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Background In Software Engineering (SE), conference publications have high importance both in effective communication and in academic careers. Researchers actively discuss how a paper should be organized to be accepted in mainstream conferences. Aiming This work tackles the problem of generalizing and characterizing the type of papers accepted at SE conferences. Method The paper offers a new perspective in the analysis of SE literature: a categorization scheme for SE papers is obtained by merging, extending and revising related proposals from a few existing studies. The categorization scheme is used to classify the papers accepted at three top-tier SE conferences during five years (2012–2016). Results While a broader experience is certainly needed for validation and fine-tuning, preliminary outcomes can be observed relative to what problems and topics are addressed, what types of contributions are presented and how they are validated. Conclusions The results provide insights to paper writers, paper reviewers and conference organizers in focusing their future efforts, without any intent to provide judgments or authoritative guidelines.

Internet of Things (IoT) devices are increasingly being found in civilian and military contexts, ranging from smart cities and smart grids to Internet-of-Medical-Things, Internet-of-Vehicles, Internet-of-Military-Things, Internet-of-Battlefield-Things, etc. In this paper, we survey articles presenting IoT security solutions published in English since January 2016. We make a number of observations, including the lack of publicly available IoT datasets that can be used by the research and practitioner communities. Given the potentially sensitive nature of IoT datasets, there is a need to develop a standard for sharing IoT datasets among the research and practitioner communities and other relevant stakeholders. Thus, we posit the potential for blockchain technology in facilitating secure sharing of IoT datasets (e.g., using blockchain to ensure the integrity of shared datasets) and securing IoT systems, before presenting two conceptual blockchain-based approaches. We then conclude this paper with nine potential research questions. Keywords: Blockchain, Blockchain security, Collaborative security, Internet-of-military things, IoT dataset, IoT self-healing, IoT security, Intrusion-prevention system, Predictive IoT security, Predictive security

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

The newly developed research area of inkjet-printed radio frequency (RF) electronics on cellulose-based and synthetic paper substrates is introduced in this paper. This review paper presents the electrical properties of the paper substrates, the printed silver nanoparticle-based thin films, the dielectric layers, and the catalyst-based metallization process. Numerous inkjet-printed microwave passive/ative systems on paper, such as a printed radio frequency identification (RFID) tag, an RFID-enabled sensor utilizing carbon nanotubes (CNTs), a substrate-integrated waveguide (SIW), fully printed vias, an autonomous solar-powered beacon oscillator (active antenna), and artificial magnetic conductors (AMC), are discussed. The reported technology could potentially act as the foundation for true “green” low-cost scalable wireless topologies for autonomous Internet-of-Things (IoT), bio-monitoring, and “smart skin” applications.

Physical paper, in its various forms (e.g. books, leaflets, catalogues), is extensively used in everyday activities, despite any advancements in digital technology and ICT. Inspired by the popularity of this medium, several research efforts have envisioned and pursued a new era of interactive paper, however several challenges still remain to be addressed. On the other hand, recent advancements towards Ambient Intelligence (AmI) and Smart environments, bear the promise of seamless integration of the physical and digital worlds in an intuitive and user-friendly manner. This paper presents InPrinted, a systematic and generic framework supporting physical paper augmentation and user interaction in Ambient intelligence environments. InPrinted enables natural multimodal user interaction with any kind of printed matter in smart environments, providing context aware and anticipation mechanisms, as well as tools and interaction techniques that support the development of applications incorporating printed matter augmentation. InPrinted has been put to practice in the development of various systems, including the Interactive Maps system, facilitating interaction with printed maps as well as their augmentation with digital information. The results of an in-situ observation experiment of the Interactive Maps system are reported, highlighting that interacting with augmented paper is quite easy and natural, while the overall User Experience is positive.

Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device&rsquo, s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow&rsquo, s IoT devices.

Today, the requirement of high grade papers is increasing day by day. To cater the need of high quality papers, new high-speed paper machines are being developed constantly. The whole economy of a pulp and paper industry depends on the quality of paper produced, quality of paper depends on the efficient operation of the major part of paper industry – paper machine. Paper machine is the heart of paper industry and has many subsystems. One of the important subsystem of paper machine is Headbox. To have the desired quality papers, efficient working of headbox is highly required. To have desired operation from headbox, its precise control is necessary. The precise modelling of headbox, leads to design of efficient controllers for its proper control to get the desired response. This paper presents the mathematical modelling of headbox along with a brief review of various research performed on headbox.

Paper, today, has made an important place in human’s life. Being an important commodity, paper has different qualities throughout the globe. As the technology is advancing, paper is being used for numerous applications and need of good quality paper is increasing daily. To have papers of desirable quality, paper machines with latest technology are developed. However, the quality of paper principally depends on the prime element of paper machine i.e. Headbox. Headbox is a highly non-linear Multi-input Multi-output (MIMO) component of paper machine and requires a precise control of its major parameters to get good quality papers which eventually responsible for sustained economy of pulp and paper industry. In past few decades, headbox has been a key element for research. Researchers have developed different control algorithms for headbox. This paper presents stability analysis of paper machine headbox using a new technique of Proportional – Integral – Derivative (PID) controller design namely Extended Forced Oscillation method (EFO). Also, the paper presents a comparative analysis of EFO method of PID tuning with the conventional Ziegler - Nichols(ZN) tuning technique based on transient characteristics and performance indices of headbox.

Internet of Things (IoT) powered by high level synthesis (HLS) provides huge opportunity of progress in the area of hardware design. However, the present era of hardware design involves globalization which poses security threat to the design integrators that rely on third party intellectual property (IP) cores for increasing design productivity at reduced design time. This paper presents for the first time in the literature, a low cost optimized hardware Trojan secured HLS approach for hardware (or application specific core) designs that is based on single or nested loop control data flow graphs (CDFG) applications. The paper presents multiple novel vendor allocation schemes (each with its own attribute of delay and area) as security constraint that yield Trojan secured schedules at behavioral level. Demonstration of the proposed approach on a nested loop case study asserts our proposed theory. Results on standard benchmarks indicate significant reduction in final solution cost compared to a similar approach.

Paper checks may have complex background features including fine lines and patterns, which make forgery more difficult. Also, halftoning techniques are used to produce continuous tones and to prevent copies with void pantograph features. When these kinds of checks are scanned, Moire patterns may occur. These patterns make it difficult for customers to examine the scanned check images on ATM (Automated Teller Machine) displays. They also can decrease the classification accuracy of check recognition systems. In this paper, we propose an algorithm to enhance the perceptual quality of scanned check images by reducing the Moire patterns. The proposed algorithm consists of foreground extraction, Moire detection and Moire removal. Subjective image quality assessment was performed to evaluate the degree of improvement. Experimental results show that the proposed algorithm improves perceptual quality while maintaining check recognition accuracy.

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

Architectures, networks, and systems and middleware technologies have been advancing.These advances lead to new concepts and platforms for computing, ranging from clusters and grids to clouds and datacenters.The CCGrid 2015 Conference discussed research and results on topics related to these concepts and platforms, and their applications.This special section presents five high-quality papers, extended from CCGrid 2015 papers.

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements are learned and inferred from data. During the inference stage, an MAP inference framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach., 10 pages, submission to IEEE TPAMI. arXiv admin note: text overlap with arXiv:1604.01219

One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip. Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming. In this work, we detail the Answer Set Programming approach we used to solve such problem.Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains.

Electronic Engineering and Design Innovations are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs [...]

With the explosion of “big data” in the past decade, exploring and mining the value hidden in the data has already generated a lot of innovative applications, especially the recent advances of AI applications. The data governance, including activities of data creation, sharing, exchange, management, analytics, tracing, and accounting, has drawn a lot of attentions. Services computing establishes the foundation of current data governance, typically in a centralized fashion, e.g., the cloud-based storage services and analytic services. However, the potential values of big data distributed on the Internet are far away from being adequately explored. Considering the infrastructure revolution made by the blockchain, in this position article, we try to rethink a new data governance fashion that is built upon the blockchain-based decentralized services computing paradigm. The core principle is that data owners are able to publish their data as a set of services that can be deployed independently from the application systems where the data were born. Meanwhile, data owners can define service rules/policies where their data should be stored and how the data can be shared, and keep governing the whole lifecycle record of how their data are actually used. Similar to existing services computing paradigm, data users can search, discover, integrate, and analyze the data in a decentralized fashion. With this perspective, we try to discuss some key insights and enumerate several related new technologies and open challenges, in terms of programmability, interoperability, and intelligence.

International audience

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

Nagahara M., Krishnamachari B., Ogura M., et al. Control, intervention, and behavioral economics over human social networks against COVID-19. Advanced Robotics, 35(11), 733-739, May 19, 2021 © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan. https://doi.org/10.1080/01691864.2021.1928553., In this short paper, we propose a new direction of cross-cutting research for prediction and control of spreading COVID-19 viruses over a human social network. Such a network consists of human agents whose behaviors are highly uncertain and biased. To predict and control such an uncertain network, we need to employ various researches such as control theory, signal processing, machine learning, and behavioral economics. In this article, we introduce our recent research results and propose future research topics to overcome the COVID-19 pandemic.

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsule-based neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0782-5.

We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency. Under consideration for acceptance in TPLP., Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 16 pages

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security,etc.

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence–assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence–assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.

Application specific instruction-set processors (ASIP) are a programmable and flexible alternative of traditional finite state machine (FSM) controlled register-transfer level (RTL) designs for multimode basedband systems. In this paper, we present two ASIPs for small scale multiple-input multiple-output (MIMO) wireless communication systems that demonstrate the soundness and effectiveness of ASIPs for this type of applications. The first ASIP is programmed with multiple MIMO symbol detection algorithms for 4 × 4 systems. The supported detection algorithms are minimum mean-square error (MMSE), two variants of the selective spanning with fast enumeration (SSFE) and K-best list sphere detection (LSD). The second ASIP supports MMSE and zero-forcing dirty paper coding (ZF-DPC) algorithms for a base station (BS) with 4 antennas and for 4 users. Both ASIPs are based on transport triggered architecture (TTA) and are programmed with a retargetable compiler with high level language to meet the time-to-market requirements. The detection and precoding algorithms can be switched in the respective ASIPs based on the error-rate requirements. Depending on the algorithms, MIMO detection ASIP delivers 6.16—66.66 Mbps throughput at a clock frequency of 200 MHz on 90 nm technology. The precoder ASIP provides a throughput of 52.17 and 51.95 Mbps for MMSE and ZF-DPC precoding respectively at a clock frequency of 210 MHz on 90 nm technology.

Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced good results on five widely used datasets. In addition, we also conducted a series of ablation experiments to verify the effectiveness of some components in the network.

Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

Annually, lots of research papers are published in scientific journals around the world. The knowledge of the status of research is a prerequisite for research planning and policy making. This type of knowledge could be gained through a scientometrics study on the published literature that analyzes research products in a scientific field. Always healthcare was a permanent concern of researchers and also rapidly expanding field of Big Data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It leads attracting attention from academia, industry and even governments around the world to “Big data in Healthcare”. Therefore, this paper has done a meta-analysis on published researches methodology in this field in the period of 2008–2018. Statistical finding shows the “Meta-analysis and evidence” is the most used methodology in published papers. We applied data mining techniques for predicting using methodologies in the various databases to achieving knowledge discovery in the field. Naïve Bayes classifier in RapidMiner has been applied and results show eight main categories for words used in papers while “Developing methods to evaluate of care” averagely is the most intended using methodology for publishing papers and “Agent-based modeling” in nature is most using methodology and could be better predicted.

Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.

The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. The use-case of performing global computations over a graph, it is first ingested into a graph processing system from one of many digital representations. Extracting information from graphs involves processing all their elements globally, and can be done with single-machine systems (with varying approaches to hardware usage), distributed systems (either homogeneous or heterogeneous groups of machines) and systems dedicated to high-performance computing (HPC). We provide an overview of different aspects of the graph processing landscape and describe classes of systems based on a set of dimensions we detail. The dimensions we detail encompass paradigms to express graph processing, different types of systems to use, coordination and communication models in distributed graph processing, partitioning techniques and different definitions related to the potential for a graph to be updated. This survey is aimed at both the experienced software engineer or researcher as well as the newcomer looking for an understanding of the landscape of solutions (and their limitations) for graph processing., Comment: 42 pages, 5 figures, 2 tables

Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of non-expert is exploited. We study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done. In our experiments, crowd workers are asked to assess the truthfulness of statements, and to provide evidence for the assessments. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we report results on workers behavior, agreement among workers, effect of aggregation functions, of scales transformations, and of workers background and bias. We perform a longitudinal study by re-launching the task multiple times with both novice and experienced workers, deriving important insights on how the behavior and quality change over time. Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e.g., source of information, behavior) impact the quality of the data. The longitudinal study demonstrates that the time-span has a major effect on the quality of the judgments, for both novice and experienced workers. Finally, we provide an extensive failure analysis of the statements misjudged by the crowd-workers., 31 pages; Preprint of an article accepted in Personal and Ubiquitous Computing (Special Issue on Intelligent Systems for Tackling Online Harms, 2021). arXiv admin note: substantial text overlap with arXiv:2008.05701

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly achieved global pandemic status. The pandemic created huge demand for relevant medical and personal protective equipment (PPE) and put unprecedented pressure on the healthcare system within a very short span of time. Moreover, the supply chain system faced extreme disruption as a result of the frequent and severe lockdowns across the globe. In such a situation, additive manufacturing (AM) becomes a supplementary manufacturing process to meet the explosive demands and to ease the health disaster worldwide. Providing the extensive design customization, a rapid manufacturing route, eliminating lengthy assembly lines and ensuring low manufacturing lead times, the AM route could plug the immediate supply chain gap, whilst mass production routes restarted again. The AM community joined the fight against COVID-19 by producing components for medical equipment such as ventilators, nasopharyngeal swabs and PPE such as face masks and face shields. The aim of this article is to systematically summarize and to critically analyze all major efforts put forward by the AM industry, academics, researchers, users, and individuals. A step-by-step account is given summarizing all major additively manufactured products that were designed, invented, used, and produced during the pandemic in addition to highlighting some of the potential challenges. Such a review will become a historical document for the future as well as a stimulus for the next generation AM community.

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

This position paper describes the context, the goal, the strategy and the tactics of the ERATO MMSD project (2016–2022). The project aims at enhanced quality assurance measures for industry products like cars. In doing so, we follow a recent trend and exploit formal methods, a body of mathematical techniques originally developed for computer systems. However, there are fundamental gaps in application of formal methods to industry products: additional concerns in industry products such as continuous dynamics of physical components and quantitative measures such as probability, time, and cost make problems fundamentally different from those about software. Formal methods that accommodate these concerns is an active research area, which shows that it is a hard problem. There are several successful theoretical developments in this direction. They typically combine one individual technique with one specific concern, such as hybrid automata that extend automata with continuous dynamics. Our project aims to contribute to this hard problem in a unique way. In our project we will take a unique metamathematical strategy to bridging the gaps: instead of creating one technique for each concern, we want to find a meta-level theory that describes how to develop such techniques for many potential concerns in general. Through this strategy, together with our emphasis on real-world applications in industry, we expect a new prototype of applied mathematics will emerge. In this prototype, abstraction and genericity—characteristics of modern mathematics that are not often associated with application—are turned into crucial advantages in applications.

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.

The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of ScienceTM Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.

Empirical software engineering has received much attention in recent years and coined the shift from a more design-science-driven engineering discipline to an insight-oriented, and theory-centric one. Yet, we still face many challenges, among which some increase the need for interdisciplinary research. This is especially true for the investigation of human-centric aspects of software engineering. Although we can already observe an increased recognition of the need for more interdisciplinary research in (empirical) software engineering, such research configurations come with challenges barely discussed from a scientific point of view. In this position paper, we critically reflect upon the epistemological setting of empirical software engineering and elaborate its configuration as an Interdiscipline. In particular, we (1) elaborate a pragmatic view on empirical research for software engineering reflecting a cyclic process for knowledge creation, (2) motivate a path towards symmetrical interdisciplinary research, and (3) adopt five rules of thumb from other interdisciplinary collaborations in our field before concluding with new emerging challenges. This shall support stopping to treating empirical software engineering as a developing discipline moving towards a paradigmatic stage of normal science, but as a configuration of symmetric interdisciplinary teams and research methods., Preprint accepted by Journal of Systems and Software (controversy corner)

Accurate segmentation of zebrafish from bright-field microscope images is crucial to many applications in the life sciences. Early zebrafish stages are used, and in these stages the zebrafish is partially transparent. This transparency leads to edge ambiguity as is typically seen in the larval stages. Therefore, segmentation of zebrafish objects from images is a challenging task in computational bio-imaging. Popular computational methods fail to segment the relevant edges, which subsequently results in inaccurate measurements and evaluations. Here we present a hybrid method to accomplish accurate and efficient segmentation of zebrafish specimens from bright-field microscope images. We employ the mean shift algorithm to augment the colour representation in the images. This improves the discrimination of the specimen to the background and provides a segmentation candidate retaining the overall shape of the zebrafish. A distance-regularised level set function is initialised from this segmentation candidate and fed to an improved level set method, such that we can obtain another segmentation candidate which preserves the explicit contour of the object. The two candidates are fused using heuristics, and the hybrid result is refined to represent the contour of the zebrafish specimen. We have applied the proposed method on two typical datasets. From experiments, we conclude that the proposed hybrid method improves both efficiency and accuracy of the segmentation of the zebrafish specimen. The results are going to be used for high-throughput applications with zebrafish.

Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge. Often, the metadata associated with images is sparse or is inconsistent. This makes keyword-based exploratory search difficult, leaving researchers to rely on serendipity and slowing down the research process. We present an image-based retrieval system that addresses this problem for biface artefacts. In order to identify artefact characteristics that need to be captured by image features, we conducted a contextual inquiry study with experts in bifaces. We then devised several descriptors for matching images of bifaces with similar artefacts. We evaluated the performance of these descriptors using measures that specifically look at the differences between the sets of images returned by the search system using different descriptors. Through this nuanced approach, we have provided a comprehensive analysis of the strengths and weaknesses of the different descriptors and identified implications for design in the search systems for archaeology.