Reviewerly/exHarmony huggingface dataset:
Combining Label Propagation and Simple Models out-performs Graph Neural Networks

Graph Neural Networks (GNNs) are now a predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful and whether they are necessary for good performance. Here, we show that for many standard transductive node classification benchmarks, we can ex-ceed or match the performance of state-of-the-art GNNs by combining shallow multilayer perceptrons models (that ignore the graph structure entirely) with two simple postprocessings for correlation in the label structure: (i) an “error correlation” that spreads residual errors in training data to correct errors in test data and(ii) an “prediction correlation” that smooths the predictions on the test data. These correlations are implemented via simple modifications to standard label propagation techniques developed in early graph-based semi-supervised learning methods. Our approach achieves state-of-the-art performance across a wide variety of benchmarks, with just a small fraction of the parameters and orders of magnitude faster runtime compared to highly-parameterized GNNs (for instance, we exceed the best known GNN performance on the OGB-Products dataset with >100x fewer parameters and >100x faster training time). The performance of our methods highlights how directly incorporating label information into the learning algorithm (as was done in traditional techniques) yields easy and substantial performance gains, and we argue that GNNs have been approximating this idea, albeit implicitly. We also incorporate label correlation into SOTA GNN models, providing modest gains.
Age- and Gender-Related Differences in Speech Alignment Toward Humans and Voice-AI

Speech alignment is where talkers subconsciously adopt the speech and language patterns of their interlocutor. Nowadays, people of all ages are speaking with voice-activated, artificially-intelligent (voice-AI) digital assistants through phones or smart speakers. This study examines participants’ age (older adults, 53–81 years old vs. younger adults, 18–39 years old) and gender (female and male) on degree of speech alignment during shadowing of (female and male) human and voice-AI (Apple’s Siri) productions. Degree of alignment was assessed holistically via a perceptual ratings AXB task by a separate group of listeners. Results reveal that older and younger adults display distinct patterns of alignment based on humanness and gender of the human model talkers: older adults displayed greater alignment toward the female human and device voices, while younger adults aligned to a greater extent toward the male human voice. Additionally, there were other gender-mediated differences observed, all of which interacted with model talker category (voice-AI vs. human) or shadower age category (OA vs. YA). Taken together, these results suggest a complex interplay of social dynamics in alignment, which can inform models of speech production both in human-human and human-device interaction.
The 1st Tiny Object Detection Challenge: Methods and Results

The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection. The TinyPerson dataset was used for the TOD Challenge and is publicly released. It has 1610 images and 72651 box-level annotations. Around 36 participating teams from the globe competed in the 1st TOD Challenge. In this paper, we provide a brief summary of the 1st TOD Challenge including brief introductions to the top three methods.The submission leaderboard will be reopened for researchers that are interested in the TOD challenge. The benchmark dataset and other information can be found at: https://github.com/ucas-vg/TinyBenchmark .
Optimal Trees Selection for Classification via Out-of-Bag Assessment and Sub-Bagging

The effect of training data size on machine learning methods has been well investigated over the past two decades. The predictive performance of tree based machine learning methods, in general, improves with a decreasing rate as the size of training data increases. We investigate this in optimal trees ensemble (OTE) where the method fails to learn from some of the training observations due to internal validation. Modified tree selection methods are thus proposed for OTE to cater for the loss of training observations in internal validation. In the first method, corresponding out-of-bag (OOB) observations are used in both individual and collective performance assessment for each tree. Trees are ranked based on their individual performance on the OOB observations. A certain number of top ranked trees is selected and starting from the most accurate tree, subsequent trees are added one by one and their impact is recorded by using the OOB observations left out from the bootstrap sample taken for the tree being added. A tree is selected if it improves predictive accuracy of the ensemble. In the second approach, trees are grown on random subsets, taken without replacement-known as sub-bagging, of the training data instead of bootstrap samples (taken with replacement). The remaining observations from each sample are used in both individual and collective assessments for each corresponding tree similar to the first method. Analysis on 21 benchmark datasets and simulations studies show improved performance of the modified methods in comparison to OTE and other state-of-the-art methods.
Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always at stake to meet the objective of local energy minimization. In this regard, Multi-access Edge Computing (MEC)-enabled FL addresses the tradeoff between the model performance and the energy consumption of the mobile devices by allowing users to offload a portion of their local dataset to an edge server for the model training. Since the edge server has high computation capability, the time consumption of the model training at the edge server is insignificant. However, the time consumption for dataset offloading from mobile users to the edge server has a significant impact on the total time consumed to complete a single round of FL process. Thus, resource management in MEC-enabled FL is challenging, where the objective is to reduce the total time consumption while saving the energy consumption of the mobile devices. In this article, we formulate an energy-aware resource management for MEC-enabled FL in which the model training loss and the total time consumption are jointly minimized, while considering the energy limitation of mobile devices. In addition, we recast the formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture the coupling constraints between the radio resource management and dataset offloading. To that end, we analyze the impact of the dataset offloading and computing resource allocation on the model training loss, time, and the energy consumption. Finally, we present the convergence analysis of the proposed solution, and evaluate its performance against the traditional FL approach. Simulation results demonstrate the efficacy of our proposed solution approach.
Challenges in Markov Chain Monte Carlo for Bayesian Neural Networks

Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, this paper shows that a nonconverged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictive distribution of the output of the neural network. Classification examples based on multilayer perceptrons showcase highly accurate posterior predictive distributions. The postulate of limited scope for MCMC developments in BNNs is partially valid; an asymptotically exact parameter posterior seems less plausible, yet an accurate posterior predictive distribution is a tenable research avenue.
Systematic generalisation with group invariant predictions

We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-trained neural network to be less reliant on more persistently-correlating complex features. When the non-persistent, simpler correlations correspond to non-semantic background factors, a neural network trained on this data can exhibit dramatic failure upon encountering systematic distributional shift, where the correlating background features are recombined with different objects. We perform an empirical study showing that group invariance methods across inferred partitionings of the training set can lead to significant improvements at such test-time situations. We suggest a new invariance penalty, showing with experiments on three synthetic datasets that it can perform better than alternatives. We find that even without assuming access to any systematic-shift validation sets, one can still find improvements over an ERM-trained reference model.
Provably Secure Hardware Masking in the Transition- and Glitch-Robust Probing Model: Better Safe than Sorry

There exists many masking schemes to protect implementations of cryptographic operations against side-channel attacks. It is common practice to analyze the security of these schemes in the probing model, or its variant which takes into account physical effects such as glitches and transitions. Although both effects exist in practice and cause leakage, masking schemes implemented in hardware are often only analyzed for security against glitches. In this work, we fill this gap by proving sufficient conditions for the security of hardware masking schemes against transitions, leading to the design of new masking schemes and a proof of security for an existing masking scheme in presence of transitions. Furthermore, we give similar results in the stronger model where the effects of glitches and transitions are combined.
Flexible Multi-View Unsupervised Graph Embedding

Faced with the increasing data diversity and dimensionality, multi-view dimensionality reduction has been an important technique in computer vision, data mining and multi-media applications. Since collecting labeled data is difficult and costly, unsupervised learning is of great significance. Generally, it is crucial to explore the complementarity or independence of different feature spaces in multi-view learning. How to find a low-dimensional subspace to preserve the intrinsic structure of original unlabeled high-dimensional multi-view data is still challenging. In addition, noises and outliers always appear in real data. In this study, we propose a novel model called flexible multi-view unsupervised graph embedding (FMUGE). A flexible regression residual term is introduced so that the strict linear mapping is relaxed, new-coming data and noises are better handled, and the raw data negotiate with the learned low-dimensional representation in the procedure. To ensure the consistency among multiple views, FMUGE adaptively weights different features and fuses them to get an optimal multi-view consensus similarity graph, which assists high-quality graph embedding. We propose an efficient alternating iterative algorithm to optimize the proposed model. Finally, experimental results on synthetic and benchmark datasets show the significant improvement of FMUGE over the state-of-the-art methods.
Building a Twitter Sentiment Analysis System with Recurrent Neural Networks

This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component within a sentiment indicator of the SynergyCrowds platform.
Performance up-gradation of Symbiotic Organisms Search by Backtracking Search Algorithm

Symbiotic Organisms Search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. Backtracking Search Algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously. The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems.
Remote Asynchronous Collaboration in Maintenance scenarios using Augmented Reality and Annotations

This paper presents an Augmented Reality (AR) remote collaborative approach making use of different stabilized annotation features, part of an ongoing research with partners from the industry. It enables a remote expert to assist an on-site technician during asynchronous maintenance tasks. To foster the creation of a shared understanding, the on-site technician uses mobile AR, allowing the identification of issues, while the remote expert uses a computer to share annotations and provide spatial information about objects, events and areas of interest. The results of a pilot user study to evaluate asynchronous collaborative aspects while using the approach are also presented.
Sandslash

Graph pattern mining (GPM) is a key building block in diverse applications, including bioinformatics, chemical engineering, social network analysis, recommender systems and security. Existing GPM frameworks either provide high-level interfaces for productivity at the cost of expressiveness or provide low-level interfaces that can express a wide variety of GPM algorithms at the cost of increased programming complexity. Moreover, existing systems lack the flexibility to explore combinations of optimizations to achieve performance competitive with hand-optimized applications.
Stroke constrained attention network for online handwritten mathematical expression recognition

In this paper, we propose a novel stroke constrained attention network (SCAN) which treats stroke as the basic unit for encoder-decoder based online handwritten mathematical expression recognition (HMER). Unlike previous methods which use trace points or image pixels as basic units, SCAN makes full use of stroke-level information for better alignment and representation. The proposed SCAN can be adopted in both single-modal (online or offline) and multi-modal HMER. For single-modal HMER, SCAN first employs a CNN-GRU encoder to extract point-level features from input traces in online mode and employs a CNN encoder to extract pixel-level features from input images in offline mode, then use stroke constrained information to convert them into online and offline stroke-level features. Using stroke-level features can explicitly group points or pixels belonging to the same stroke, therefore reduces the difficulty of symbol segmentation and recognition via the decoder with attention mechanism. For multi-modal HMER, other than fusing multi-modal information in decoder, SCAN can also fuse multi-modal information in encoder by utilizing the stroke based alignments between online and offline modalities. The encoder fusion is a better way for combining multi-modal information as it implements the information interaction one step before the decoder fusion so that the advantages of multiple modalities can be exploited earlier and more adequately. Besides, we propose an approach combining the encoder fusion and decoder fusion, namely encoder-decoder fusion, which can further improve the performance. Evaluated on a benchmark published by CROHME competition, the proposed SCAN achieves the state-of-the-art performance. Furthermore, by conducting experiments on an additional task: online handwritten Chinese character recognition (HCCR), we demonstrate the generality of our proposed method.
Adaptive Explainable Neural Networks (Axnns)

While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpret-ability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models (through explainable neural networks) using a two-stage process. This can be done using either a boosting or a stacking ensemble. For interpret-ability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects. The computations are inherited from Google's open source tool AdaNet and can be efficiently accelerated by training with distributed computing. The results are illustrated on simulated and real data-sets.
Robust transfer learning based on Geometric Mean Metric Learning

Transfer learning usually utilizes the knowledge learned from the relative labeled source domain to promote the model performance in the unlabeled or few labeled target domain with different distribution. Most of the existing transfer learning methods aim to reduce the discrepancy of distributions between the source and target domains, but ignore the discriminative category information involved in the data from both domains in the process of knowledge transfer. To learn more discriminative feature representation in knowledge transfer, this paper integrates the transfer learning and metric learning into a unified framework and proposes a novel robust transfer learning based on geometric mean metric learning, namely Geometric Mean Transfer Learning (GMTL). GMTL uses weighted geometric mean metric learning to model the intra-class distance and the inter-class similarity. In the meantime, the marginal distributions and conditional distributions of the source and target domains are jointly adapted. Moreover, according to the natures of the datasets in different tasks, we dynamically combine the discriminative modeling and domain adaption to make the proposed model more robust. We assign different weights to the intra-class distance and the inter-class similarity in metric learning and different weights to marginal distribution adaption and conditional distribution adaption, respectively. Finally, the solution to the objective function is converted to the problem of finding a point on the geodesic joining two points on the Riemannian manifold, which is very simple and direct. Extensive experiments are conducted on six datasets widely adopted in transfer learning to verify the superiority of our proposed GMTL over existing state-of-the-art transfer learning methods.
Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM

To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user's forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young's modulus (286 KPa) and exceptional breathability (18 g m-2 h-1 of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation

The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP).While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical.We present MATE-KD, a novel textbased adversarial training algorithm which improves the performance of knowledge distillation.MATE-KD first trains a masked language model-based generator to perturb text by maximizing the divergence between teacher and student logits.Then using knowledge distillation a student is trained on both the original and the perturbed training samples.We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines.On the GLUE test set our 6 layer RoBERTa based model outperforms BERT LARGE .