Reviewerly/exHarmony huggingface dataset:
Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction.To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer, which was attained in two stages.First, several images were generated from a single image using the pre-processing method.The median and Wiener filters were utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second, the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images, respectively.The proposed method achieved very high accuracy (98%), sensitivity (98%), and specificity (99%).As a result, the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy, sensitivity, and specificity.
Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “ ... all the impressive achievements of deep learning amount to just curve fitting .” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times : “ we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality .” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all.
Test Selection for Deep Learning Systems

Testing of deep learning models is challenging due to the excessive number and complexity of the computations involved. As a result, test data selection is performed manually and in an ad hoc way. This raises the question of how we can automatically select candidate data to test deep learning models. Recent research has focused on defining metrics to measure the thoroughness of a test suite and to rely on such metrics to guide the generation of new tests. However, the problem of selecting/prioritising test inputs (e.g., to be labelled manually by humans) remains open. In this article, we perform an in-depth empirical comparison of a set of test selection metrics based on the notion of model uncertainty (model confidence on specific inputs). Intuitively, the more uncertain we are about a candidate sample, the more likely it is that this sample triggers a misclassification. Similarly, we hypothesise that the samples for which we are the most uncertain are the most informative and should be used in priority to improve the model by retraining. We evaluate these metrics on five models and three widely used image classification problems involving real and artificial (adversarial) data produced by five generation algorithms. We show that uncertainty-based metrics have a strong ability to identify misclassified inputs, being three times stronger than surprise adequacy and outperforming coverage-related metrics. We also show that these metrics lead to faster improvement in classification accuracy during retraining: up to two times faster than random selection and other state-of-the-art metrics on all models we considered.
Efficient key-dependent dynamic S-boxes based on permutated elliptic curves

Key-dependent dynamic substitution boxes (S-boxes) are important building blocks of many image encryption schemes, in which a session key controls the construction of dynamic S-boxes. Frequently changing the dynamic S-boxes is necessary to improve security. However, the computational overhead of dynamic S-box construction limits the achievable encryption throughput. Therefore, minimizing the construction time of dynamic S-boxes is required. In this paper, we exploit key-dependent permutations over finite elliptic curves to present a novel computationally efficient method for dynamic S-box construction. Theoretical analysis shows that the proposed S-box construction method is immune to chosen-plaintext attacks. Analysis of an image encryption application using the proposed key-dependent S-box indicates resistance to statistical, related key, and chosen-plaintext attacks. Experimental results show that the proposed method is asymptotically an order-of-magnitude faster than relevant methods. A dynamic S-box with 512-bit key can be generated by the proposed method in 0.76 ms, paving the way to reach 1 GB/s encryption throughput. Results show that the generated S-boxes satisfy the standard S-box security criteria and have a competitive nonlinearity distribution compared with rival methods.
A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case

Autonomous surfaces vehicles (ASVs) excel at monitoring and measuring aquatic nutrients due to their autonomy, mobility, and relatively low cost.When planning paths for such vehicles, the task of patrolling with multiple agents is usually addressed with heuristics approaches, such as Reinforcement Learning (RL), because of the complexity and high dimensionality of the problem.Not only do efficient paths have to be designed, but addressing disturbances in movement or the battery's performance is mandatory.For this multiagent patrolling task, the proposed approach is based on a centralized Convolutional Deep Q-Network, designed with a final independent dense layer for every agent to deal with scalability, with the hypothesis/assumption that every agent has the same properties and capabilities.For this purpose, a tailored reward function is created which penalizes illegal actions (such as collisions) and rewards visiting idle cells (cells that remains unvisited for a long time).A comparison with various multiagent Reinforcement Learning (MARL) algorithms has been done (Independent Q-Learning, Dueling Q-Network and multiagent Double Deep Q-Learning) in a case-study scenario like the Ypacaraí lake in Asunción (Paraguay).The training results in multiagent policy leads to an average improvement of 15% compared to lawn mower trajectories and a 6% improvement over the IDQL for the case-study considered.When evaluating the training speed, the proposed approach runs three times faster than the independent algorithm.
Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks

The advances in today's IoT devices and machine learning methods have given rise to the concept of Federated Learning. Through such a technique, a plethora of network devices collaboratively train and update a mutual machine learning model while protecting their individual data-sets. Federated learning proves its effectiveness in tackling communication efficiency and privacy-safeguarding issues. Moreover, blockchain was introduced to solve many network issues in regard to data privacy and network single point of failure. In this article, we introduce a solution that integrates both federated learning and blockchain to ensure both data privacy and network security. We present a framework to decentralize the mutual machine learning models on end-devices. A blockchain-based consensus solution as a second line of privacy is used to ensure trustworthy shared training on the fog. The proposed model enables on-end device machine learning without any centralized training of the data nor coordination by utilizing a consensus method in the blockchain. We evaluate and verify our proposed model through simulation to showcase the effectiveness of the adapted scheme in terms of accuracy, energy consumption, and lifetime rate, along with throughput and latency metrics. The proposed model performs with an accuracy rate of ≈ 0.97.
Short Text Sentiment Analysis Based on Multi-Channel CNN With Multi-Head Attention Mechanism

In view of the limited text features of short texts, features of short texts should be mined from various angles, and multiple sentiment feature combinations should be used to learn the hidden sentiment information. A novel sentiment analysis model based on multi-channel convolutional neural network with multi-head attention mechanism (MCNN-MA) is proposed. This model combines word features with part of speech features, position features and dependency syntax features separately to form three new combined features, and inputs them into the multi-channel convolutional neural network, as well as integrates the multi-head attention mechanism to more fully learn the sentiment information in the text. Finally, experiments are carried out on two Chinese short text data sets. The experimental results show that the MCNN-MA model has a higher classification accuracy and a relatively low training time cost compared with other baseline models.
A Resource-Constrained and Privacy-Preserving Edge-Computing-Enabled Clinical Decision System: A Federated Reinforcement Learning Approach

Internet-of-Things-enabled E-health system, which could monitor and collect the personal health information (PHI), has gradually transformed the clinical treatment to a more personalized way with in-home monitoring smart devices. Then, with the collected PHI, clinical decision support systems (CDSSs), which are based on data mining techniques and historical electronic medical records (EMRs) to help clinicians make proper treatment decisions, have attracted considerable attention. To address issues, such as network congestion and low rate of responsiveness for traditional methods when implementing CDSSs, we integrate the technologies mobile-edge computing (MEC) and software-defined networking for exploiting the computation resources and storage capacities among edge nodes (ENs) (i.e., MEC servers) in our model. Based on this integrated system, each edge node will deploy a double deep Q-network (DDQN) to obtain a stable and sequential clinical treatment policy. It is enabled by a novel fully decentralized federated framework (FDFF) for aggregating models of DDQN and extracting the knowledge from EMRs across all ENs. Furthermore, we discuss the convergence of FDFF in resource-constrained environments. However, since most EMRs are faced with stringent privacy concerns, we adopt two additively homomorphic encryption schemes to prevent leakage of EMRs' privacy during the training process of FDFF. Finally, we measure the time cost of our additively homomorphic encryption schemes and validate DDQN with experiments on large data sets based on FDFF, which shows promising performance on clinician treatment.
A Supply-chain System Framework Based on Internet of Things Using Blockchain Technology

Numerous supply-chain combines with internet of things (IoT) applications have been proposed, and many methods and algorithms enhance the convenience of supply chains. However, new businesses still find it challenging to enter a supply chain, because unauthorised IoT devices of different companies illegally access resources. As security is paramount in a supply chain, IoT management has become very difficult. Public resources allocation and waste management also pose a problem. To solve the above problems, we proposed a new IoT management framework that embraces blockchain technology to help companies to form a supply chain effectively. This framework consists of an access control system, a backup peer mechanism and an internal data isolation and transmission approach. The access control system has a registrar module and an inspection module. The registrar module is mainly responsible for information registration with a registration policy, which has to be followed by all the companies in the supply chain. Besides, it provides a revocation and updating function. The inspection module focuses on judging misbehaviour and monitors the actions of the subjects; when any misoperation occurs, the system will correspondingly penalise violators. So that all related actions and information are verified and stored into blockchain, the IoT access control and safety of IoT admission are enhanced. Furthermore, in a blockchain system, if one single peer in the network breaks down, then the whole system may stop, because consensus cannot be reached. The data of the broken peer may be lost if it does not commit yet. The backup peer mechanism allows the primary peer and the backup peer to connect to an inspecting server for acquiring real-time data. The internal data isolation and transmission modules transmit and stores private data without creating a new subchannel. The proposed method is taken full account of the stability of the network and the fault tolerance to guarantee the robust of the system. To obtain unbiases results, experiments are conducted in two different blockchain environment. The results show our proposed method are promising IoT blockchain system for the supply chain.
Blockchain-based privacy preservation for supply chains supporting lightweight multi-hop information accountability

The benefits of information sharing along supply chains are well known for improving productivity and reducing costs. However, with the shift toward more dynamic and flexible supply chains, privacy concerns severely challenge the required information retrieval. A lack of trust between the different involved stakeholders inhibits advanced, multi-hop information flows, as valuable information for tracking and tracing products and parts is either unavailable or only retained locally. Our extensive literature review of previous approaches shows that these needs for cross-company information retrieval are widely acknowledged, but related work currently only addresses them insufficiently. To overcome these concerns, we present PrivAccIChain, a secure, privacy-preserving architecture for improving the multi-hop information retrieval with stakeholder accountability along supply chains. To address use case-specific needs, we particularly introduce an adaptable configuration of transparency and data privacy within our design. Hence, we enable the benefits of information sharing as well as multi-hop tracking and tracing even in supply chains that include mutually distrusting stakeholders. We evaluate the performance of PrivAccIChain and demonstrate its real-world feasibility based on the information of a purchasable automobile, the e.GO Life. We further conduct an in-depth security analysis and propose tunable mitigations against common attacks. As such, we attest PrivAccIChain’s practicability for information management even in complex supply chains with flexible and dynamic business relationships.
MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

We present MultEYE, a traffic monitoring system that can detect, track, and estimate the velocity of vehicles in a sequence of aerial images. The presented solution has been optimized to execute these tasks in real-time on an embedded computer installed on an Unmanned Aerial Vehicle (UAV). In order to overcome the limitation of existing object detection architectures related to accuracy and computational overhead, a multi-task learning methodology was employed by adding a segmentation head to an object detector backbone resulting in the MultEYE object detection architecture. On a custom dataset, it achieved 4.8% higher mean Average Precision (mAP) score, while being 91.4% faster than the state-of-the-art model and while being able to generalize to different real-world traffic scenes. Dedicated object tracking and speed estimation algorithms have been then optimized to track reliably objects from an UAV with limited computational effort. Different strategies to combine object detection, tracking, and speed estimation are discussed, too. From our experiments, the optimized detector runs at an average frame-rate of up to 29 frames per second (FPS) on frame resolution 512 × 320 on a Nvidia Xavier NX board, while the optimally combined detector, tracker and speed estimator pipeline achieves speeds of up to 33 FPS on an image of resolution 3072 × 1728. To our knowledge, the MultEYE system is one of the first traffic monitoring systems that was specifically designed and optimized for an UAV platform under real-world constraints.
Cyber security training for critical infrastructure protection: A literature review

Today, cyber-security curricula are available across educational types and levels, including a vast array of programs and modules tailored to specific sectors of industry and audiences, to allow more targeted delivery of knowledge. Nonetheless, general agreement on best measures and methods for cybersecurity training has yet to be reached. In this study, we seek to establish the current state-of-the-art in cyber-security training offerings for critical infrastructure protection and the key performance indicators (KPIs) that allow evaluating their effectiveness. Particular focus is given in this study on the aviation, energy and nuclear sectors. Accordingly, the article presents the findings of a systematic literature review that collected relevant literature produced after 2000. The identified sources have been examined according to a formal data extraction form, allowing the analysis of relevant training solutions, methodologies, target groups and focus areas. The results show that solutions that provide hands-on experience, team skills development, high level of real-life fidelity are often preferred to other options, with simulation-based solutions showing the highest amount of research and development. Nonetheless, researchers have not reached agreements on optimal training delivery methods and design of cybersecurity exercises. Consequently, research on improving current cybersecurity training offerings should be conducted, to demonstrate whether integrating advantageous attributes from different delivery methods could produce more comprehensive and effective solutions.
A Collaborative Learning Framework via Federated Meta-Learning

Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local data. To tackle these challenges, we propose a platform-aided collaborative learning framework where a model is first trained across a set of source edge nodes by a federated meta-learning approach, and then it is rapidly adapted to learn a new task at the target edge node, using a few samples only. Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge. To combat against the vulnerability of meta-learning algorithms to possible adversarial attacks, we further propose a robust version of the federated meta-learning algorithm based on distributionally robust optimization, and establish its convergence under mild conditions. Experiments on different datasets demonstrate the effectiveness of the proposed Federated Meta-Learning based framework.
Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy

The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin-eosin (H&E)-stained formalin-fixed primary breast tumor tissue sections, weakly supervised by ERBB2 gene amplification status. The gene amplification was determined by chromogenic in situ hybridization (CISH). The training data comprised digitized tissue microarray (TMA) samples from 1,047 patients. The correlation between the deep learning-predicted ERBB2 status, which we call H&E-ERBB2 score, and distant disease-free survival (DDFS) was investigated on a fully independent test set, which included whole-slide tumor images from 712 patients with trastuzumab treatment status available. The area under the receiver operating characteristic curve (AUC) in predicting gene amplification in the test sets was 0.70 (95% CI, 0.63-0.77) on 354 TMA samples and 0.67 (95% CI, 0.62-0.71) on 712 whole-slide images. Among patients with ERBB2-positive cancer treated with trastuzumab, those with a higher than the median morphology-based H&E-ERBB2 score derived from machine learning had more favorable DDFS than those with a lower score (hazard ratio [HR] 0.37; 95% CI, 0.15-0.93; P = 0.034). A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.