Reviewerly/exHarmony huggingface dataset:
A Hybrid Meta-heuristic-based Linear Regression Algorithm for Live Virtual Machine Migration in Cloud Datacenters

Cloud computing provides services to its subscribers. Since there are different kinds of users that request computing resources for their applications, their resource usages obey from variable usage patterns. To manage cloud datacenters regarding to both users and providers’ viewpoints, a new system architecture for cloud datacenters is presented. This architecture embeds different modules. The core is a broker module which records resource requests for predetermined time-intervals. Then, the whale optimization algorithm (WOA) is combined with linear regression (WOALR) model that is a machine learning (ML) prediction tool to robust forecasting resource requests for short-term near future. Once the prediction model recognizes the system is subject to over-loaded or under-loaded circumstances, the live virtual machine (VM) migration approach is triggered to transfer appropriate VMs for meeting the needs. To assess the proposed prediction model, the linear regression model is hybridized with the most successful meta-heuristic algorithms. The proposed WOALR outperforms against other hybrid approaches in terms of service level agreement level (SLA) violation rates, total power usage, precluding number of additional VM migrations because of less squared error of proposed hybrid prediction model.
Navigation jamming signal recognition based on long short-term memory neural networks

This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver.The method introduces the long shortterm memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing.Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition.Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM.The performance of this method is evaluated by simulations and experiments.The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).
Creating order from the mess: web archive derivative datasets and notebooks

For a quarter-century, memory institutions have been preserving web-based content. These web archives have been collected and stored in ARC and WARC (W/ARC) file formats and will form a basis for contemporary histories. Yet, these formats present significant challenges to researchers who wish to access and use web archival data. This is primarily due to the nature of collecting, storing, and providing access to these multifaceted digital objects. In other words, web archives are messy. Applying traditional archival methods of description to digital-born collections is complicated due to issues of provenance, original order, and scale. However, we believe that archival description offers a practical starting point for thinking about access. This paper argues a robust finding aid must extend beyond basic collection-level description to allow for more meaningful interactions with web archives. As such, we propose a reimagining of a traditional finding-aid model into a three-level mode of description to include computational methods, the generation of derivative datasets, and interactive code-rich notebooks. These three factors combine to ultimately contribute to the expanded access and use of web archives.
SORAG: Synthetic Data Over-Sampling Strategy on Multi-Label Graphs

In many real-world networks of interest in the field of remote sensing (e.g., public transport networks), nodes are associated with multiple labels, and node classes are imbalanced; that is, some classes have significantly fewer samples than others. However, the research problem of imbalanced multi-label graph node classification remains unexplored. This non-trivial task challenges the existing graph neural networks (GNNs) because the majority class can dominate the loss functions of GNNs and result in the overfitting of the majority class features and label correlations. On non-graph data, minority over-sampling methods (such as the synthetic minority over-sampling technique and its variants) have been demonstrated to be effective for the imbalanced data classification problem. This study proposes and validates a new hypothesis with unlabeled data over-sampling, which is meaningless for imbalanced non-graph data; however, feature propagation and topological interplay mechanisms between graph nodes can facilitate the representation learning of imbalanced graphs. Furthermore, we determine empirically that ensemble data synthesis through the creation of virtual minority samples in the central region of a minority and generation of virtual unlabeled samples in the boundary region between a minority and majority is the best practice for the imbalanced multi-label graph node classification task. Our proposed novel data over-sampling framework is evaluated using multiple real-world network datasets, and it outperforms diverse, strong benchmark models by a large margin.
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction

The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An observation for NLP is that emotions can be communicated implicitly by referring to events, appealing to an empathetic, intersubjective understanding of events, even without explicitly mentioning an emotion name. In psychology, the class of emotion theories known as appraisal theories aims at explaining the link between events and emotions. Appraisals can be formalized as variables that measure a cognitive evaluation by people living through an event that they consider relevant. They include the assessment if an event is novel, if the person considers themselves to be responsible, if it is in line with the own goals, and many others. Such appraisals explain which emotions are developed based on an event, e.g., that a novel situation can induce surprise or one with uncertain consequences could evoke fear. We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories. To achieve that, we compile a corpus by asking people to textually describe events that triggered particular emotions and to disclose their appraisals. Then, we ask readers to reconstruct emotions and appraisals from the text. This setup allows us to measure if emotions and appraisals can be recovered purely from text and provides a human baseline. Our comparison of text classification methods to human annotators shows that both can reliably detect emotions and appraisals with similar performance. Therefore, appraisals constitute an alternative computational emotion analysis paradigm and further improve the categorization of emotions in text with joint models.
The Discrete Carnivorous Plant Algorithm with Similarity Elimination Applied to the Traveling Salesman Problem

The traveling salesman problem (TSP) widely exists in real-life practical applications; it is a topic that is under investigation and presents unsolved challenges. The existing solutions still have some challenges in convergence speed, iteration time, and avoiding local optimization. In this work, a new method is introduced, called the discrete carnivorous plant algorithm (DCPA) with similarity elimination to tackle the TSP. In this approach, we use a combination of six steps: first, the algorithm redefines subtraction, multiplication, and addition operations, which aims to ensure that it can switch from continuous space to discrete space without losing information; second, a simple sorting grouping method is proposed to reduce the chance of being trapped in a local optimum; third, the similarity-eliminating operation is added, which helps to maintain population diversity; fourth, an adaptive attraction probability is proposed to balance exploration and the exploitation ability; fifth, an iterative local search (ILS) strategy is employed, which is beneficial to increase the searching precision; finally, to evaluate its performance, DCPA is compared with nine algorithms. The results demonstrate that DCPA is significantly better in terms of accuracy, average optimal solution error, and iteration time.
Verifier’s Dilemma in Ethereum Blockchain: A Quantitative Analysis

A blockchain is an immutable ledger driven by a distributed consensus protocol. In public blockchains such as Bitcoin and Ethereum consensus is established through a computational effort called Proof-of-Work (PoW). Special users called miners contribute to the PoW computational effort in exchange for a fee and also verify the data stored in blocks mined by the other miners. Here is where the Verifier's Dilemma emerges. To maximise their profits, miners are incentivized to invest their resources in PoW, because they do not receive any incentives for the verification phase. In this paper, we study the Verifier's Dilemma using a quantitative model based on PEPA. The analysis demonstrates the circumstances under which non-verifying miners gain fees higher than that of verifying miners. Moreover, we consider a mitigation approach consisting of the injection of invalid blocks to disturb the mining process of non-verifying miners. The model allows us to derive the optimal rate at which invalid blocks must be injected, so that skipping the verifying phase becomes economically disadvantageous while the throughput of the blockchain is only minimally reduced. The impact on miners' rewards and overall performance is also assessed.
AI at Home: An Urgent Urban Policy and Research Agenda

This article makes the case for considering conversational AI devices a critical urban challenge, particularly as the home gains importance in everyday life. Drawing on excerpts from conversations with Alexa – the market-leading digital voice assistant designed and manufactured by Amazon – the article illustrates how this device’s friendly feminine personality masks significant environmental and gender effects. Building on the author’s ongoing research on the smart home, the article considers how the “feminist reboot” proposed by Strengers & Kennedy in The Smart Wife could provide promising routes for urban scholars seeking to disrupt and intervene in the troubling trajectories of feminised AI.
Deflection monitoring of morphing winglet by binocular vision system with environment adaptability

In this paper, a method with environment adaptability for measuring the deflection angle of morphing winglet based on binocular vision system is proposed. The measurement accuracy of the binocular vision system under the different imaging environment is studied. The circular luminous marks are arranged on the surface of the winglet as detection targets. The edges of luminous marks are detected by the Anisotropic Gaussian Kernel function. Ellipse fitting is carried out on the edges to obtain the center points of the circular marks. According to the coordinates of the luminous mark center, the winglet angles which is changed from −14° to 20° can be obtained. Experimental results of winglet deflection show that the three-dimensional coordinate error of luminous marks is less than 1.2 mm. The angular deviation under different light conditions is less than 0.1°. The fixed angular deflection test show that the maximum repetition error is less than 0.50 mm. The method proposed in this paper can accurately get the angular deflection of the winglet under different light environments.
Collaborative Learning with Unreliability Adaptation for Semi-Supervised Image Classification

Constructing training goals for unlabeled data is crucial for image classification in the semi-supervised setting. Consistency regularization typically encourages a model to produce consistent predictions with the given training goals, while unreliability adaptation aims to learn the transition probabilities from model predictions to training goals, instead of enforcing their consistency. In this paper, we present a model of Collaborative learning with Unreliability Adaptation (CoUA), in which multiple constituent networks collaboratively learn with each other by adapting their predictions. Toward this end, an additional adaptation module is incorporated into each network to learn a transition probability from its own prediction to that of the paired network. Therefore, the networks can exchange training experience, without being overly sensitive to the unreliability of predictions. To further enhance the collaborative learning, each network is encouraged to produce consistent predictions with the consensus results, while being resistant to the adversarial perturbations against others. Therefore, the networks are able to mutually reinforce each other. We perform extensive experiments on multiple image classification benchmarks to verify the superiority of the co-adaptation based collaborative learning mechanism.
SiSL-Net: Saliency-guided self-supervised learning network for image classification

The emergent studies and success on contrastive self-supervised learning have been well verified in the pretext task of instance discrimination, which learns visual representations by maximizing agreement between different augmented views of the same image sample (positive pairs). However, randomly cropping on original images may cause that the augmented view contains interference from a large proportion of the backgrounds, referred to as noisy data. Aiming to optimize the data augmentation and improve positive pairs, a Saliency-Augmented Module is proposed to obtain the augmented views, which only contain the ”latent” object area, referred to as clean data. Furthermore, a Saliency-Guided Self-Supervised Learning Network (SiSL-Net) is constructed as a new pattern of contrastive learning. A symmetric structure of trunk net and branch net is trained to learn a feature mapping from the clean data space and the noisy data space. Besides, a novel loss function is designed, including the embedding contrastive loss and distribution consistency loss, to optimize the feature representations during network training. The linear classification performance of our SiSL-Net is evaluated on the miniImageNet dataset with ResNet-50. Experiments show that our method achieves the top-1 accuracy from 64.67% to 69.02%, outperforming the state-of-the-art performance.
Leveraging convergence behavior to balance conflicting tasks in multi-task learning

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjusts the importance of each task during backpropagation. The result of this method is to give more attention to tasks that are diverging or not being benefited during the last iterations, ensuring that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-the-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances.
KDM: A knowledge-guided and data-driven method for few-shot video action recognition

Few Shot-Video Action Recognition (FS-VAR) has recently aroused great interest with the rise of meta-learning. The generalization ability of complex meta-learning is limited despite that it is effective and prevalent. In this work, we propose a knowledge-guided and data-driven method for FS-VAR, termed as KDM. FV-R(2 + 1) D is initiated in the paper as the feature extraction architecture, which adopts the self-attention mechanism of BERT to cope the time-series in the video and takes full advantage of the data in the training set to guide the construction of support set features. Meanwhile, a transductive inference is incorporated into the N-way K-shot task of FS-VAR, that is, the samples of query set are taken as data-driver, and the statistical information of unlabeled samples in the task is utilized to optimize a fusion loss. Our claim is supported by conducting extensive experiments on three datasets (Kinetics, Something-Something-v2 (SSv2) and HMDB51) that our proposal outperfoms the state-of-the-art FS-VAR methods (more than 10% average improvement on all settings). In more challenging and realistic FS-VAR scenario, three powerful benchmarks (more ways, more shots and domain shift) are presented, which can be used as benchmarks for future research.
Joint posterior inference for latent Gaussian models with R-INLA

Efficient Bayesian inference remains a computational challenge in hierarchical models. Simulation-based approaches such as Markov Chain Monte Carlo methods are still popular but have a large computational cost. When dealing with the large class of Latent Gaussian Models, the INLA methodology embedded in the R-INLA software provides accurate Bayesian inference by computing deterministic mixture representation to approximate the joint posterior, from which marginals are computed. The INLA approach has from the beginning been targeting to approximate univariate posteriors. In this paper, we lay out the development foundation of the tools for also providing joint approximations for subsets of the latent field. These approximations inherit Gaussian copula structure and additionally provide corrections for skewness. The same idea is carried forward also to sampling from the mixture representation, which we now can adjust for skewness.
AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching

Subgraph matching is to find all subgraphs in a data graph that are isomorphic to an existing query graph. Subgraph matching is an NP-hard problem, yet has found its applications in many areas. Many learning-based methods have been proposed for graph matching, whereas few have been designed for subgraph matching. The subgraph matching problem is generally more challenging, mainly due to the different sizes between the two graphs, resulting in considerable large space of solutions. Also the extra edges existing in the data graph connecting to the matched nodes may lead to two matched nodes of two graphs having different adjacency structures and often being identified as distinct objects. Due to the extra edges, the existing learning based methods often fail to generate sufficiently similar node-level embeddings for matched nodes. This study proposes a novel Adaptive Edge-Deleting Network (AEDNet) for subgraph matching. The proposed method is trained in an end-to-end fashion. In AEDNet, a novel sample-wise adaptive edge-deleting mechanism removes extra edges to ensure consistency of adjacency structure of matched nodes, while a unidirectional cross-propagation mechanism ensures consistency of features of matched nodes. We applied the proposed method on six datasets with graph sizes varying from 20 to 2300. Our evaluations on six open datasets demonstrate that the proposed AEDNet outperforms six state-of-the-arts and is much faster than the exact methods on large graphs.
Evaluating Virtual Hand Illusion through Realistic Appearance and Tactile Feedback

We conducted a virtual reality study to explore virtual hand illusion through three levels of appearance (Appearance dimension: realistic vs. pixelated vs. toon hand appearances) and two levels of tactile feedback (Tactile dimension: no tactile vs. tactile feedback). We instructed our participants to complete a virtual assembly task in this study. Immediately afterward, we asked them to provide self-reported ratings on a survey that captured presence and five embodiment dimensions (hand ownership, touch sensation, agency and motor control, external appearance, and response to external stimuli). The results of our study indicate that (1) tactile feedback generated a stronger sense of presence, touch sensation, and response to external stimuli; (2) the pixelated hand appearance provided the least hand ownership and external appearance; and (3) in the presence of the pixelated hand, prior virtual reality experience of participants impacted their agency and motor control and their response to external stimuli ratings. This paper discusses our findings and provides design considerations for virtual reality applications with respect to the realistic appearance of virtual hands and tactile feedback.
Adaptive Neural-PID Visual Servoing Tracking Control via Extreme Learning Machine

The vision-guided robot is intensively embedded in modern industry, but it is still a challenge to track moving objects in real time accurately. In this paper, a hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) is proposed for dynamic visual tracking of the manipulator. The scheme extracts line features on the image plane based on a laser-camera system and determines an optimal control input to guide the robot, so that the image features are aligned with their desired positions. The observation and state–space equations are first determined by analyzing the motion features of the camera and the object. The system is then represented as an autoregressive moving average with extra input (ARMAX) and a valid estimation model. The adaptive predictor estimates online the relevant 3D parameters between the camera and the object, which are subsequently used to calculate the system sensitivity of the neural network. The ELM–PID controller is designed for adaptive adjustment of control parameters, and the scheme was validated on a physical robot platform. The experimental results showed that the proposed method’s vision-tracking control displayed superior performance to pure P and PID controllers.