Reviewerly/exHarmony huggingface dataset:
FedSuper: A Byzantine-Robust Federated Learning Under Supervision

Federated Learning (FL) is a machine learning setting where multiple worker devices collaboratively train a model under the orchestration of a central server, while keeping the training data local. However, owing to the lack of supervision on worker devices, FL is vulnerable to Byzantine attacks where the worker devices controlled by an adversary arbitrarily generate poisoned local models and send to FL server, ultimately degrading the utility (e.g., model accuracy) of the global model. Most of existing Byzantine-robust algorithms, however, cannot well react to the threatening Byzantine attacks when the ratio of compromised worker devices (i.e., Byzantine ratio) is over 0.5 and worker devices’ local training datasets are not independent and identically distributed (non-IID). We propose a novel Byzantine-robust Fed erated Learning under Super vision (FedSuper), which can maintain robustness against Byzantine attacks even in the threatening scenario with a very high Byzantine ratio (0.9 in our experiments) and the largest level of non-IID data (1.0 in our experiments) when the state-of-the-art Byzantine attacks are conducted. The main idea of FedSuper is that the FL server supervises worker devices via injecting a shadow dataset into their local training processes. Moreover, according to the local models’ accuracies or losses on the shadow dataset, we design a Local Model Filter to remove poisoned local models and output an optimal global model. Extensive experimental results on three real-world datasets demonstrate the effectiveness and the superior performance of FedSuper, compared to five latest Byzantine-robust FL algorithms and two baselines, in defending against two state-of-the-art Byzantine attacks with high Byzantine ratios and high levels of non-IID data.
A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption

Abstract Anomaly detection for the Internet of Things (IoT) is a very important topic in the context of cyber-security. Indeed, as the pervasiveness of this technology is increasing, so is the number of threats and attacks targeting smart objects and their interactions. Behavioral fingerprinting has gained attention from researchers in this domain as it represents a novel strategy to model object interactions and assess their correctness and honesty. Still, there exist challenges in terms of the performance of such AI-based solutions. The main reasons can be alleged to scalability, privacy, and limitations on adopted Machine Learning algorithms. Indeed, in classical distributed fingerprinting approaches, an object models the behavior of a target contact by exploiting only the information coming from the direct interaction with it, which represents a very limited view of the target because it does not consider services and messages exchanged with other neighbors. On the other hand, building a global model of a target node behavior leveraging the information coming from the interactions with its neighbors, may lead to critical privacy concerns. To face this issue, the strategy proposed in this paper exploits Federated Learning to compute a global behavioral fingerprinting model for a target object, by analyzing its interactions with different peers in the network. Our solution allows the training of such models in a distributed way by relying also on a secure delegation strategy to involve less capable nodes in IoT. Moreover, through homomorphic encryption and Blockchain technology, our approach guarantees the privacy of both the target object and the different workers, as well as the robustness of the strategy in the presence of attacks. All these features lead to a secure fully privacy-preserving solution whose robustness, correctness, and performance are evaluated in this paper using a detailed security analysis and an extensive experimental campaign. Finally, the performance of our model is very satisfactory, as it consistently discriminates between normal and anomalous behaviors across all evaluated test sets, achieving an average accuracy value of 0.85.
A feasibility analysis of image approximation with image quality assessments

Abstract With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications.
Storytelling as <i>Inverse</i> Inverse Planning

Abstract Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by this “journey?” Recently, literary theorist Karin Kukkonen proposed that storytelling is “probability design:” the art of giving an audience pieces of information bit by bit, to craft the journey of their changing beliefs about the fictional world. A good “probability design” choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as inverse inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary “naïve planning.” Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.
Dynamic smooth and stable obstacle avoidance for unmanned aerial vehicle based on collision prediction

Abstract Autonomous dynamic obstacle avoidance of unmanned aerial vehicles (UAVs) based on collision prediction is critical for stable flight missions. UAVs must deal with high‐speed and nonlinearly moving obstacles that obey nonlinear dynamics, which are one of the numerous objects affecting stable flight. To satisfy the requirements of smooth flight and attitude stability, a novel dynamic smooth obstacle avoidance (QVA) method based on obstacle trajectory prediction is proposed. To predict the dynamic obstacle trajectories, a trajectory prediction (QLTP) method using a quasi‐linear parameter varying representations is proposed. The proposed QVA integrates the QLTP approach, velocity obstacle (VO) approach, and artificial potential field (APF) methods. The QVA detects an imminent collision based on the QLTP method, and then replans the UAV path based on the VO method at the time of the predicted collision. The UAV tracks planned waypoints for collision avoidance. To ensure the flight safety of the UAV, a virtual APF is constructed with waypoints as local targets and obstacles. The simulation results show that the proposed method performs better than the improved APF and VO methods in terms of the smoothness of the obstacle avoidance path and the stability of the UAV attitude.
FDDKT:A Novel Knowledge Tracing Method by Fusing Difficulty and Discrimination for Intelligent Education

In the field of intelligent education, knowledge tracing (KT), which can estimate and trace students’ grasp of knowledge concepts to provide high-quality education, is drawing increasing attention. Recent studies use flexible deep neural network models for knowledge tracing task and have gain good results. However, most of the existing studies ignore the influence of difficulty factor and discrimination factor on knowledge tracing task. In fact, the different difficulty of the same knowledge concept and the difference in the degree of discrimination of the question also indicate the degree of students’ mastery of knowledge. In this paper, we first fusing question difficulty and question differentiation into the student answer record, and then propose a novel knowledge tracing model(called FDDKT) based on the question difficulty and discrimination embedding, so as to make more use of the rich information. We calculate the questoin difficulty and discrimination respectively through the existing datasets, and embed them into the representation, and then we integrate them through a linear layer to process the features. In addition, we use a monotonic attention mechanism to connect the learner’s responses to evaluation questions at different time periods. In other words, the attention weight of the current problem on past problems depends not only on the similarity between query and key, but also on their relative temporal distance. Experiments on several real world benchmark datasets and show that FDDKT has good predictive performance, which demonstrates the difficulty of knowledge concept and discrimination of question are not ignored factors.
Diagnosis of Breast Cancer Using the Harris Extraction Algorithm in the Processing of Medical Images

Background and Aim: Identifying and demarcating the masses and diagnosing the disease in breast tissue is a serious challenge in diagnosing this cancer. Mammography is currently the most common method to diagnose breast cancer, in which incorrectly identifying the masses can lead to misdiagnosis or sampling of breast tissue. In this study, using feature extraction in medical image processing, we tried to make a diagnosis with better accuracy than in the past.
Clause and Literal Selection Strategies Based on Complementary Pair Distribution for Contradiction Separation Deduction

The automated deduction mechanism based on contradiction separation is a novel deduction mechanism, which has some outstanding features, such as dynamicity, multi-clauses involving, and synergy. In order to better utilize the synergy between clauses of this state-of-art automated deduction mechanism, this paper proposes two types of strategies for clause and literal selection during the deduction process. The first one is clause selection strategy based on complementary pairs in unit clauses, which consider mainly the synergized effect between non-unit clause and unit clause. The second type consists of several literal and clause selection strategies based on distribution of complementary pairs and deduction distance, which consider mainly the synergized effect of non-unit clauses. We integrate the proposed second type of strategies into CSE1.5, which is the latest version of contradiction separation based automated theorem prover, and denote the new prover as CSE1.5_CD1. Similarly, integrating both the two types of strategies generates CSE1.5_CD2. We then test CSE1.5_CD1, CSE1.5_CD2 and CSE1.5 on the theorems of CADE ATP system competition (CASC-28) FOF division. CSE1.5_CD1 can solve 5 more theorems than that solved by CSE1.5 while CSE1.5_CD2 solved 11 more. Experimental results show that the integration of the proposed strategies can enhance the capability of CSE1.5 to a certain extent.
A Trustworthiness Fuzzy Evaluation Model for Open Source Community

With the frequent occurrence of security issues in open-source software, the public is gradually paying attention to the trustworthiness of open-source software. As the cradle of open-source software, the open-source community affects the quality and security of open-source software. Current researches about trustworthiness in open-source communities mainly focus on single attribute, rather than conducting a comprehensive analysis from a macro perspective. This paper proposes an open-source community trustworthiness measurement model that combines multi-level fuzzy comprehensive evaluation method with trustworthiness attribute hierarchical model. The model includes five attributes: support, activeness, standardization, maturity, and community team. This study is the first to propose a comprehensive trustworthiness model for open-source community, reflecting the multi-dimensional attributes of open-source community. At the same time, We construct membership functions to optimize experts’ evaluations of open-source community. Finally, this paper takes OpenEuler community as an empirical object and uses the fuzzy comprehensive evaluation model proposed in this paper to measure and verify the practicality of the method.
An Abstraction Neural Network Generator for Efficient Formal Verification

Deep neural networks have been increasingly used in safety-critical systems as an important component. Most methods of verifying neural networks are difficult to apply to large neural networks because of the complexity of the neural network. Abstraction has proven to be an effective way to improve scalability. A challenging problem is that the output precision is negatively correlated with the degree of abstraction. In this paper, we propose a Distance-Based Merging (DBM) operator to optimize the abstract operation. Also, we propose a Dynamic-Merging Abstraction (DMA) algorithm to heuristically merge the network. Experimental results show that our method outperforms other state-of-the-art methods in terms of output precision.
Comparison and Implementation of ROS-Based SLAM and Path Planning Methods

The article is based on the ROS robot platform and introduces four SLAM algorithms, namely Gmapping, Hector, Cartographer, and Karto. It also introduces the global path planning algorithm and local path planning algorithm that cooperate with its navigation. The global path planning algorithm here uses Dijkstra and A*, while the local path planning algorithm uses DWA and TEB. After introducing the hardware equipment and experimental environment required for the implementation of this article's testing, the article utilized the above techniques to conduct multiple experimental tests in the actual application scenarios of this topic, accurately creating maps, completing navigation tests, and visualizing the test results on the RViz tool. By testing the robots using four different mapping methods and combining different global and local planning algorithms to obtain efficiency comparison results, the optimal combination for navigation can be obtained. Based on the experimental results, a basic service platform for SLAM and path planning based on ROS was developed, which provides further services for subsequent work.
Non-negative Matrix Factorization Method Based on Mixed Gaussian Kernels

Non-negative Matrix Factorization (NMF) is a data dimensionality reduction method, which can process large scale and high dimensional data more efficiently. Among them, kernel-based non-negative matrix factorization maps data to high-dimensional space, which is a nonlinear NMF method and is mainly used to extract high-order features of image data. The feature information obtained by different kernel functions has different characteristics and makes different contributions to image classification. However, most of the researches only consider single-kernel NMF, and some multi-kernel NMF methods also have some problems such as uneven weight distribution, which loses the feature information of some kernel functions. Therefore, it is necessary to study the NMF method of multi-kernel fusion with adaptive weights. In this paper, a non-negative matrix factorization method based on mixed Gaussian kernels is proposed. By fusing multiple Gaussian kernels with different weights and updating weights adaptively, the feature information of different Gaussian kernels can complement each other, which has good performance on public datasets FERET, ORL and Yale Face, indicating that the algorithm can effectively improve the classification performance.
Workshop#6 Transformers with spaCy and TensorFlow (Hour 11–12)

In Chap. 8 , the basic concept about Transfer Learning, its motivation and related background knowledge such as Recurrent Neural Networks (RNN) with Transformer Technology and BERT model are introduced. This workshop explores how to put these concepts and theories into practice. More importantly, is to implement Transformers, BERT Technology with the integration of spaCy’s Transformer Pipeline Technology and TensorFlow. Firstly, it gives an overview and summation on Transformer and BERT Technology. Secondly, it explores Transformer implementation with TensorFlow by revisiting Text Classification using BERT model as example. Thirdly, it introduces spaCy’s Transformer Pipeline Technology and how to implement Sentiment Analysis and Text Classification system using Transformer Technology.
Workshop#4 Semantic Analysis and Word Vectors Using spaCy (Hour 7–8)

In Chaps. 5 and 6 , we studied the basic concepts and theories related to meaning representation and semantic analysis. This workshop will explore how to use spaCy technology to perform semantic analysis starting from a revisit on word vectors concept, implement and pre-train them followed by the study of similarity method and other advanced semantic analysis.
Workshop#3 POS Tagging Using NLTK (Hour 5–6)

In Chap. 3 , we studied basic concepts and theories related to Part-of-Speech (POS) and various POS tagging techniques. This workshop explores how to implement POS tagging by using NLTK starting from a simple recap on tokenization techniques and two fundamental processes in word-level progressing: stemming and stop-word removal, which will introduce two types of stemming techniques: Porter Stemmer and Snowball Stemmer that can be integrated with WordCloud commonly used in data visualization followed by the main theme of this workshop with the introduction of PENN Treebank Tagset and to create your own POS tagger.
Workshop#2 N-grams in NLTK and Tokenization in SpaCy (Hour 3–4)

This is a coherent workshop of Chap. 2 using NTLK technology for N-gram generation and statistics. This workshop consists of two parts. Part 1 introduces N-gram language model using NLTK in Python and N-grams class to generate N-gram statistics on any sentence, text objects, whole document, literature to provide a foundation technique for text analysis, parsing and semantic analysis in subsequent workshops. Part 2 introduces spaCy, the second important NLP Python implementation tools not only for teaching and learning (like NLTK) but also widely used for NLP applications including text summarization, information extraction, and Q&A chatbot. It is a critical mass to integrate with Transformer Technology in subsequent workshops.
Improving a quantum-dot-based single-photon source with continuous measurements

We propose a technique to improve the probability of single-photon emission with an electrically pumped quantum dot in an optical microcavity by continuously monitoring the dot's energy state and using feedback to control when to stop pumping. The goal is to boost the probability of single-photon emission while bounding the probability of two or more photons. We model the system by a stochastic master equation that includes postmeasurement operations. Ideally, feedback should be based on the entire continuous measurement record, but in practice, it may be difficult to do such processing in real time. We show that even a simple threshold-based feedback scheme using measurements at a single time can improve performance over deterministic (open-loop) pumping. This technique is particularly useful for strong dot-cavity coupling with lower rates of pumping, as can be the case for electrical pumping. It is also numerically tractable since we can perform ensemble averaging with a single master equation rather than averaging over a large number of quantum trajectories.
CDFSIP Feature Selection Algorithm Based on ADA-DPC

To address the limitations of the feature selection algorithm DFSIP (Discernibility matrix based fully adaptive 2D Feature Selection based on Information gain and Pearson correlation coefficient), which utilizes a global threshold for each feature in a dataset, a new feature selection algorithm called CDFSIP (Clustering based Fully adaptive 2D DFSIP) is proposed in this study. The CDFSIP algorithm relies on the adaptive density peak clustering algorithm ADA-DPC (An Adaptive Clustering Algorithm by Finding Density Peaks) to determine the appropriate neighborhood parameter for calculating the neighborhood information of each feature, thereby establishing an adaptive threshold for each feature in CDFSIP. The algorithm evaluates the feature distribution using the Gini coefficient and decides whether to use traditional domain division or neighborhood division through clustering. This adaptive approach enables the selection of optimal neighborhood parameter for each feature, resulting in a feature subset that exhibits superior classification capability in complex scenarios. The selected feature subset is then evaluated using the K-ELM classifier. Experimental tests conducted on benchmark datasets demonstrate the effectiveness of the CDFSIP algorithm in identifying feature subsets with strong recognition capabilities. Additionally, the classifier built on the selected feature subset demonstrates excellent classification performance compared to classifiers based on feature subsets detected by other feature selection algorithms, including DFSIP, FSIP, mRMR, LLE score, DRJMIM, AVC, and AMID.
Link Prediction Based on Pairwise Proximity Preserving Graph Neural Networks

Link prediction is one of the key problems in network science and can be applied to many scenarios such as recommender system. Traditional link prediction methods are either based on heuristics or shallow network embedding methods which may either hardly generalize well or cannot optimize node representations and link probabilities simultaneously. Although the recently proposed graph neural network-based methods achieve promising performances, they do not preserve the original pairwise proximity at each hidden layer since non-linear operations may hinder it. In this paper, we propose a novel end-to-end link prediction method named Pairwise Proximity Preserving Graph neural network (PPPG), which can preserve the pairwise proximity at each layer. Specifically, inspired by graph signal denoising techniques, we preserve the structural pairwise proximity by solving an optimization problem after the non-linear operations at each layer. We evaluate our model on various standard link prediction benchmarks, and obtain competitive results.