Reviewerly/exHarmony huggingface dataset:
Adversarial attack vulnerability of medical image analysis systems: Unexplored factors

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model. Secondly, we study the influence of differences in development data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice.
Low-rank quaternion tensor completion for recovering color videos and images

Low-rank quaternion tensor completion method, a novel approach to recovery color videos and images, is proposed in this paper. We respectively reconstruct a color image and a color video as a quaternion matrix (second-order tensor) and a third-order quaternion tensor by encoding the red, green, and blue channel pixel values on the three imaginary parts of a quaternion. Different from some traditional models which treat color pixel as a scalar and represent color channels separately, whereas, during the quaternion-based reconstruction, it is significant that the inherent color structures of color images and color videos can be completely preserved. Under the definition of Tucker rank, the global low-rank prior to quaternion tensor is encoded as the nuclear norm of unfolding quaternion matrices. Then, by applying the ADMM framework, we provide the tensor completion algorithm for any order ( ≥ 2) quaternion tensors, which theoretically can be well used to recover missing entries of any multidimensional data with color structures. Simulation results for color videos and color images recovery show the superior performance and efficiency of the proposed method over some state-of-the-art existing ones.
Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods

The values of global solar radiation are important fundamental data for potential evapotranspiration estimation, solar energy utilization, climate change study, crop growth model, and etc. This research tried to explore the optimal combination of input meteorological factors and the machine learning methods for the estimation of daily solar radiation under different climatic conditions so as to improve the estimation accuracy. Based on the correlation between meteorological factors, different meteorological factor input combinations were established and the support vector machine method was used to estimate global solar radiation at 80 weather stations in four climatic regions of China mainland. The results showed that, the optimal combinations of input meteorological factors were different in the four different climatic zones in China mainland. Three meteorological factors of sunshine hours, extraterrestrial radiation, and air temperature had greater impacts on the solar radiation estimation. Adding the factor of precipitation could obviously improve the estimation accuracy in humid regions, but not remarkably in arid regions. Wind speed had very little influence on solar radiation estimation. The accuracies of machine learning methods were better than the Angstrom-Prescott formula and the multiple linear regression method. Among them, support vector machine and extreme learning machine were more appropriate. In some sites, the root mean square error of support vector machine method was even 20% less than that of the Angstrom-Prescott formula. In general, reasonable division of the areas and establishment of appropriate input combinations of meteorological factors according to the climatic conditions, combined with machine learning methods, can effectively improve the accuracy of solar radiation estimation.
Reputation-Based Coalition Formation for Secure Self-Organized and Scalable Sharding in IoT Blockchains With Mobile-Edge Computing

We propose a fully distributed system architecture and a scalable self-organized sharding scheme for the Internet-of-Things (IoT) blockchains that can guarantee system security without reducing its throughput. In the system, the IoT devices are supported by the set of blockchain peers that gather, process, verify, and store the blocks of IoT transaction records. To support communications among peers, the system is realized in the mobile-edge computing (MEC) network. We design a new consensus mechanism in which each peer votes on the outputs of each block task in its shard. The peer's voting power is computed from its reputation, i.e., trustworthiness in the system. By adopting a reputation-based coalitional game model, we formulate a novel self-organized shard formation algorithm in which each peer acts as a rational player aiming to maximize both its payoff and the coalitional reputation. We prove that the algorithm converges to the reputation-based stable shard structure, i.e., a structure that maximizes the payoff and coalitional reputation of each peer without negatively affecting other peers. The algorithm shows a superior performance in terms of system security and throughput when compared to state-of-the-art sharding schemes and reputation-based blockchains.
Explaining Autonomous Driving by Learning End-to-End Visual Attention

Current deep learning based autonomous driving approaches yield impressive results also leading to in-production deployment in certain controlled scenarios. One of the most popular and fascinating approaches relies on learning vehicle controls directly from data perceived by sensors. This end-to-end learning paradigm can be applied both in classical supervised settings and using reinforcement learning. Nonetheless the main drawback of this approach as also in other learning problems is the lack of explainability. Indeed, a deep network will act as a black-box outputting predictions depending on previously seen driving patterns without giving any feedback on why such decisions were taken. While to obtain optimal performance it is not critical to obtain explainable outputs from a learned agent, especially in such a safety critical field, it is of paramount importance to understand how the network behaves. This is particularly relevant to interpret failures of such systems. In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in a standard benchmark using the CARLA driving simulator.
Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.
Energy-aware workflow scheduling and optimization in clouds using bat algorithm

With the ever-increasing deployment of data centers and computer networks around the world, cloud computing has emerged as one of the most important paradigms for large-scale data-intensive applications. However, these cloud environments face many challenges including energy consumption, execution time, heat and CO2 emission, as well as operational cost. Due to the extremely large scale of these applications and a huge amount of resource consumption, even a small portion of the improvements in any of the above fields can yield huge ecological and financial rewards. Efficient and effective workflow scheduling in cloud environments is one of the most significant ways to confront the above problems and achieve optimal resource utilization. We propose an Energy Aware, Time, and Throughput Optimization heuristic (EATTO) based on the bat algorithm. Our goal is to minimize energy consumption and execution time of computation-intensive workflows while maximizing throughput, without imposing any significant loss on the Quality of Service (QoS) guarantee.
Tracking multiple construction workers through deep learning and the gradient based method with re-matching based on multi-object tracking accuracy

Multiple construction worker tracking is an active research area critical to the planning of the job site. Challenges in multiple construction worker tracking include miss detection and mismatch due to occlusion and identity switches. To the best knowledge of the authors, the mismatch is not reported in the literature of construction for image based single camera multiple worker tracking. As a result, the mismatch should be taken into account through a representative performance index such as the Multi-Object Tracking Accuracy (MOTA). This work aims to improve the performance of the current multiple worker tracking through an approach composed of three stages: detection, matching and re-matching. In the detection stage, the deep learning detector, Mask R-CNN, is utilized. In the matching stage, we attempt to track workers between consecutive image frames through a gradient based method with feature based comparison. Several cost means and matching methods have been experimented for model selection. Trajectories of tracking objects are derived in this stage. The best cost measurements and matching methods are recommended. Trajectories of tracking objects could be interrupted because of miss detection or mismatch. We call those broken trajectories, without matched detections, orphans. In the re-matching stage, we attempt to recover unmatched detections in the current frame with previous orphans based on extracted features. A competitive MOTA of 56.7% was obtained from the proposed approach over MOTA of 55.9% from the state-of-the-art Detect-And-Track model on a human tracking benchmark dataset. On construction job sites, we have tested the approach with 4 testing videos, resulting in a total MOTA of 81.8%, average MOTA per video of 79.0% and standard deviation of 13.0%, while the maximum and minimum MOTAs are 96.0% and 69.0%, respectively. As a result, the proposed work could potentially provide better multiple worker tracking on the construction job site. Additionally, to have a better representation of the tracking errors, this work suggests to utilize the MOTA for multiple construction worker tracking.
Weaving a Second Skin

Weaving as a craft possesses the structural, textural, aesthetic, and cultural expressiveness for creating a diversity of soft, wearable forms that are capable of technological integration. In this paper, we extend the woven practice for crafting on-skin interfaces, exploring the potential to "weave a second skin." Weaving incorporates circuitry in the textile structure, which, when extended to on-skin interface fabrication, allows for electrical connections between layers while maintaining a slim form. Weaving also supports multi-materials integration in the structure itself, offering richer materiality for on-skin devices. We present the results of extensive design experiments that form a design space for adapting weaving for on-skin interface fabrication. We introduce a fabrication approach leveraging the skin-friendly material of PVA, which enables on-skin adherence, and a series of case studies illustrating the functional and design potential of the approach. To understand the feasibility of on-skin wear, we conducted a user study on device wearability. To understand the expressiveness of the design space, we conducted a workshop study in which textiles practitioners created woven on-skin interfaces. We draw insights from this to understand the potential of adapting weaving for crafting on-skin interfaces.
What Kind of Work Do "Asshole Designers" Create? Describing Properties of Ethical Concern on Reddit

Design practitioners are increasingly engaged in describing ethical complexity in their everyday work, exemplified by concepts such as "dark patterns" and "dark UX." In parallel, researchers have shown how interactions and discourses in online communities allow access to the various dimensions of design complexity in practice. In this paper, we conducted a content analysis of the subreddit "/r/assholedesign," identifying how users on Reddit engage in conversation about ethical concerns. We identify what types of artifacts are shared, and the salient ethical concerns that community members link with "asshole" behaviors. Based on our analysis, we propose properties that describe "asshole designers," both distinct and in relation to dark patterns, and point towards an anthropomorphization of ethics that foregrounds the inscription of designer's values into designed outcomes. We conclude with opportunities for further engagement with ethical complexity in online and offline contexts, stimulating ethics-focused conversations among social media users and design practitioners.
Proof of witness presence: Blockchain consensus for augmented democracy in smart cities

Smart Cities evolve into complex and pervasive urban environments with a citizens’ mandate to meet sustainable development goals. Repositioning democratic values of citizens’ choices in these complex ecosystems has turned out to be imperative in an era of social media filter bubbles, fake news and opportunities for manipulating electoral results with such means. This paper introduces a new paradigm of augmented democracy that promises actively engaging citizens in a more informed decision-making augmented into public urban space. The proposed concept is inspired by a digital revive of the Ancient Agora of Athens, an arena of public discourse, a Polis where citizens assemble to actively deliberate and collectively decide about public matters. The core contribution of the proposed paradigm is the concept of proving witness presence: making decision-making subject of providing secure evidence and testifying for choices made in the physical space. This paper shows how the challenge of proving witness presence can be tackled with blockchain consensus to empower citizens’ trust and overcome security vulnerabilities of GPS localization. Moreover, a novel platform for collective decision-making and crowd-sensing in urban space is introduced: Smart Agora. It is shown how real-time collective measurements over citizens’ choices can be made in a fully decentralized and privacy-preserving way. Witness presence is tested by deploying a decentralized system for crowd-sensing the sustainable use of transport means. Furthermore, witness presence of cycling risk is validated using official accident data from public authorities, which are compared against wisdom of the crowd. The paramount role of dynamic consensus, self-governance and ethically aligned artificial intelligence in the augmented democracy paradigm is outlined.
Cloud-Based Quadratic Optimization With Partially Homomorphic Encryption

This article develops a cloud-based protocol for a constrained quadratic optimization problem involving multiple parties, each holding private data. The protocol is based on the projected gradient ascent on the Lagrange dual problem and exploits partially homomorphic encryption and secure communication techniques. Using formal cryptographic definitions of indistinguishability, the protocol is shown to achieve computational privacy. We show the implementation results of the protocol and discuss its computational and communication complexity. We conclude this article with a discussion on privacy notions.
Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital

The COVID-19 pandemic has challenged our diagnostic services at a time when many histopathology departments already faced a diminishing workforce and increasing workload. Digital pathology (DP) has been hailed as a potential solution to at least some of the challenges faced. We present a survey of pathologists within a UK National Health Service cellular pathology department with access to DP, in which we ascertain the role of DP in clinical services during this current pandemic and explore challenges encountered. This survey indicates an increase in uptake of diagnostic DP during this period, with increased remote access. Half of respondents agreed that DP had facilitated maintenance of diagnostic practice. While challenges have been encountered, these are remediable, and none have impacted on the uptake of DP during this period. We conclude that in our institution, DP has demonstrated current and future potential to increase resilience in diagnostic practice and have highlighted some of the challenges that need to be considered.
Industrial Anomaly Detection: A Comparison of Unsupervised Neural Network Architectures

Anomaly detection is critical to the efficient and secure operation of industrial equipment, integrated sensors, and the overall production process. Cyber-physical systems and supervisory control and data acquisition systems often embed algorithms for anomaly detection. Anomalies are, by definition, rare events and labor-intensive to identify. Pipelined deep learning techniques such as recurrent neural networks (RNN), 1-D convolution neural networks (1DCNN), and generative adversarial networks create unique anomaly detection architectures. These architectures are unsupervised, and labeled anomaly data is not required. With two large, multivariate time-series testbed experimental datasets, comparisons are made between the neural network architectures and traditional machine learning (TML) baseline techniques. Evaluation metrics appropriate for imbalanced anomaly datasets include the area under the receiver operating characteristic curve and the area under the precision-recall tradeoff curve. Experimental results demonstrate that pipelined neural network architectures exhibit higher detection rates than the TML techniques. 1DCNN can substitute for RNNs when performance and long time-sequences are essential.
Antisocial online behavior detection using deep learning

Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The paper examines the viability of automatic content monitoring using deep machine learning and natural language processing (NLP). More specifically, we consolidate prior work in the field of antisocial online behavior detection and compare relevant approaches to recent NLP models in an empirical study. Covering important methodological advancements in NLP including bidirectional encoding, attention, hierarchical text representations, and pre-trained transformer-based language models, and extending previous approaches by introducing a pseudo-sentence hierarchical attention network, the paper provides a comprehensive summary of the state-of-affairs in NLP-based AOB detection, clarifies the detection accuracy that is attainable with today's technology, discusses whether this degree is sufficient for deploying deep learning-based text screening systems, and approaches the interpretability topic.
The combination of visual communication cues in mixed reality remote collaboration

Many researchers have studied various visual communication cues (e.g. pointer, sketch, and hand gesture) in Mixed Reality remote collaboration systems for real-world tasks. However, the effect of combining them has not been so well explored. We studied the effect of these cues in four combinations: hand only, hand + pointer, hand + sketch, and hand + pointer + sketch, within the two user studies when a dependent view and independent view are supported respectively. In the first user study with the dependent view, the results showed that the hand gesture cue was the main visual communication cue and adding sketch cue to the hand gesture cue helped participants complete the task faster. In the second study with the independent view, the results showed that the hand gesture had an issue of local worker understanding remote expert's hand gesture cue and the main visual communication cue was the pointer cue with fast completion time and high level of co-presence.
Evaluation of Energy Consumption in Block-Chains with Proof of Work and Proof of Stake

Abstract Although there are several special features in block-chain technology such as machine trust, traceability, and security, high energy consumption remains an issue in broadening the applications of block-chain technology. Some researchers proposed the use of proof of stake (PoS) mechanism rather than proof of work (PoW) mechanism to reduce energy consumption of block-chain. However, because PoS cannot guarantee fairness, mixed consensus mechanisms could be a solution and has been adopted in many studies. This paper aims to evaluate the performances of PoW, PoS and mixed consensus mechanisms from three aspects: energy consumption, fairness, and reliability. An agent-based model of a typical block-chain system equipped with different consensus mechanisms is created in NetLogo. This model simulates and evaluates the performances of different consensus mechanisms in the block-chain system.