarding real-time aspect, computational complexity and patient mobility. Therefore, Federated Learning (FL) engages promising technologies such as the fog and the cloud computing as well as machine learning (ML) and deep learning (DL) to address the aforementioned issues. Fog computing enables data preprocessing in proximity of medical users, exploiting patients mobile phones or personal digital assistant (PDA) or small-scale distributed servers. In this paper, we pinpoint the important role of the FL-based system within Internet of Medical Things (IoMT) to combat COVID-19 pandemic. Specifically, we first introduce the architecture highlighting the fog layer within the smart healthcare system. We then discuss the preprocesing tasks that could be implemented at the fog layer with a particular focus on ML and DL tasks. After that, an investigation related to the FL against several COVID-19 contexts is provided. Finally, this paper explores open issues and future directions regarding the FL potentialities in pandemic situation.
A novel fractional‐order reaction diffusion system for the multiplicative noise removal

Abstract In this paper, a fractional‐order nonlinear reaction diffusion system is proposed to remove the multiplicative Gamma noise. The new reaction diffusion system consists of three equations: the regularized Perona and Malik (PM) equation , which is used for presmoothing the image that is contaminated by noise; the time‐delay regularization equation, which is used for incorporating the past information into the diffusion process and adjusting oversmoothing; and the fractional‐order diffusion equation, which is used for removing the multiplicative Gamma noise and maintaining texture. The new reaction diffusion system is coupled, leading to the difficulty in theoretical analysis. To this end, we use decoupled and Schauder's fixed‐point theorem to obtain the existence and uniqueness of weak solution of the system. The explicit finite difference scheme is employed to implement the fractional‐order nonlinear reaction diffusion system. In addition, we test both texture images and nontexture images. Experimental results show that the new model achieves a better trade‐off between denoising performance and texture preservation than the other three models.
Hierarchical Spatiotemporal Graph Regularized Discriminative Correlation Filter for Visual Object Tracking

Visual object tracking is a fundamental and challenging task in many high-level vision and robotics applications. It is typically formulated by estimating the target appearance model between consecutive frames. Discriminative correlation filters (DCFs) and their variants have achieved promising speed and accuracy for visual tracking in many challenging scenarios. However, because of the unwanted boundary effects and lack of geometric constraints, these methods suffer from performance degradation. In the current work, we propose hierarchical spatiotemporal graph-regularized correlation filters for robust object tracking. The target sample is decomposed into a large number of deep channels, which are then used to construct a spatial graph such that each graph node corresponds to a particular target location across all channels. Such a graph effectively captures the spatial structure of the target object. In order to capture the temporal structure of the target object, the information in the deep channels obtained from a temporal window is compressed using the principal component analysis, and then, a temporal graph is constructed such that each graph node corresponds to a particular target location in the temporal dimension. Both spatial and temporal graphs span different subspaces such that the target and the background become linearly separable. The learned correlation filter is constrained to act as an eigenvector of the Laplacian of these spatiotemporal graphs. We propose a novel objective function that incorporates these spatiotemporal constraints into the DCFs framework. We solve the objective function using alternating direction methods of multipliers such that each subproblem has a closed-form solution. We evaluate our proposed algorithm on six challenging benchmark datasets and compare it with 33 existing state-of-the art trackers. Our results demonstrate an excellent performance of the proposed algorithm compared to the existing trackers.
A multimodal approach for regional GDP prediction using social media activity and historical information

This work proposes a multimodal approach with which to predict the regional Gross Domestic Product (GDP) by combining historical GDP values with the embodied information in Twitter messages concerning the current economic condition. This proposal is of great interest, since it delivers forecasts at higher frequencies than both the official statistics (published only annually at the regional level in Spain) and the existing unofficial quarterly predictions (which rely on economic indicators that are available only after months of delay). The proposed method is based on a two-stage architecture. In the first stage, a multi-task autoencoder is initially used to obtain a GDP-related representation of tweets, which are then filtered to remove outliers and to obtain the GDP prediction from the consensus of opinions. In a second stage, this result is combined with the historical GDP values of the region using a multimodal network. The method is evaluated in four different regions of Spain using the tweets written by the most relevant economists, politicians, newspapers and institutions in each one. The results show that our approach successfully learns the evolution of the GDP using only historical information and tweets, thus making it possible to provide earlier forecasts about the regional GDP. This method also makes it possible to establish which the most or least influential opinions regarding this prediction are. As an additional exercise, we have assessed how well our method predicted the effect of the COVID-19 pandemic.
Waypoint Planning Networks

With the recent advances in machine learning, path planning algorithms are also evolving; however, the learned path planning algorithms often have difficulty competing with success rates of classic algorithms. We propose waypoint planning networks (WPN), a hybrid algorithm based on LSTMs with a local kernel-a classic algorithm such as A*, and a global kernel using a learned algorithm. WPN produces a more computationally efficient and robust solution. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value iteration networks (VIN). In this paper, the design and experiments have been conducted for 2D environments. Experimental results outline the benefits of WPN, both in efficiency and generalization. It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results. Additionally, WPN works on partial maps, unlike A* which needs the full map in advance. The code is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
An investigation of IBM quantum computing device performance on combinatorial optimisation problems

The intractability of deterministic solutions in solving $$\mathcal {NP}$$ -Hard Combinatorial Optimisation Problems (COP) is well reported in the literature. One mechanism for overcoming this difficulty has been the use of efficient COP non-deterministic approaches. However, with the advent of quantum technology, these modern devices’ potential to overcome this tractability limitation requires exploration. This paper juxtaposes classical and quantum optimisation algorithms’ performance to solve two common COP, namely the Travelling Salesman Problem and the Quadratic Assignment Problem. Two accepted classical optimisation methods, Branch and Bound and Simulated Annealing, are compared to two quantum optimisation methods, Variational Quantum Eigensolver (VQE) algorithm and Quantum Approximate Optimisation Algorithm (QAOA). These algorithms are, respectively, executed on both classical devices and IBM’s suite of Noisy Intermediate-Scale Quantum (NISQ) devices. We have encoded the COP problems for the respective technologies and algorithms and provided the computational encodings for the NISQ devices. Our experimental results show that current classical devices significantly outperform the presently available NISQ devices, which both agree with and extend on those findings reported in the literature. Further, we introduce additional performance metrics to better compare the two approaches with respect to computational time, feasibility and solution quality. Our results show that the VQE performs better than QAOA with respect to these metrics, and we infer that this is due to the increased number of operations required. Additionally, we investigate the impact of a new set of basis gates on the quantum optimisation techniques and show they yield no notable improvement on obtained results. Finally, we highlight the present shortcomings of state-of-the-art NISQ IBM quantum devices and argue for continued future work on improving evolving devices.
Routed quantum circuits

We argue that the quantum-theoretical structures studied in several recent lines of research cannot be adequately described within the standard framework of quantum circuits. This is in particular the case whenever the combination of subsystems is described by a nontrivial blend of direct sums and tensor products of Hilbert spaces. We therefore propose an extension to the framework of quantum circuits, given by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">routed linear maps</mml:mtext></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">routed quantum circuits</mml:mtext></mml:mrow></mml:math>. We prove that this new framework allows for a consistent and intuitive diagrammatic representation in terms of circuit diagrams, applicable to both pure and mixed quantum theory, and exemplify its use in several situations, including the superposition of quantum channels and the causal decompositions of unitaries. We show that our framework encompasses the `extended circuit diagrams' of Lorenz and Barrett [arXiv:2001.07774 (2020)], which we derive as a special case, endowing them with a sound semantics.
Embedding Q-Learning in the selection of metaheuristic operators: The enhanced binary grey wolf optimizer case

In the different situations present in the industry, combinatorial problems are increasingly frequent. This paper presents the interaction of Metaheuristics and Machine Learning, specifically as Machine Learning can be a support to enhance Metaheuristics. The resolution of the Set Covering Problem is presented, using the Grey Wolf Optimizer and Sine Cosine Algorithm metaheuristics that have been improved by adding a Q-Learning technique for the selection of a Discretization Scheme, using two-steps, intelligently choosing which transfer function to use and which binarization technique to apply in each iteration. The results show a better result for the Grey Wolf Optimizer with Q-Learning configuration, compared to other configurations in the literature, obtaining a better balance between exploration and exploitation.
Blockchain-Engineers Wanted: an Empirical Analysis on Required Skills, Education and Experience

There is an indisputable industrial need for highly skilled individuals in the role of blockchain engineers. However, little is known about the typology of activities which fall under the blockchain engineers' responsibilities and the competencies and experiences sought from employers' perspectives. To shed light about the main characteristics of the new roles available in the market, we built and analyzed a dataset of 400 job ads related to software blockchain engineering and development roles in the U.S.. We performed a thorough analysis conducted to find the industrial demand for competencies related to the blockchain engineer role in terms of (i) level of education, training, and experience; (ii) blockchain-related development skills; (iii) technical skills; and (iv) soft-skills. Relevant correlated skills that may influence shaping the profile of the blockchain engineers were also analyzed. We conclude that a new “blockchain engineer” role is shaping up, and that specific skills, educational background, and experience are requested from the job market.
On the use of summarization and transformer architectures for profiling résumés

Profiling professional figures is becoming more and more crucial, as companies and recruiters face the challenges of Industry 4.0. On the one hand, demand for specific knowledge in professional figures is rising. On the other hand, workers try to broaden the spectrum of their skills in order to remain appealing in the job market. Therefore, research related to these topics is receiving more and more attention. In this paper, we propose a methodology to profile résumés based on summarization and transformer architectures for generating résumé embeddings and on hierarchical clustering algorithms for grouping these embeddings. We evaluate different strategies and show that our approach achieves promising results on a public domain dataset containing 1202 résumés.
Underwater image enhancement method based on color correction and three-interval histogram stretching

Since the attenuation and absorption of light in the water medium and the influence of the tiny suspended particles, underwater images are degraded and suffer from color cast and low contrast. An underwater image enhancement method based on color correction and three-interval histogram stretching is proposed to handle these issues. Firstly, a color correction method based on sub-interval linear transformation is designed to achieve color correction. Meanwhile, using the three adaptive sub-histograms equalization algorithm to enhance the contrast, and then the resulting images of the above-mentioned methods are combined by the multi-scale fusion. Final experiments demonstrate the proposed method can improve the image contrast, obtain the details of the bright and dark regions of the image, rich color, and finally output high-quality images.
Hierarchical Learning Framework for UAV Detection and Identification

The ubiquity of unmanned aerial vehicles (UAVs) or drones is posing both security and safety risks to the public as UAVs are now used for cybercrimes. To mitigate these risks, it is important to have a system that can detect or identify the presence of an intruding UAV in a restricted environment. In this work, we propose a radio frequency (RF) based UAV detection and identification system by exploiting signals emanating from both the UAV and its flight controller, respectively. While several RF devices (i.e., Bluetooth and WiFi) operate in the same frequency band as UAVs, the proposed framework utilizes a semi-supervised learning approach for the detection of UAV or UAV’s control signals in the presence of other wireless signals such as Bluetooth and WiFi. The semi-supervised learning approach uses stacked denoising autoencoder and local outlier factor algorithms. After the detection of UAV or UAV’s control signals, the signal is decomposed by using Hilbert-Huang transform and wavelet packet transform to extract features from the time-frequency-energy domain of the signal. The extracted feature sets are used to train a three-level hierarchical classifier for identifying the type of signals (i.e., UAV or UAV control signal), UAV models, and flight mode of UAV.
FoCL: Feature-oriented continual learning for generative models

In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL). Unlike previous works that aim to solve the catastrophic forgetting problem by introducing regularization in the parameter space or image space, FoCL imposes regularization in the feature space. We show in our experiments that FoCL has faster adaptation to distributional changes in sequentially arriving tasks, and achieves state-of-the-art performance for generative models in task incremental learning. We discuss choices of combined regularization spaces towards different use case scenarios for boosted performance, e.g., tasks that have high variability in the background. Finally, we introduce a forgetfulness measure that fairly evaluates the degree to which a model suffers from forgetting. Interestingly, the analysis of our proposed forgetfulness score also implies that FoCL tends to have a mitigated forgetting for future tasks.
Architecture of the intelligent video surveillance systems for fog environments based on embedded computers

The paper focuses on distributed video processing for embedded computers using foggy environments. The possibilities of using foggy technologies for intelligent video processing surveillance are identified. A comparison of existing systems for video processing is provided. The place of embedded computers in the intelligent video surveillance systems and the ways of modernizing these systems are considered. The data from the devices is collected using Internet of Things technologies. A system for solving tasks of intelligent video surveillance has been developed and the effectiveness of the new system has been proven. As an example, the task of processing video received from the cameras used in the subways is considered. The proposed system can be applied not only for processing images in the subways but in many other domains where the Internet of Things technologies are used.
Verifiable and Dynamic Multi-Keyword Search Over Encrypted Cloud Data Using Bitmap

Searchable Symmetric Encryption (SSE), which enables users to search over encrypted data without decryption, has gained increasing attention from both academic and industrial fields. However, existing SSE schemes either have low search efficiency or cannot support multi-keyword search, dynamic updates, and result verification simultaneously. To solve these problems, we propose a Verifiable and Dynamic Multi-keyword Search (VDMS) scheme over encrypted data by using the bitmap and RSA accumulator, which provides multi-keyword search over encrypted data in an efficient, verifiable and updated way. The bitmap is used as a data structure to build the indexes, which improves the search efficiency and reduces the storage space of the indexes. The RSA accumulator and bitmap are combined to verify the correctness of results. Formal security analysis proves that our VDMS is adaptively secure against Chosen-Keyword Attacks (CKA), and empirical experiments using a real-world dataset demonstrate that our VDMS is efficient and feasible in practical applications.
Brief Industry Paper: optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3×. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5× in memory efficiency improvement) and mitigate OOM problems during GNN inference.
Rethinking affective polarization and sharing of emotions in digital platform ecosystems. Theories and research practices

The purpose of this introduction is to set a useful frame for reading this thematic section. The key concepts of digital platforms, affective polarization, sharing of emotions are explored. The articles contained in this thematic section, which combine different research disciplines and techniques, provide to identify the different fields of communication within which the mediatization of emotions, the generation of affective cultures can prove to be a bearer of social, political cultural; and to outline methods and techniques for the analysis of emotions within digital platforms.