Reviewerly/exHarmony huggingface dataset:
Mind your MANRS

Mutually Agreed Norms on Routing Security (MANRS) is an industry-led initiative to improve Internet routing security by encouraging participating networks to implement a series of mandatory or recommended actions. MANRS members must register their IP prefixes in a trusted routing database and use such information to prevent propagation of invalid routing information. MANRS membership has increased significantly in recent years, but the impact of the MANRS initiative on the overall Internet routing security remains unclear. In this paper, we provide the first independent look into the MANRS ecosystem by using publicly available data to analyze the routing behavior of participant networks. We quantify MANRS participants' level of conformance with the stated requirements, and compare the behavior of MANRS and non-MANRS networks. While not all MANRS members fully comply with all required actions, we find that they are more likely to implement routing security practices described in MANRS actions. We assess the relevance of the MANRS effort in securing the overall routing ecosystem. We found that as of May 2022, over 83% of MANRS networks were conformant to the route filtering requirement by dropping BGP messages with invalid information according to authoritative records, and over 95% were conformant to the routing information facilitation requirement, registering their resources in authoritative databases.
Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders

We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). DC-VAE detects anomalies in MTS data through a single model, exploiting temporal and spatial MTS information. We showcase DC-VAE in different MTS datasets, and portray its future application in a continual learning framework, exploiting the generative properties of the underlying generative model to deal with continuously evolving data, avoiding catastrophic forgetting. We showcase the functioning of DC-VAE in the event of concept drifts, and propose the application of a novel approach to generative-driven continual learning, introducing the Deep Generative Replay model.
3D Object detector: A multiscale region proposal network based on autonomous driving

Recently, 3D object detection by point cloud data processing has been applied to robotics and autonomous driving because of the popularity of the LiDAR sensors. Point cloud data contain the depth and geometric space information of an object as compared with the 2D images, and achieve high precision for classification and location. In the traditional processing of point cloud data, the disorder and sparsity of the points are significant problems. In addition, the traditional detector can only support processing a limited number of point clouds. Thus, it is difficult to detect objects using a large number of point clouds. However, the previous methods need to sample the point cloud data into a coarser type, so they cannot avoid the loss of information and the accuracy is affected, as seen in in PV-RCNN. In this paper, we propose a multiscale feature fusion detector called multiscale region proposal networks (MS-RPNs), which can provide multiscale prediction results for difficult category objects. Meanwhile, our method can improve the detection accuracy for smaller objects with the optimal processing of the multiscale feature extraction module. The efficiency and accuracy of the multiscale region proposal network on the KITTI 3D object detection datasets was evaluated using numerous experiments.
PPT: A privacy-preserving global model training protocol for federated learning in P2P networks

The concept of Federated Learning (FL), which is vital to the development of machine learning, has emerged as a convergence of machine learning, information, and communication technology. However, general FL settings cannot meet requirements in decentralized environments, especially in peer-to-peer (P2P) networks, where a fully connected central server is unavailable due to limited communication ranges. In this paper, to satisfy the requirements of security, privacy preservation, and robustness in the context of FL in P2P networks, we propose a decentralized global model training protocol, named PPT. Particularly, PPT aggregates local model update parameters in a single-hop manner and uses the symmetric cryptosystem to ensure secure communications between network nodes where an enhanced Eschenauer-Gligor (E-G) scheme is proposed for secure key distribution. Further, PPT generates random noise for privacy preservation without reducing the model accuracy since the noise is eliminated ultimately. PPT also adopts game theory to resist collusion attacks. In addition, PPT has elaborate designs in terms of communication efficiency and dropout-robustness. Through extensive analysis, we demonstrate that PPT can resist various security threats and preserve user privacy. Ingenious experiments on Trec05, Trec06p, Trec07, and SMS Spam Collection v.1 confirm the 20× and 12× improvement of computation efficiency that PPT achieves compared to Google’s Secure Aggregation and Local Differential Privacy (LDP)-based FL methods. More importantly, the global model trained by PPT is better than that trained by LDP-based FL methods in terms of prediction performance (about 14% and 1% improvement in convergence rate and accuracy).
Recommendations with residual connections and negative sampling based on knowledge graphs

A knowledge graph (KG) contains a large amount of well-structured external triple information that can effectively solve the problems of poor interpretability in collaborative filtering. Recently, recommendation system (RS) models relying on graph neural networks (GNNs) have been widely developed, but the increase of GNN layers inevitably leads to over-smoothing problems. Meanwhile, most of the current KG-based negative sampling strategies randomly collect negative samples from unobserved data to train RS models. However, these strategies are insufficient to generate negative samples reflecting genuine user demands. To overcome these obstacles, we design a model called K nowledge G raph R esidual N egative S ampling Recommendation (KGRNS), which utilizes residual connections and pooling operation to alleviate the over-smoothing problem, and generate high-quality negative samples by negative sampling. Specifically, we devise residual connections on each output layer of the GNN and then utilize sum pooling operation to mitigate the effects of the over-smoothing problem on the model. In addition, to generate high-quality negative samples, we create a gated strategy to mix the knowledge of both positive and negative samples to generate synthetic negative samples and then select the virtual negative sample that is closest to the positive ones through a theoretically backed hard negative sample select strategy. We conducted broad experiments on three datasets. The experimental results showed that KGRNS performed considerable enhancements over state-of-the-art methods. Ablation studies validated the effectiveness of each part of the KGRNS. • Utilizing residual connections and sum pooling operation to alleviate the over-smoothing problem. • Designing a gated strategy to mix the knowledge of both positive and negative samples. • Selecting virtual negative sample through we designed negative sample select strategy. • The extensive experiments demonstrate the state-of-the-art performance of KGRNS.
DFSGAN: Introducing editable and representative attributes for few-shot image generation

Training generative adversarial networks (GANs) usually requires large-scale data and massive computation resources. The performance of GANs plummets when given limited data due to the discriminator overfitting, thus providing meaningless feedback to the generator during the adversarial training. Existing few-shot GANs are primarily concerned with transferring knowledge from models that have been pre-trained on large-scale datasets or using data augmentation to expand the training sets. However, previous methods consistently take latent codes sampled from a single distribution as the generator’s input. We contend that more complicated latent codes can provide the generator with more editable attributes. In this paper, we propose DFSGAN for few-shot image generation, which takes dynamic Gaussian mixture (DGM) latent codes as the generator’s input. Our DFSGAN can select the Gaussian components of the latent codes quantitatively. We also design two techniques to strengthen the representative ability of intermediate features of the generating process to improve the fidelity and maintain the content and layout information of the synthesized images. Our DGM and intermediate representation enhancement techniques complement each other and improve synthesis quality. We conduct extensive experiments on 15 few-shot datasets with different resolutions spanning from art paintings to realistic photos. Qualitative and quantitative results demonstrate the superiority and effectiveness of our model.
Convergence analysis of Particle Swarm Optimization in one dimension

This paper is devoted to a rigorous analysis of one dimensional Particle Swarm Optimization (PSO) for non convex objective function. We provide a quantitative bound on the error between the equilibrium point and the global minimizer of the given objective function. Our strategy does not rely on the Laplace principle. We also investigate the underdamped PSO with linear objective function.
Factors influencing the bioavailability of some selected elements in the agricultural soil of a geologically varied territory: The Campania region (Italy) case study

Bioavailability of some major and trace elements was evaluated in 1,993 topsoil samples collected across Campania region (Southern Italy). A main focus was made on Al, Ca, K, Mg, Cu, Tl since they are linked, for different reasons, to agriculture. Bioavailability was assessed by an extraction with ammonium nitrate and the data were compared with the pseudo-total concentration determined by Aqua Regia digestion. Geochemical maps of the pseudo-total and bioavailable concentrations were generated using a multifractal inverse distance weighted (MIDW) interpolation. In addition, the spatial distribution patterns of the percent bioavailability of elements, based on the ratio among bioavailable and the pseudo-total fractions, were also determined. The median value of the percent bioavailability showed the order Ca > K ≫ Mg ≃ Tl ≫ Cu ≫ Al and it represents a positive finding in terms of both agricultural productivity and environmental quality. Further, a multiple linear regression was finally applied to data to unveil any dependence of the bioavailable fraction on the pseudo-total content of elements. The grain size distribution and organic matter content of samples were later included to evaluate their possible role in promoting the environmental availability of elements. The pseudo-total concentrations of Al, Ca, K, and Mg alone resulted to be poorly able to predict the variability of the bioavailable fraction. The addition of the grain size distribution and organic matter content to the models expanded the predictive capability of Ca, K, and Mg whereas a marginal improvement was showed by Al, Cu, and Tl. This study represents a methodological contribution to a better understanding of the processes underlying the spatial variability of chemical elements in soil. Considering the positive outcomes obtained, further researches were planned to include more variables (e.g. soil pH, redox potential, content in Iron and Manganese oxides, etc.) in the predictive models.
Mobile Technology Application in Aviation: Chatbot for Airline Customer Experience

Effective communication between customers and businesses is crucial. Enhancing the communication between customer and company are non-ending efforts that require continuous improvement and approach. In the emergence of new technologies, the transmission of information through technology as a platform adds to more challenging initiatives performed. More enterprises adopt artificial intelligence (AI) to increase operational efficiency, eliminate costly errors, and increase customer satisfaction. Time spent by passengers interacting with airlines is minimized through the use of a practical application that supports their needs, integrated with the natural language processing, conversational agents, or Chatbot's serving as virtual assistants. Artificial intelligence shall assist airline customers in acquiring more accurate related information such as flight booking, schedules, and updates. This chapter offers a multi-focus discussion on initiatives for applying Chatbot systems in the aviation sector, a debate on artificial intelligence technology used in improving communication, enhancing natural language interactions, and the usability response from selected airlines passengers' feedback on the improved systems.
Improved broad learning system for machinery intelligent fault diagnosis with increasing fault samples, fault modes, and running conditions

Intelligent fault diagnosis (IFD) plays an indispensable role in protecting machinery from catastrophic accidents. Existing IFD methods are mainly developed in the framework of one-time learning. Therefore, they work under the hypothesis of complete dataset. Nevertheless, it is unrealistic to gain the complete dataset of machinery faults at once. More practically, new data will be progressively acquired over time. Therefore, it is urgently required to develop the incremental learning (IL) capabilities for IFD models to learn new knowledge continually from new data. For this purpose, this study proposes an improved broad learning system (IBLS) for lifelong learning IFD. Firstly, the initial IBLS is constructed based on the original broad learning system (BLS). Then, the IL capabilities of the IBLS are developed for three scenarios: increasing fault samples, increasing fault modes, and increasing running conditions. Based on these IL capabilities, the IBLS can be progressively updated to learn more and more diagnosis functions. Finally, the effectiveness of the proposed IBLS is verified using three experiments of high-speed train bearing, disc component, and Case Western Reserve University bearing. The results show that the IBLS is capable of learning continually new knowledge from new data. Besides, the diagnosis accuracy of the IBLS is 12.45%, 7.84%, and 5.10% higher than that of the original BLS in the three case studies. The satisfying results prove that the proposed IBLS is a useful method to solve the lifelong learning IFD problem.
Hate and Aggression Analysis in NLP with Explainable AI

Social platforms such as Twitter and Facebook have now become only media to express their thoughts, and due to lack of censorship, it often embellishes themselves as an abode for hate towards minorities. People of color, Asian people, Muslims, women, transgenders, and LGBTQ+ communities are often the target of such online hate and aggression. Though several companies have incorporated considerable algorithms on their platforms, nevertheless due to being rather hard to often detect such comments still make it to the platforms, creating a negative space towards targeted people. This research involves the study and comparison of different hate and aggression detection algorithms with intent on two languages, i.e. English and German including machine learning models (linear SVC, logistic regression, multinomial naive Bayes and random forests) with their variations with feature engineering and bag of words and deep learning (CNN-GRU static, TCN static, Seq2Seq) with their variations vis-à-vis Word2Vec embedding. CNN+GRU static + Word2Vec embedding has outperformed all the other techniques with an accuracy of 68.29%.
A novel soft-coded error-correcting output codes algorithm

1 A new ECOC encoding algorithm is proposed, which separates classes into two groups by maximizing the ratio of the inter-group distance to intra-group distance. 2 A novel Soft-Coded (SC) ECOC algorithm is proposed by softening the codewords of the ECOC codematrix by considering the subordination degree of each class to both groups. 3 A new measure coverage is designed to evaluate the subordination degrees of different classes to the positive and the negative groups, and the values are set as the elements of the codematrix. 4 A self-adaptive strategy is designed to adjust codewords, making them better fit the associated learners. Error-Correcting Output Codes (ECOC) algorithms enable multiclass classification by reassigning multiple classes to the positive/negative group with the class reassignment schemes being recorded as binary/ternary hard-coded (HC) codematrices. Different classes tend to get diverse subordination degrees to the positive/negative group, providing clues to correct potential errors. However, the HC codematrices are unable to provide the information in the subordination degrees. In this paper, a Soft-Coded ECOC (SC-ECOC) scheme, namely, the Sequential Forward Floating Selection algorithm, is proposed by filling codematrices with real values instead of hard codes to improve classification performance. This algorithm divides multiple classes into two groups by maximizing the ratio of inter-group distance to intra-group distance. Then a new measure coverage is designed to evaluate the subordination degrees of different classes to both groups, which are set as the elements to form a codematrix. Furthermore, a self-adaptive strategy adjusts the value of each element to fit learners better. Experiments are carried out to verify the performance of our algorithm on various data sets, and results confirm that our algorithm can achieve more balanced results compared with the traditional HC ECOC algorithms. Besides, the values of soft codes correlate with the difficulty level of various classes to improve the multiclass classification ability.
ProgramTransformer: A tool for generating semantically equivalent transformed programs

We propose a tool, referred to as ProgramTransformer, to generate new transformed programs that aim at maintaining the semantics of original programs with different syntactical changes. Given the input and output directories, the tool applies a set of transformations to the methods of programs in the input directory and saves the generated transformed programs into the output directory. The goal is to evaluate the generalizability and robustness of models by observing their predictions on the semantically equivalent transformed programs. Those transformed programs can additionally be useful for improving code representation, data diversity, and counterfactual reasoning of code intelligence models.
A novel method for computing self-motion manifolds

For kinematically redundant robots, self-motion manifolds that represent all the inverse kinematic solutions for a given end-effector location play a significant role in global motion planning. However, the efficient computation of self-motion manifolds, especially the computation of high-dimensional manifolds, is still a problem that needs to be solved. In this paper, the grid elements modeling criteria are formulated, and the specific modeling method of a grid element in the configuration space is developed. Based on the idea of cellular automata, the problem of computing self-motion manifolds is transformed into a dynamic model searching problem, and an evolution strategy is proposed to realize the efficient computation of self-motion manifolds. Finally, two examples are used to verify the effectiveness of the proposed method in computing high-dimensional self-motion manifolds. The results show that compared to the conventional methods, the proposed method can correctly identify all the self-motion manifolds with 94% reduced computational time on average.
PADP-FedMeta: A personalized and adaptive differentially private federated meta learning mechanism for AIoT

Powered by edge computing, the last few years have seen a rapid growth in AIoT applications. Federated learning (FL), as a typical machine learning framework for edge intelligence, has attracted a large number of attention since it can protect user privacy. However, recent studies have shown that FL cannot fully ensure privacy. To address this, differential privacy technique is widely used in FL. Nevertheless, existing works neglect that data on devices are non-independent and identically distributed (Non-IID), which largely degrades model accuracy and convergence speed. In this paper, we propose PADP-FedMeta, a personalized and adaptive differentially private federated meta learning mechanism with a provable privacy and convergence guarantee. PADP-FedMeta mitigates the negative effect of Non-IID upon model accuracy by introducing federated meta learning, and significantly improves the convergence speed with an adaptive privacy parameter. Comprehensive experimental results show the effectiveness of our mechanism and its superior performance over the state-of-the-art differentially private FL schemes.
A two-phase strategy for nonconvex clusters integrating a spectral clustering with a merging technique

In this paper we address the problem of arbitrarily shaped clustering of points belonging to a linear space. Among the several approaches to clustering proposed in literature, the spectral clustering has become more and more popular for the case of arbitrarily shaped clusters. It depends on parameters whose choice can be critical. In order to reduce these criticalities, in this paper we propose a two-phase strategy that integrates the partitioning obtained by the spectral clustering with a merging technique which makes use only of the information already produced in the first phase by the spectral clustering. The main novelty of this approach is precisely the fact that no geometric tool is exploited in the merging phase. A numerical experimentation on artificial and real-world datasets has been performed to compare the proposed method with the spectral clustering and with two other widely used algorithms for non-convex problems, namely DBSCAN and Chameleon 2.
Expressive Bodies Engaging with Embodied Disability Cultures for Collaborative Design Critiques

In our experience as researchers engaging with non-academic audiences, we observed that it remains a challenge to receive direct and critical feedback from participants. This is particularly amplified in the context of disabilities even if the researchers identify themselves as disabled given that the interaction is governed by social status and material power dimensions to say the least. To work productively with these power dynamics, we explored embodied approaches to articulating critique acknowledging the different ways of knowing stemming from different bodyminds. Here, we line out two exploratory cases illustrating how physical bodies can be directly attended to to express critiques in more direct ways than participants might be used to on a language based level (spoken or signed). We show how communication and critique can take on many forms encouraging us to broaden our methodological toolset to incorporate practices common in disability cultures. Our experiences show that we need to embrace crip approaches to knowledge production to receive more actionable and useful feedback in developing technologies with disabled communities.