Reviewerly/exHarmony huggingface dataset:
Deep learning of circulating tumour cells

Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. However, their use is currently hindered by their low frequency, tedious manual scoring and extensive cell heterogeneities. Those challenges limit the effectiveness of classical machine-learning methods for automated CTC analysis. Here, we combine autoencoding convolutional neural networks with advanced visualization techniques. This provides a very informative view on the data that opens the way for new biomedical research questions. We unravel hidden information in the raw image data of fluorescent images of blood samples enriched for CTCs. Our network classifies fluorescent images of single cells in five different classes with an accuracy, sensitivity and specificity of over 96%, and the obtained CTC counts predict the overall survival of cancer patients as well as state-of-the-art manual counts. Moreover, our network excelled in identifying different important subclasses of objects. Deep learning was faster and superior to classical image analysis approaches and enabled the identification of new biological phenomena. Counting different types of circulating tumour cells can give valuable information on the severity of the disease and on whether treatments are effective for a specific patient. In this work, the authors show that their method based on autoencoders can identify and count cells more accurately and faster than human experts.
Data Allocation Mechanism for Internet-of-Things Systems With Blockchain

The use of Internet of Things (IoT) has introduced genuine concerns regarding data security and its privacy when data are in collection, exchange, and use. Meanwhile, blockchain offers a distributed and encrypted ledger designed to allow the creation of immutable and tamper-proof records of data at different locations. While blockchain may enhance IoT with innate security, data integrity, and autonomous governance, IoT data management and its allocation in blockchain still remain an architectural concern. In this article, we propose a novel context-aware mechanism for on-chain data allocation in IoT-blockchain systems. Specifically, we design a data controller based on fuzzy logic to calculate the Rating of Allocation (RoA) value of each data request considering multiple context parameters, i.e., data, network, and quality and decide its on-chain allocation. Furthermore, we illustrate how the design and realization of the mechanism lead to refinements of two commonly used IoT-blockchain architectural styles (i.e., blockchain-based cloud and fog). To demonstrate the effectiveness of our approach, we instantiate the data allocation mechanism in the blockchain-based cloud and fog architectures and evaluate their performance using FogBus. We also compare the efficacy of our approach to the existing decision-making mechanisms through the deployment of a real-world healthcare application. The experimental results suggest that the realization of the data allocation mechanism improves network usage, latency, and blockchain storage and reduces energy consumption.
Graph convolutional network with structure pooling and joint-wise channel attention for action recognition

Recently, graph convolutional networks (GCNs) have achieved state-of-the-art results for skeleton based action recognition by expanding convolutional neural networks (CNNs) to graphs. However, due to the lack of effective feature aggregation method, e.g. max pooling in CNN, existing GCN-based methods only learn local information among adjacent joints and are hard to obtain high-level interaction features, such as interactions between five parts of human body. Moreover, subtle differences of confusing actions often hide in specific channels of key joints’ features, this kind of discriminative information is rarely exploited in previous methods. In this paper, we propose a novel graph convolutional network with structure based graph pooling (SGP) scheme and joint-wise channel attention (JCA) modules. The SGP scheme pools the human skeleton graph according to the prior knowledge of human body’s typology. This pooling scheme not only leads to more global representations but also reduces the amount of parameters and computation cost. The JCA module learns to selectively focus on discriminative joints of skeleton and pays different levels of attention to different channels. This novel attention mechanism enhance the model’s ability to classify confusing actions. We evaluate our SGP scheme and JCA module on three most challenging skeleton based action recognition datasets: NTU-RGB+D, Kinetics-M, and SYSU-3D. Our method outperforms the state-of-art methods on three benchmarks.
Collaborating with technology-based autonomous agents

Purpose This article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work. Design/methodology/approach The panelists (Drs Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision-making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis for a moderated panel discussion (the moderators were Drs Isabella Seeber and Lena Waizenegger), during which the initial position statements were elaborated on and additional issues were raised. Findings Through the discussion, a set of additional issues were identified. These issues related to (1) the design of autonomous technology-based agents in terms of human–machine workplace configurations, as well as transparency and explainability, and (2) the unintended consequences of using autonomous technology-based agents in terms of de-evolution of social interaction, prioritization of machine teammates, psychological health, and biased algorithms. Originality/value Key issues related to the affective and cognitive implications of using autonomous technology-based agents, design issues, and unintended consequences highlight key contemporary research challenges that allow researchers in this area to leverage compelling questions that can guide further research in this field.
Blockchain Applications in the Agri-Food Domain: The First Wave

This study aims to investigate the application potential of blockchain technology in the agri-food industry, by following a case study approach focused on the early adopter companies. As the topic is emerging, there are not enough academic papers available to warrant a systematic literature review. To confirm this hypothesis and to inform to better understand guide this research, we first conducted a pre-literature review. Based on its outcomes, we formulated three research questions, and developed a protocol to identify case studies, and subsequently analyzed them to answer our questions. We identified and studied six case studies, in which blockchain has been used to address issues of trust and transparency, and to facilitate information sharing among agrifood chain stakeholders. While blockchain adoption is still in its infancy, governance issues are important, as broader partnerships are required for successful, sustainable applications.
Development and validation of the DIGIGLO, a tool for assessing the digital competence of educators

Abstract Educators must be able to adapt to the opportunities provided by information and communication technologies, making use of those which are best suited to meeting their students' needs and identifying areas of practice in which they themselves need to develop their skills. The European Framework for the Digital Competence of Educators (DigCompEdu) proposes 22 basic competences, organised into six areas, which educators should ideally acquire. The present study seeks to build on this Framework by developing and validating an assessment tool that considers a total of eight areas: the six areas covered by DigCompEdu and two new areas corresponding to extrinsic factors in the digital competence of educators. The latter refers specifically to the digital resources and support services that are available to educators in their working environment. A total of 509 teacher educators from across Spain and Latin America completed the 29‐item DIGIGLO questionnaire. Analysis of the Spanish language instrument's construct validity and psychometric properties showed it to be a valid and reliable tool for assessing the digital competence of educators across the eight areas considered. Practitioner Notes What is already known about this topic Educators must be able to adapt (habituate) to the opportunities provided by information and communication technologies. The European Framework for the Digital Competence of Educators (DigCompEdu) proposes 22 basic competences, organised into six areas, which educators should ideally acquire. The speed with which digital technology is developing means that educators need to keep track of their own digital skills. What this paper adds We develop and validate an instrument for assessing educators' digital competence (the DIGIGLO) that considers external factors not included within the DigCompEdu. These external factors refer to the digital resources and support services that are available to educators in their working environment. The total score on the 29‐item questionnaire provides a measure of educators' overall digital competence across eight different areas. Implications for practice and/or policy The DIGIGLO is a brief and easy‐to‐use tool for assessing the digital competence of educators, a key issue in the educational field today. It extends the scope of the DigCompEdu Framework by including two further areas of digital competence (digital environment and extrinsic digital engagement). Application of the instrument can provide individual educators and their institutions with useful feedback about both strengths and areas in need of improvement with regard to the use of digital technology in teaching .
Deep Image Blending

Image composition is an important operation to create visual content. Among image composition tasks, image blending aims to seamlessly blend an object from a source image onto a target image with lightly mask adjustment. A popular approach is Poisson image blending [23], which enforces the gradient domain smoothness in the composite image. However, this approach only considers the boundary pixels of target image, and thus can not adapt to texture of target background image. In addition, the colors of the target image often seep through the original source object too much causing a significant loss of content of the source object. We propose a Poisson blending loss that achieves the same purpose of Poisson image blending. In addition, we jointly optimize the proposed Poisson blending loss as well as the style and content loss computed from a deep network, and reconstruct the blending region by iteratively updating the pixels using the L-BFGS solver. In the blending image, we not only smooth out gradient domain of the blending boundary but also add consistent texture into the blending region. User studies show that our method outperforms strong baselines as well as state-of-the-art approaches when placing objects onto both paintings and real-world images. Code is available at: https://github.com/owenzlz/DeepImageBlending.
Potentially toxic elements in soils of Campania region (Southern Italy): Combining raw and compositional data

Concern about health effect of Potentially Toxic Elements (PTEs) has led to an increasing global attention about their concentration levels in the environment. Soil geochemistry has been widely used as a tool for environment monitoring. This study investigates topsoil geochemistry of Campania region (Southern Italy) and (i) allows a reliable overview of the PTEs (As, Be, Cd, Co, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sn, Tl, V and Zn) concentration in soils, (ii) enable the investigation of the main factors governing PTEs geochemical variation on a regional scale. Over 7300 topsoil samples were collected from the survey area, which occupies an area of about 13.600 km2. Samples were analyzed for pseudo-total content of 53 elements (major and trace elements) by ICP-MS after aqua regia digestion. Data analysis was performed taking into account both raw data and their compositional nature as a tool for the PTEs environmental evaluation and origin investigation. As, Be, Cu, Pb, Sn, Tl, V and Zn in soils of Campania region show higher median concentration levels than Italian and European ones. In addition, all PTEs exceed the residential/ recreational intervention limit (CSCA) set by Italian legislation. The variation structure of compositional data had been visualized using compositional (clr) biplots and displaying the individual sample observations according to their parent rock. Clr-biplot analysis allowed us to recognize geochemical processes controlling most of soil chemical signatures. Multivariate analysis has been performed and three principal components were determined. PC1 is controlled by enrichment of elements deriving from dominant parent rocks of the area (siliciclastics and volcanoclastics). On the other hand, PC1 reveals the presence of an elemental association dominated by Na, K, U, Th, Zr, Ti, Tl and Be (clr variables), which are pathfinder element of soils developed from volcanic parent material. The second (PC2) component well discriminates the geochemical mobility of the elements in soils. The third (PC3) component reveal the presence of an anthropogenic association (Hg, Sb, Pb, Sn, Au, Ag) which depict a marginal contribution in soil geochemistry of the study area. The generation of clr-biplot helped us in a deeper interpretation of single element spatial distribution patterns. As, Be, Sn, Tl and V spatial distribution shows dominance in soil developed from volcanic products of the main volcanic complexes. Our study showed that element such as Be, Sn and Tl naturally exceed the contamination thresholds in almost the entire territory, due to a quite elevated background concentration values. Hence, Campania region con not be considered entirely contaminated (at this scale) by such elements. Co, Cd, Cr and Ni are more abundant where siliciclastic parent rocks occur.
Poisoning and Evasion Attacks Against Deep Learning Algorithms in Autonomous Vehicles

With the ongoing development and improvement of deep learning technology, autonomous vehicles (AVs) have made tremendous progress in recent years. Despite its great potential, AV supported by deep learning technology still faces numerous security threats, which prevent AV from being putting into large-scale practice. Aiming at this challenging situation, in this paper, we would like to exploit two attacks against deep learning algorithms in traffic sign recognition system by leveraging particle swarm optimization. Specifically, we first exploit the PAPSO (poisoning attack with particle swarm optimization) which focuses on the training process of the deep learning algorithms in the traffic sign recognition system, i.e., the attacker injects crafted samples into the training dataset, causing a reduction in classification accuracy of the traffic sign recognition system. Then, we also explore the EAPSO (evasion attack with particle swarm optimization) which on the other hand focuses on the interference process of the deep learning algorithms, i.e., the attacker adds some hardly perceptible perturbations to the targeted test sample, leading to a misclassification on it. Extensive experiments are conducted to shed light on the effectiveness of our attacks, and some corresponding defense strategies are also presented.
Learning Political Polarization on Social Media Using Neural Networks

Social media analysis is a fast growing research area aimed at extracting useful information from social media platforms.This paper presents a methodology, called IOM-NN (Iterative Opinion Mining using Neural Networks), for discovering the polarization of social media users during election campaigns characterized by the competition of political factions.The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users.Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules.Such rules are then used to determine the polarization of people towards a faction.The methodology has been assessed on two case studies that analyze the polarization of a large number of Twitter users during the 2018 Italian general election and 2016 US presidential election.The achieved results are very close to the real ones and more accurate than the average of the opinion polls, revealing the high accuracy and effectiveness of the proposed approach.Moreover, our approach has been compared to the most relevant techniques used in the literature (sentiment analysis with NLP, adaptive sentiment analysis, emoji-and hashtag-based polarization) by achieving the best accuracy in estimating the polarization of social media users.
Multiobjective Overtaking Maneuver Planning for Autonomous Ground Vehicles

Constrained autonomous vehicle overtaking trajectories are usually difficult to generate due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularly placed. In this article, a novel swarm intelligence-based algorithm is proposed for producing the multiobjective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process to dynamically balance the local exploitation and global exploration, thereby exploring the tradeoff between objectives more effectively. The performance of using the designed fuzzy adaptive multiobjective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multiobjective optimal overtaking trajectories for autonomous ground vehicles. Moreover, the comparison to other state-of-the-art multiobjective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality Pareto-optimal solutions.
A Survey of Deep Learning-Based Source Image Forensics

Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.
Bayesian Optimized 1-Bit CNNs

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited environments, such as on embedded devices and smart phones. Researchers have realized that 1-bit CNNs can be one feasible solution to resolve the issue; however, they are baffled by the inferior performance compared to the full-precision DCNNs. In this paper, we propose a novel approach, called Bayesian optimized 1-bit CNNs (denoted as BONNs), taking the advantage of Bayesian learning, a well-established strategy for hard problems, to significantly improve the performance of extreme 1-bit CNNs. We incorporate the prior distributions of full-precision kernels and features into the Bayesian framework to construct 1-bit CNNs in an end-to-end manner, which have not been considered in any previous related methods. The Bayesian losses are achieved with a theoretical support to optimize the network simultaneously in both continuous and discrete spaces, aggregating different losses jointly to improve the model capacity. Extensive experiments on the ImageNet and CIFAR datasets show that BONNs achieve the best classification performance compared to state-of-the-art 1-bit CNNs.
Visualising basins of attraction for the cross-entropy and the squared error neural network loss functions

Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural network loss surfaces at large. This work proposes a novel method to visualise basins of attraction together with the associated stationary points via gradient-based stochastic sampling. The proposed technique is used to perform an empirical study of the loss surfaces generated by two different error metrics: quadratic loss and entropic loss. The empirical observations confirm the theoretical hypothesis regarding the nature of neural network attraction basins. Entropic loss is shown to exhibit stronger gradients and fewer stationary points than quadratic loss, indicating that entropic loss has a more searchable landscape. Quadratic loss is shown to be more resilient to overfitting than entropic loss. Both losses are shown to exhibit local minima, but the number of local minima is shown to decrease with an increase in dimensionality. Thus, the proposed visualisation technique successfully captures the local minima properties exhibited by the neural network loss surfaces, and can be used for the purpose of fitness landscape analysis of neural networks.
Path Planning for UAV to Cover Multiple Separated Convex Polygonal Regions

In many unmanned aerial vehicle (UAV) applications such as land assessment, search and rescue, and precision agriculture, UAVs are often required to survey multiple spatially distributed regions. To perform these applications, one of the key steps is to plan the path for the UAV to quickly cover all regions. The new path planning problem explored here, which we call the TSP-CPP problem, can be viewed as an integration of the traveling salesman problem (TSP) and the coverage path planning (CPP) problem, which has not been well studied in the literature. In this paper, we conduct a systematic investigation on the TSP-CPP problem. In particular, we first provide a mixed integer programming formulation for this new problem, and then introduce a CPP method for covering a single convex polygonal region. Based on this method, we then develop two approaches to solve the TSP-CPP problem, including 1) a dynamic programming-based exact approach that can find the (near) optimal tour, and 2) a heuristic approach that can generate high-quality tours very efficiently. Through comprehensive theoretical analyses and simulation studies, we demonstrate the optimality and efficiency of the proposed approaches.