Reviewerly/exHarmony huggingface dataset:
Truedepth Measurements of Facial Expressions: Sensitivity to the Angle Between Camera and Face

Facial expressions play an important role in communication, especially in sign languages. Linguistic analysis of the exact contribution of facial expressions, as well as the creation of realistic conversational avatars, especially sign language avatars, requires accurate measurements of the facial expressions of humans while engaged in linguistic interaction. Several recent projects have employed a TrueDepth camera to make such measurements. The present paper investigates how reliable this technique is. In particular, we consider the extent to which the obtained measurements are affected by the angle between the camera and the face. Overall, we find that there are generally significant, and often rather substantial differences between measurements from different angles. However, when the measured facial features are highly activated, measurements from different angles are generally strongly correlated.
Speeding up Detection and Imaging Using Quantum Radars

In this paper, we provide a simple introduction to the enhancement achievable by quantum radars. We show that an important type of quantum radar, known as quantum two-mode squeezing (QTMS) radar, is mathematically connected to noise radars. The quantum advantage stems from QTMS radar’s ability to mitigate the effect of quantum noise, leading to greater contrast in the matched filter output between the target present and target absent cases. This reduced the integration time needed to detect a target. For imaging applications that rely on matched filtering, the quantum enhancement leads to a speedup in imaging as well.
Learning a logistic regression with the help of unknown features at prediction stage

The use of features available at training time, but not at prediction time, as additional information for training models is known as learning using privileged information paradigm.In this paper, the handling of privileged features is addressed from the logistic regression perspective, commonly used in the clinical setting.Two new proposals, LOGIT+ and LRPROB+, learned with the influence of privileged features and preserving the interpretability of conventional logistic regression, are proposed.Experimental results on datasets report improvements of our proposals over the performance of traditional logistic regression learned without privileged information.
SCS-VAE: Generate Style Headlines Via Novel Disentanglement

Current headline generation models only focus on consistent headlines while lacking attention to headline styles. However, headlines of different styles could have different effects on producers and viewers. Capturing salient content while following a unique style is challenging when generating text. In this paper, we propose a Semantic Content-Style VAE(SCS-VAE), combining the Variational Auto-Encoder(VAE) and the dictionary learning method to solve the content-related and stylized problems of generating headlines simultaneously. Specifically, we disentangle the semantic information in content and style space, allowing us to control the generation of headline content and styles. Moreover, we design interpretable loss functions better to supervise the semantic information into two different feature spaces. Experiment results show that SCS-VAE has SOTA performance in headline generation method and makes the style of the headlines more diversified.
Lowbit Neural Network Quantization for Speaker Verification

With the continuous development of deep neural networks (DNN) in recent years, the performance of speaker verification systems has been significantly improved with the application of Deeper ResNet architectures. However, these deeper models occupy more storage space in application. In this paper, we adopt Alternate Direction Methods of Multipliers (ADMM) to realize low-bit quantization on the original ResNets. Our goal is to explore the maximal quantization compression without evident degradation in model performance. We implement different uniform quantization for each convolution layer to achieve mixed precision quantization of the entire model. Moreover, the impact of batch normalization layers in ADMM training and layer sensibility to quantization are explored. In our experiments, the 8 bit quantized ResNetl52 achieved comparable results to the full-precision one on Voxceleb 1, with only 45% of original model size. Besides, we find that shallow convolution layers are more sensitive to quantization. In addition, experimental results indicate that the model performance will be severely degraded if batch normalization layers are integrated into the convolution layer before the quantization training starts.
An asynchronous federated learning focusing on updated models for decentralized systems with a practical framework

Federated learning (FL), a machine learning technique that preserves privacy by aggregating models from each device without exchanging personal data, has gained significant interest. This paper aims to establish an efficient and practical asynchronous FL method for decentralized systems. In asynchronous decentralized FL, the order of learning and aggregation is arbitrary. First, we explore the impact of this ordering on FL. Then, we propose to utilize the version information regarding model updates. Our strategy is to aggregate only the updated models after the previous round to improve the quality of the device’s model by avoiding the reaggregation of older models. Moreover, we also design a new practical framework for asynchronous decentralized FL by extending Flower framework. Our framework realizes effective communication ability by leveraging gRPC communication and thus can be applied to practical systems without the central server. Our evaluation shows the effectiveness of our methods that aggregate only the updated models from other devices. In addition, we show the impact of ordering on learning and aggregation according to situations.
A Standard Baseline for Software Defect Prediction: Using Machine Learning and Explainable AI

The utility of ubiquitous computing systems drives large-scale software development with millions of lines of code (LOC). As there are vast code sets, it also increases the possibility of coding errors since it is difficult for even highly trained software engineers to write flawless code. Such flawed software can lead to severe issues once deployed. McCabe and Halstead proposed feature extractors for source code to define software quality. Based on the static features proposed by McCabe and Halstead, we run a series of feature engineering techniques and different machine learning models to detect code defects and use explainable algorithms to assess the prediction quality. We report different processing pipeline combinations to detect defects and compare the approaches. We conclude the paper with comments on the nature of the dataset and establish a baseline for further research.
Curriculum Learning Based Multi-Agent Path Finding for Complex Environments

Multi-agent reinforcement learning (MARL) is a promising tool to solve the Multi-Agent Path Finding (MAPF) task, which aims to find conflict-free paths for multiple agents, one for each agent, from a start position to a goal position. It uses global information to learn a mechanism for cooperation among agents by maximising the cumulative team rewards, which are often very sparse. However, the sparsity of rewards implies that agents have to blindly explore all possible paths, which makes MARL methods difficult to converge in complex environments. To address this issue, this paper proposes a novel Curriculum based Path-finding Learning (CPL) under the framework of curriculum learning, which allows agents to start with simple skills and to learn cooperative strategies stage-by-stage for more efficient training. Specifically, CPL divides the training process into three stages and speeds up the learning by changing the difficulty of the tasks from easy to hard. Experiments on random obstacle grid worlds show that our proposed method performs significantly better in terms of success rate and makespan than state-of-the-art learning-based methods.
Deep Imputation-Prediction Networks for Health Risk Prediction using Electronic Health Records

Electronic health records (EHRs) have an inherently high degree of irregularity, including many missing values and varying time intervals, due to variations in patient conditions and treatment needs. This makes successful health risk prediction challenging. EHRs contain longitudinal patient data that records meaningful information associated with a chronological set of clinical observations for each patient. Existing methods focus on modeling variable correlations in patient data with deep neural networks to impute missing values and feed complete data matrices into machine learning models to perform downstream healthcare prediction tasks. However, not enough attention was given to the reliability of the imputed values by these methods. Further, it is likely that the pattern of missing data in EHR contains important information affecting relationships among variables, including time intervals. We propose a novel deep imputation-prediction network to simultaneously perform imputation and prediction tasks with EHR. Our method has the advantages of being able to: 1) learn from the longitudinal patient data in both forward and backward directions, 2) generate both the predicted and imputed values and enhance the reliability of imputed values, and 3) incorporate three common decay functions to capture the variation pattern of input variables in time and adaptively enhances the temporal representation of each pattern with adjustable weights. As well, our method is able to examine the association between input variables to identify critical indicative variables regardless of how long ago the associated event happened. Experimental results on MIMIC-III and eICU datasets demonstrate the effectiveness and superiority of our method for both imputation and prediction, as well as transparency and interpretability, compared to existing state-of-the-art methods.
Maintaining Distinction and Fairness in Data-Free Class Incremental Learning

Catastrophic forgetting is the central challenge in class incremental learning (CIL), namely adapting a model to new data often results in severe performance degradation on previous tasks or classes. The most successful methods to alleviate this forgetting require extensive replay of previously seen data, which is problematic when memory constraints or data legality concerns exist. In non-exemplar-based CIL setting, an important factor causing catastrophic forgetting is the presence of severe bias between the new and previously learned classes in both the feature extractor and classifier. To address this problem, this paper proposes a simple and effective solution to calibrate the bias in non-exemplar-based CIL. Specifically, we propose to maintain the decision boundaries of the previously learned classes by preserving a collection of pseudo-samples in the latent space. By maintaining the distinction and balance between old and new classes mitigates representation bias. Then, to further maintain fairness, we propose classifier aligning that corrects the bias in the FC layer, which help the model output more discriminative results within older classes. Experimental results on benchmark datasets show that our method is significantly superior to non-exemplar-based methods, with a 10% improvement in average incremental accuracy, and achieves comparable performance compared to exemplar-based methods. The ablation study further confirms the effectiveness of the method.
CGCCMR: An Enhanced Multi-gate Model for Cross-Market Recommendation

E-commerce applications such as Amazon and Tmall often provide services in multiple countries, forming multiple markets around the world. Generally, different markets over-lap in some items and differ in users. To make effective use of data from different markets, Cross-Market Recommendation (CMR) has been proposed to improve recommendation performance in data-scarce markets(i.e. target markets) using knowledge learned from other data-rich markets(i.e. source markets). Previous works on CMR do not capture the differences between markets well and still do not provide good recommendations to users in the target market. To address this limitation, we propose the CGCCMR model to leverage data from multiple markets and to improve the recommendation performance in the target market. CGCCMR first pre-trains the embedding layer by Light Graph Convolution(LGC) and performs Mask Data Augmentation on the samples from target markets. Then, it extracts the user interest representation related to the target item by User Interest Attention Network(UIAN) and adopts Cross-Market Customized Gate Control(CM-CGC) to effectively capture the similarities and differences between markets. Finally, it adopts an auxiliary network and a market-specific tower network to make final predictions. The experimental results from real-world datasets validate the superiority of the proposed CGCCMR model.
Resource-Efficient Federated Clustering with Past Negatives Pool

Federated learning (FL) provides a global model over data distributed to multiple clients. However, most recent work on FL focuses on supervised learning, and a fully unsupervised federated clustering scheme has remained an open problem. In this context, Contrastive learning (CL) trains distinguishable instance embeddings without labels. However, most CL techniques are restricted to centralized data. In this work, we consider the problem of clustering data that is distributed to multiple clients using FL and CL. We propose a federated clustering framework with a novel past negatives pool (PNP) for intelligently selecting positive and negative samples for CL. PNP benefits FL and CL simultaneously, specifically, alleviating class collision for CL and reducing client-drift in FL. PNP thus provides a higher accuracy for a given constraint on the communication rounds, which makes it suitable for networks with limited communication and computation resources. Numerical results show that the resulting FedPNP scheme achieves superior performance in solving federated clustering problems on benchmark datasets including CIFAR-10 and CIFAR-100, especially in non-iid settings.
Transformer-Based Multi-Source Domain Adaptation Without Source Data

Source-free domain adaptation (SFDA) aims at target adaptation without access to source data, but with only pre-trained source model. A recent line of work proposed to automatically combine the source models with suitable weights so that it performs at least as good as the best source model when there are multiple pre-trained source models. In addition, some works proposed to preserve consistent inter-class relationships across domains to promote more shared transferable knowledge from source domains towards target adaptation. However, these works ignore the generalization ability of the pre-trained source models, which profoundly affects the initial target predictions that are crucial to the target adaptation stage. To this end, we develop a generic and effective framework based on Transformer, called TransMDA, for multi-source-free domain adaptation (MSFDA). Specifically, we inject the Transformer as the attention module into the convolutional network of each source model since it has the ability to encourage the model to turn attention towards the object regions, which can dramatically improve the model's generalization on the target domain. Furthermore, a novel pseudo-label smoothing strategy is proposed to avoid overfitting to the target domain. Experiments on several challenging datasets demonstrate the superiority of our proposed TransMDA method.
A causal graph-based framework for satellite health monitoring

In satellite operations, one of the essential tasks is to monitor the health status of the systems, which involves forecasting telemetry data that reflects the state of health. The application of data-driven approaches in system monitoring has led to significant improvements in health monitoring and anomaly detection. However, existing methods fail to fully leverage the complex inter-sensor relationships present in satellites. They do not explicitly exploit the structure of these relationships to predict the expected behavior of telemetry time series either. To address these limitations, this paper introduces a novel health monitoring framework for artificial satellites that combines causal graphs and deep learning. In the causality learning phase, we propose a method that integrates mRMR (Maximum Relevance Minimum Redundancy) and PCMCI (Peter-Clark Momentary Conditional Independence) to construct an efficient and accurate causal discovery approach for learning causal graphs for high-dimensional telemetry data. Subsequently, we design a graph attention-based neural network that incorporates these causal graphs into a deep network for prediction. Experimental evaluation on two datasets from satellite attitude control systems and power systems demonstrates the superior performance of our proposed method in accurately predicting health status compared to baseline approaches. Furthermore, the experiments highlight the interpretability-enhancing role of causal graphs, which is beneficial for health monitoring and anomaly detection.
Application of neural networks in beam emission spectroscopy modelling

Beam emission spectroscopy (BES) is an active plasma diagnostic utilized for plasma density measurements. BES synthetic diagnostics are computationally expensive and comprehensive modelling suites designed to provide a better understanding of the diagnostics' perception of underlying plasma phenomena. RENATE-OD is an advanced BES synthetic diagnostic relying on a rate-equation solver to derive the beam emission for given input plasma profiles. In this work, linear regression, multi-layer perceptron and extreme learning machines were explored as a substitute for the rate-equation solver to predict the beam emission. Our experiments show that extreme learning machines are suitable for the task of predicting the arising emission profiles with very high accuracy and about 8000 times faster than RENATE-OD.
Multi-Task Learning For Radar Signal Characterisation

Radio signal recognition is a crucial task in both civilian and military applications, as accurate and timely identification of unknown signals is an essential part of spectrum management and electronic warfare. The majority of research in this field has focused on applying deep learning for modulation classification, leaving the task of signal characterisation as an understudied area. This paper addresses this gap by presenting an approach for tackling radar signal classification and characterisation as a multi-task learning (MTL) problem. We propose the IQ Signal Transformer (IQST) among several reference architectures that allow for simultaneous optimisation of multiple regression and classification tasks. We demonstrate the performance of our proposed MTL model on a synthetic radar dataset, while also providing a first-of-its-kind benchmark for radar signal characterisation.
Interesting Near-boundary Data: Inferring Model Ownership for DNNs

Deep neural networks (DNNs) require expensive training, which is why the protection of model intellectual property (IP) is becoming more critical. Recently, model stealing has emerged frequently, and many researchers design model watermarking and fingerprinting for verifying model ownership. However, attacks such as ambiguity statements have been used to break the current defense, which poses a challenge to model ownership verification. Therefore, this paper proposes an interesting near-boundary data as evidence for obtaining model ownership and innovatively proposes to infer model ownership instead of verifying model ownership. In this paper, we propose to generate the initial near-boundary data using an algorithm that adds slight noise to generate adversarial examples. We design a generator to privatize the near-boundary data. Our main observation is that the near-boundary data exhibit results close to the classification boundary in both the source model and its derived stolen model. At the end of this work, we design many experiments to verify the effectiveness of the proposed method. The experimental results demonstrate that model ownership can be inferred with high confidence. Noting that our method does not require the training data to be private, and it is extremely costly for model stealers to reuse our method.
Procrustes-DTW: Dynamic Time Warping Variant for the Recognition of Sign Language Utterances

In this paper we present a method for classifying sign language videos using a variation of dynamic time warping with the Procrustes distance, which is a shape similarity measure appropriate for handshape recognition. We initially extract features from the dominant hand. We then classify the signs using a nearest-neighbor scheme which includes a dynamic time warping variant, which we dub Procrustes-DTW and which is suitable for extracting a similarity measure of sequences of handshapes only. To reduce computational costs it is combined with a curvature-based summarization scheme with a reasonable sacrifice in accuracy. We verify experimentally our approach on a custom dataset and we show its merit compared to the conventional dynamic time warping approach.
Monetary incentives and social ties in academic publications

Monetary incentives underlie academic publications, especially in countries where many researchers' incomes are performance-based. Evidence has documented that social ties improve the chances of publication in journals, whereas building and maintaining social ties require resources. We develop a simple model to investigate the consequences of monetary investment on the probability of publication through social ties. The results indicate that monetary incentive can stimulate researchers to make more efforts regarding publication. Through social ties, monetary investment in the process of academic publication distorts researchers' incentive. Monetary investment through social ties improves the probability of publication and then disables the signaling function of publication in terms of researchers’ abilities. Depending on the contribution of different types of researchers to society and the ability of the latter to differentiate the quality of publication in journals, the improvement in publication probability by monetary investment through social ties may improve or reduce the level of social welfare.