Reviewerly/exHarmony huggingface dataset:
SSWS: An edge detection algorithm with strong semantics and high detectability for spacecraft

Space tasks such as docking, capture and maintenance of spacecraft in orbit are increasingly dependent on visual system. High-order features extraction, target recognition and spacecraft pose measurement are essential image processing technologies to accomplish above tasks, for which image edge detection is one of the most critical and basic techniques. Traditional edge detection algorithms such as Canny have good extraction effect, but cannot extract and obtain semantic information. Besides, most of the edge detection algorithms based on deep-learning rely on a large number of annotation labels and require huge workload. Therefore, they cannot be applied to non-cooperative space targets. In this paper, we propose an edge detection algorithm with strong semantics and weak supervision (SSWS) to combine edge detection with image semantic segmentation. By learning geometric boundaries of ellipse and quadrilateral for docking ring and solar panel of spacecraft, and using the training method of weak supervision, our algorithm achieves effective boundary detection of important parts of spacecraft from images regardless of the background interference and light intensity. Real image experiments show that SSWS can effectively extract the boundary of important parts of spacecraft and filter out the background information and noise. In the performance verification experiment of BSDS500 dataset, with weakly supervised annotation tags, SSWS obtains the best OIS (0.831), in comparison with traditional edge detection algorithms and excellent recent deep-learning edge detection algorithms in recent years; in the contrast experiment of image segmentation in PASCAL VOC 2012 test set, SSWS gets the best mIoU value (66.9%).
Diabetes and conversational agents: the AIDA project case study

Abstract One of the key aspects in the process of caring for people with diabetes is Therapeutic Education (TE). TE is a teaching process for training patients so that they can self-manage their care plan. Alongside traditional methods of providing educational content, there are now alternative forms of delivery thanks to the implementation of advanced Information Technologies systems such as conversational agents (CAs). In this context, we present the AIDA project: an ensemble of two different CAs intended to provide a TE tool for people with diabetes. The Artificial Intelligence Diabetes Assistant (AIDA) consists of a text-based chatbot and a speech-based dialog system. Their content has been created and validated by a scientific board. AIDA Chatbot—the text-based agent—provides a broad spectrum of information about diabetes, while AIDA Cookbot—the voice-based agent—presents recipes compliant with a diabetic patient’s diet. We provide a thorough description of the development process for both agents, the technology employed and their usage by the general public. AIDA Chatbot and AIDA Cookbot are freely available and they represent the first example of conversational agents in Italian to support diabetes patients, clinicians and caregivers.
Towards Feature-free TSP Solver Selection: A Deep Learning Approach

It is widely recognized that for the traveling salesman problem (TSP), there exists no universal best solver for all problem instances. This observation has greatly facilitated the research on Algorithm Selection (AS), which seeks to identify the solver best suited for each TSP instance. Such segregation usually relies on a prior representation step, in which problem instances are first represented by carefully established problem features. However, the creation of good features is non-trivial, typically requiring considerable domain knowledge and human effort. To alleviate this issue, this paper proposes a deep learning framework, named CTAS, for TSP solver selection. Specifically, CTAS exploits deep convolutional neural networks (CNN) to automatically extract informative features from TSP instances and utilizes data augmentation to handle the scarcity of labeled instances. Extensive experiments are conducted on a challenging TSP benchmark with 6,000 instances, which is the largest benchmark ever considered in this area. CTAS achieves over 2 × speedup of the average running time, compared with the single best solver. More importantly, CTAS is the first feature-free approach that notably outperforms classical AS models, showing huge potential of applying deep learning to AS tasks.
Time Aspect in Making an Actionable Prediction of a Conversation Breakdown

Online harassment is an important problem of modern societies, usually mitigated by the manual work of website moderators, often supported by machine learning tools. The vast majority of previously developed methods enable only retrospective detection of online abuse, e.g., by automatic hate speech detection. Such methods fail to fully protect users as the potential harm related to the abuse has always to be inflicted. The recently proposed proactive approaches that allow detecting derailing online conversations can help the moderators to prevent conversation breakdown. However, they do not predict the time left to the breakdown, which hinders the practical possibility of prioritizing moderators' works. In this work, we propose a new method based on deep neural networks that both predict the possibility of conversation breakdown and the time left to conversation derailment. We also introduce three specialized loss functions and propose appropriate metrics. The conducted experiments demonstrate that the method, besides providing additional valuable time information, also improves on the standard breakdown classification task with respect to the current state-of-the-art method.
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training

Neural network implementations are known to be vulnerable to physical attack vectors such as fault injection attacks. As of now, these attacks were only utilized during the inference phase with the intention to cause a misclassification. In this work, we explore a novel attack paradigm by injecting faults during the training phase of a neural network in a way that the resulting network can be attacked during deployment without the necessity of further faulting. In particular, we discuss attacks against ReLU activation functions that make it possible to generate a family of malicious inputs, which are called fooling inputs, to be used at inference time to induce controlled misclassifications. Such malicious inputs are obtained by mathematically solving a system of linear equations that would cause a particular behaviour on the attacked activation functions, similar to the one induced in training through faulting. We call such attacks fooling backdoors as the fault attacks at the training phase inject backdoors into the network that allow an attacker to produce fooling inputs. We evaluate our approach against multi-layer perceptron networks and convolutional networks on a popular image classification task obtaining high attack success rates (from 60% to 100%) and high classification confidence when as little as 25 neurons are attacked while preserving high accuracy on the originally intended classification task.
Word Sense Disambiguation System for Information Retrieval in Telugu Language

Nowadays, In Natural Language Processing (NLP), using artificial intelligence is a open challenge. Word Sense Disambiguation (WSD) is a sub field of artificial intelligence. In this research paper, WSD system is developed and validated for regional Telugu language. Many Natural Languages are having many ambiguous words. The word having more than one sense is known as ambiguous word or polysemy word. Word Sense Disambiguation is termed as the methodology of finding the appropriate sense of the ambiguity word. To develop WSD system a training dataset Lexical Knowledge Base LKB is used. In Query Expansion, WSD technique is used to increase the accuracy of the information retrieval system. In this research paper, baseline method and the proposed method Word First Sense are discussed. The evaluation metrics used are Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) metrics. The results are encouraging in the proposed method.
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data

Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization towards a target task. In this paper, we go further and extract synthetic data by leveraging the learnt model parameters. We dub them Data Impressions, which act as proxy to the training data and can be used to realize a variety of tasks. These are useful in scenarios where only the pretrained models are available and the training data is not shared (e.g., due to privacy or sensitivity concerns). We show the applicability of data impressions in solving several computer vision tasks such as unsupervised domain adaptation, continual learning as well as knowledge distillation. We also study the adversarial robustness of lightweight models trained via knowledge distillation using these data impressions. Further, we demonstrate the efficacy of data impressions in generating data-free Universal Adversarial Perturbations (UAPs) with better fooling rates. Extensive experiments performed on benchmark datasets demonstrate competitive performance achieved using data impressions in absence of original training data.
Experience in Designing for Cyber Resiliency in Embedded DoD Systems

Abstract In DoD systems, software component reuse is highly valued, and development is rarely started from scratch. Most embedded weapon systems are a heterogeneous mixture of new, modified, and legacy components. This mixture drives an increased impetus to improve upon the existing system's model, and also creates an opportunity to assess potential cyber vulnerabilities earlier in the lifecycle. In this paper, we describe our experience in developing an experimental platform, which is a representative testbed that incorporates legacy, modified, and new components, using a process where we address cyber resiliency early in development. We give accounts of where each of the parts of our representative testbed originated, what modeling decisions we made, and how we analyzed cyber vulnerabilities. We also propose a system engineering process, based on architectural modeling of the system, which introduces analysis early in the design for cyber resiliency. We are developing an approach to use advanced tools to find vulnerabilities and to develop cyber resiliency requirements to counter those vulnerabilities. The creation of the model and documentation of cyber resiliency requirements and design decisions provide a positive impact on the system lifecycle and future integration of the system.
State of the art cell detection in bone marrow whole slide images

Diseases of the hematopoietic system such as leukemia is diagnosed using bone marrow samples. The cell type distribution plays a major role but requires manual analysis of different cell types in microscopy images.Automated analysis of bone marrow samples requires detection and classification of different cell types. In this work, we propose and compare algorithms for cell localization, which is a key component in automated bone marrow analysis.We research fully supervised detection architectures but also propose and evaluate several techniques utilizing weak annotations in a segmentation network. We further incorporate typical cell-like artifacts into our analysis. Whole slide microscopy images are acquired from the human bone marrow samples and annotated by expert hematologists.We adapt and evaluate state-of-the-art detection networks. We further propose to utilize the popular U-Net for cell detection by applying suitable preprocessing steps to the annotations.Evaluations are performed on a held-out dataset using multiple metrics based on the two different matching algorithms.The results show that the detection of cells in hematopoietic images using state-of-the-art detection networks yields very accurate results. U-Net-based methods are able to slightly improve detection results using adequate preprocessing - despite artifacts and weak annotations.In this work, we propose, U-Net-based cell detection methods and compare with state-of-the-art detection methods for the localization of hematopoietic cells in high-resolution bone marrow images. We show that even with weak annotations and cell-like artifacts, cells can be localized with high precision.
Multi-Feature Broad Learning System for Image Classification

A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a “stacking with ridge regression” strategy is applied to the fusion block to get the final output of MFBLS. Experimental results show that MFBLS achieves the accuracies of 92.25%, 81.03%, and 54.66% on SVHN, CIFAR-10, and CIFAR-100, respectively, which outperforms BLS and its variants. Besides, it is even superior to the deep network, convolutional deep belief network, in both accuracy and training time on CIFAR-10. Code for the paper is available at https://github.com/threedteam/mfbls .
Generalization of reference filtering control strategy for 2D/3D visual feedback control of industrial robot manipulators

This paper develops the application of the Dual Rate Dual Sampling Reference Filtering Control Strategy to 2D and 3D visual feedback control.This strategy allows to overcome the problem of sensor latency and to address the problem of control task failure due to visual features leaving the camera field of view.In particular, a Dual Rate Kalman Filter is used to generate inter-sample estimations of the visual features to deal with the problem of vision sensor latency, whereas a Dual Rate Extended Kalman Filter Smoother is used to generate more convenient visual features trajectories in the image plane.Both 2D and 3D visual feedback control approaches are widely analyzed through the paper, as well as the overall system performance using different visual feedback controllers, providing a set of results that highlight the improvements in terms of solution reachability, robustness and time domain response.The proposed control strategy has been validated on an industrial system with hard real-time limitations, consisting of a 6 DOF industrial manipulator, a 5 MP camera and a PLC as controller.
Research on a real-time polarization compensation method for dynamic quantum communication terminals

In the experiment of quantum key distribution in free space, the polarization degradation and the propagation directions of the four polarization-encoded quantum light beams vary relative to each other and reduce the service life of the terminals, which severely limits their practical application. In order to solve the above problems, we report research on real-time polarization compensation methods for dynamic quantum communication terminals. The difference from Micius is that our proposed system emits quantum lights through the same optical fiber to ensure high-precision coaxiality, and uses a set of wave plates to compensate for both the polarization degradation caused by single-mode fiber and the polarization change caused by optical elements. We introduce our polarization compensation methods in detail and present a design for a dynamic polarization compensation scheme and experimental device. The final experimental polarization extinction ratio of our dynamic optical system is greater than 30 dB after real-time polarization compensation, which meets quantum communication requirements. The successful implementation of this method can effectively improve terminal service life and provide a foundation for the practical applications of quantum communication.
Rotation Forest-Based Logistic Model Tree for Website Phishing Detection

The emergence of web and internet technology has led to its use in a broad array of services ranging from financial to educational services. This has led to a spike in the number of cybersecurity problems over the years, the most notable of which is the phishing attack, in which malicious websites imitate legitimate websites to capture gullible users’ details needed for unauthorized access. However, current mitigation strategies, such as anti-phishing applications and Machine Learning (ML) methods, have been effective for detecting phishing activities. Hackers, on the other hand, are developing new ways to circumvent these countermeasures. Nevertheless, given the dynamism of phishing attempts, there is a continual demand for innovative and efficient solutions for website phishing detection. This study proposes a Rotation Forest-based Logistic Model Trees (RF-LMT) for website phishing detection. LMT is a technique that combines logistic regression and tree inference into a single model tree. Three datasets of different instance distributions, both balanced and imbalanced, are used to investigate the proposed RF-LMT. From the results, it was observed that LMT performed better than the selected baseline classifiers. This finding revealed that LMT can perform comparably to baseline classifiers. However, in comparison to LMT and experimented baseline classifiers, the proposed RF-LMT method showed superior performance in website phishing detection. Specifically, RF-LMT had a high detection accuracy (98.24%), AUC (0.998), f-measure (0.982) values with a low false-positive rate (0.018). Furthermore, RF-LMT outperformed existing ML-based phishing attack models. As a result, the proposed RF-LMT method is recommended for dealing with complex phishing attacks.
A comparative study of blockchain’s largest permissionless networks

Blockchain is increasingly gaining interest in both the academic and professional worlds. The implementation of this decentralised and distributed network has started taking place, firstly in the financial world and in recent years reaching to other industries. Nevertheless, not all blockchain networks are the same, and choosing the right consensus algorithm is important for companies willing to invest in this technology. Companies eager to implement blockchain should understand the underlying architecture when selecting a particular network. Through an in-depth analysis, this paper aims to explore and compare from a technical perspective the three biggest permissionless blockchain networks by market capitalisation: Bitcoin, Ethereum and Ripple. Our research shows that Bitcoin gains a competitive advantage as a widely adopted means of payment, Ethereum excels in adopting a robust and flexible smart contract functionality whilst Ripple is most suitable for cross-border payments due to its scalability and fast processing speed. Based on the comprehensive analysis of the networks and their consensus protocols, this paper is designed to set the ground for further studies on the emerging applications of blockchain in numerous sectors.
Spark‐based parallel processing whale optimization algorithm

Summary Swarm intelligence meta‐heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta‐heuristic optimization algorithms. However, complex applications might require the processing of large‐scale computations, which results in down‐scaling computational throughput of WOA. Apache Spark, a well‐known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR‐WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher‐complexity problems which require complex computations.
Raising context awareness in motion forecasting

Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history. Yet, we find that state-of-the-art forecasting methods tend to overly rely on the agent’s current dynamics, failing to exploit the semantic contextual cues provided at its input. To alleviate this issue, we introduce CAB, a motion forecasting model equipped with a training procedure designed to promote the use of semantic contextual information. We also introduce two novel metrics — dispersion and convergence-to-range — to measure the temporal consistency of successive forecasts, which we found missing in standard metrics. Our method is evaluated on the widely adopted nuScenes Prediction benchmark as well as on a subset of the most difficult examples from this benchmark. The code is available at github.com/valeoai/CAB.
New singly and doubly even binary [72,36,12] self-dual codes from M2(R)G - group matrix rings

In this work, we present a number of generator matrices of the form [I2n|τ2(v)], where I2n is the 2n×2n identity matrix, v is an element in the group matrix ring M2(R)G and where R is a finite commutative Frobenius ring and G is a finite group of order 18. We employ these generator matrices and search for binary [72,36,12] self-dual codes directly over the finite field F2. As a result, we find 134 Type I and 1 Type II codes of this length, with parameters in their weight enumerators that were not known in the literature before. We tabulate all of our findings.
Optimal layout of stacked graph for visualizing multidimensional financial time series data

In the era of big data, the analysis of multi-dimensional time series data is one of the important topics in many fields such as finance, science, logistics, and engineering. Using stacked graphs for visual analysis helps to visually reveal the changing characteristics of each dimension over time. In order to present visually appealing and easy-to-read stacked graphs, this paper constructs the minimum cumulative variance rule to determine the stacking order of each dimension, as well as adopts the width priority principle and the color complementary principle to determine the label placement positioning and text coloring. In addition, a color matching method is recommended by user study. The proposed optimal visual layout algorithm is applied to the visual analysis of actual multidimensional financial time series data, and as a result, vividly reveals the characteristics of the flow of securities trading funds between sectors.