Reviewerly/exHarmony huggingface dataset:
Residual multi-task learning for facial landmark localization and expression recognition

Facial landmark localization and expression recognition are two important and highly relevant topics in facial analysis. However, few works focus on using the complementary information between the two tasks to improve the performance. In this paper, we propose a residual multi-task learning framework to predict the two tasks simultaneously. Different from previous multi-task learning methods which directly train a deep multi-task network with additional branches and losses, we propose a novel residual learning module to further strengthen the linkages between the two tasks. Benefit from the proposed residual learning module, one task can learn complementary information from the other task, leading to the performance promotion. Another problem for the multi-task learning is the lack of training data with multi-task labels. For example, there is no landmark localization annotation for the two widely-used FER dataset (AffectNet and RAF), vice versa. To solve this problem, we propose an association learning method to further enhance the connection between the two tasks. Based on this connection, the dataset with single-task labels can be used in the multi-task learning. Extensive experiments are conducted on four popular datasets (i.e. 300-W, AFLW for landmark localization and AffectNet, RAF for expression recognition), demonstrating the effectiveness of the proposed algorithm.
Quantum Repeater Node Demonstrating Unconditionally Secure Key Distribution

Long-distance quantum communication requires quantum repeaters to overcome photon loss in optical fibers. Here we demonstrate a repeater node with two memory atoms in an optical cavity. Both atoms are individually and repeatedly entangled with photons that are distributed until each communication partner has independently received one of them. An atomic Bell-state measurement followed by classical communication serves to establish a key. We demonstrate scaling advantage of the key rate, increase the effective attenuation length by a factor of 2, and beat the error-rate threshold of 11% for unconditionally secure communication, the corner stones for repeater-based quantum networks.
Crosstalk Suppression for Fault-tolerant Quantum Error Correction with Trapped Ions

Physical qubits in experimental quantum information processors are inevitably exposed to different sources of noise and imperfections, which lead to errors that typically accumulate hindering our ability to perform long computations reliably. Progress towards scalable and robust quantum computation relies on exploiting quantum error correction (QEC) to actively battle these undesired effects. In this work, we present a comprehensive study of crosstalk errors in a quantum-computing architecture based on a single string of ions confined by a radio-frequency trap, and manipulated by individually-addressed laser beams. This type of errors affects spectator qubits that, ideally, should remain unaltered during the application of single- and two-qubit quantum gates addressed at a different set of active qubits. We microscopically model crosstalk errors from first principles and present a detailed study showing the importance of using a coherent vs incoherent error modelling and, moreover, discuss strategies to actively suppress this crosstalk at the gate level. Finally, we study the impact of residual crosstalk errors on the performance of fault-tolerant QEC numerically, identifying the experimental target values that need to be achieved in near-term trapped-ion experiments to reach the break-even point for beneficial QEC with low-distance topological codes.
Fine-grained, aspect-based sentiment analysis on economic and financial lexicon

Extracting sentiment from news text, social media and blogs has recently gained increasing interest in economics and finance. Despite many successful applications of sentiment analysis (SA) in these domains, the range of semantic techniques employed is still limited and predominantly focused on the detection of sentiment at a coarse-grained level. This paper proposes a novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis. The aim is to identify the sentiment associated with specific topics of interest in each sentence of a document and to assign real-valued polarity scores between -1 and +1 to those topics. The proposed approach is unsupervised and customised to the economic and financial domains by using a specialised lexicon provided by us along with the FiGAS source code. Our lexicon-based SA approach relies on a detailed set of semantic polarity rules that allow understanding of the origin of sentiment – in the spirit of the recent trend on Interpretable AI. We provide an in-depth comparison of the performance of the FiGAS algorithm with other popular lexicon-based SA approaches in predicting a humanly annotated dataset in the economic and financial domains. Our results indicate that FiGAS statistically outperforms the other methods by providing a sentiment score that is closer to those of human annotators.
Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Specific emitter identification (SEI) is a highly potential technology for physical-layer authentication that is one of the most critical supplements for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristics of hardware circuits, which are difficult to counterfeit. Recently, various deep learning (DL)-based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with a complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intracategory distance and separable intercategory distance, while the latter is realized by an ensemble classifier. Simulation results show that if the number of samples per category is more than 5, the average accuracy of our proposed method is higher than 98%. Moreover, feature visualization demonstrates the advantages of our proposed method in both discriminability and generalization. The code and the dataset can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BeechburgPieStar/FS-SEI</uri> .
Evaluation of a decided sample size in machine learning applications

An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets and three real datasets to derive the criteria for sample size. We systematically increase the sample size, starting from 16, by randomly sampling and examine the impact of sample size on classifiers' performance and both effect sizes. Tenfold cross-validation was used to quantify the accuracy.The results demonstrate that the effect sizes and the classification accuracies increase while the variances in effect sizes shrink with the increment of samples when the datasets have a good discriminative power between two classes. By contrast, indeterminate datasets had poor effect sizes and classification accuracies, which did not improve by increasing sample size in both simulated and real datasets. A good dataset exhibited a significant difference in average and grand effect sizes. We derived two criteria based on the above findings to assess a decided sample size by combining the effect size and the ML accuracy. The sample size is considered suitable when it has appropriate effect sizes (≥ 0.5) and ML accuracy (≥ 80%). After an appropriate sample size, the increment in samples will not benefit as it will not significantly change the effect size and accuracy, thereby resulting in a good cost-benefit ratio.We believe that these practical criteria can be used as a reference for both the authors and editors to evaluate whether the selected sample size is adequate for a study.
Variational Bayesian Learning of Generalized Dirichlet-Based Hidden Markov Models Applied to Unusual Events Detection

Learning a hidden Markov model (HMM) is typically based on the computation of a likelihood which is intractable due to a summation over all possible combinations of states and mixture components. This estimation is often tackled by a maximization strategy, which is known as the Baum-Welch algorithm. However, some drawbacks of this approach have led to the consideration of Bayesian methods that add a prior over the parameters in order to work with the posterior probability and the marginal likelihood. These approaches can lead to good models but to the cost of extremely long computations (e.g., Markov Chain Monte Carlo). More recently, variational Bayesian frameworks have been proposed as a Bayesian alternative that keeps the computation tractable and the approximation tight. It relies on the introduction of a prior over the parameters to be learned and on an approximation of the true posterior distribution. After proving good standing in the case of finite mixture models and discrete and Gaussian HMMs, we propose here to derive the equations of the variational learning of the Dirichlet mixture-based HMM, and to extend it to the generalized Dirichlet. The latter case presents several properties that make the estimation more accurate. We prove the validity of this approach within the context of unusual event detection in public areas using the University of California San Diego data sets. HMMs are trained over normal video sequences using the typical Baum-Welch approach versus the variational one. The variational learning leads to more accurate models for the detection and localization of anomaly, and the general HMM approach is shown to be versatile enough to handle the detection of various synthetically generated tampering events.
Digital pathology: semper ad meliora

This review is an evidence-based summary of digital pathology: past, present and future. It discusses digital surgical pathology and the cytopathology digitisation challenge as well as the performance of digital histopathology and cytopathology as a diagnostic tool, particularly in contrast to user perceptions. Time and cost efficiency of digital pathology, learning curves, education and quality assurance, with the importance of validation of systems, is emphasised. The review concludes with a discussion of digital pathology as a source of ‘big data’ and where this might lead pathologists in the digital pathology future.
FPGA Implementation of Digital Images Watermarking System Based on Discrete Haar Wavelet Transform

In this paper we propose a novel and efficient hardware implementation of an image watermarking system based on the Haar Discrete Wavelet Transform (DWT). DWT is used in image watermarking to hide secret pieces of information into a digital content with a good robustness. The main advantage of Haar DWT is the frequencies separation into four subbands (LL, LH, HL, and HH) which can be treated independently. This permits ensuring a better compromise between robustness and visibility factors. A Field Programmable Gate Array (FPGA) that is based on a very large scale integration architecture of the watermarking algorithm is developed to accelerate media authentication. A hardware cosimulation strategy using the Matlab-Xilinx system generator (XSG) was applied to prove the validity of the suggested implementation. The hardware cosimulation results show the effectiveness of the developed architecture in terms of visibility and robustness against several attacks. The proposed hardware system presents also a high performance in terms of the operating speed.
Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection

Unsupervised feature selection is a challenging task to gain relevant features for improving learning performance due to lack of the label information. Traditional unsupervised feature selection methods are often vector-based, which may ignore the location information of original matrix element. In this paper, we propose a joint sparse matrix regression and nonnegative spectral analysis model for two-dimensional unsupervised feature selection. To obtain proper label information under unsupervised condition, we adopt a nonnegative spectral clustering technique to yield the clustering labels as the pseudo class labels. To directly select the relevant feature on matrix data, we construct a regression relationship between matrix data and the pseudo class labels by deploying left and right regression matrices. Our proposed method can integrate the merits of both sparse matrix regression and nonnegative spectral clustering for feature selection. An efficient optimization algorithm is designed to solve our proposed optimization problem. Extensive experimental results on clustering and classification demonstrate the effectiveness of our proposed method.
Improved K-Pass Pixel Value Ordering Based Data Hiding

K-pass pixel value ordering (PVO) is an effective reversible data hiding (RDH) technique. In k-pass PVO, the complexity measurement may lead to a weak estimation result because the unaltered pixels in a block are excluded to estimate block complexity. In addition, the prediction-error is computed without considering the location relationship of the second largest and largest pixels or the second smallest and smallest pixels. To this end, an improved RDH technique is proposed in this paper to enhance the embedding performance. The improvement mainly lies in the following two aspects. First, some pixels in a block, which are excluded from data hiding in some existing RDH methods, are exploited together with the neighborhood surrounding this block to increase the estimation accuracy of local complexity. Second, the remaining pixels in a block, i.e., three largest and three smallest pixels are involved in data embedding. Taking three largest pixels for example, when the difference between the largest and third largest pixels is relatively large (e.g., > 1), we improve k-pass PVO by considering the location relationship of the second largest and largest pixels. The advantage of doing this is that the difference valued 3 between the maximum and the second largest pixel which is shifted in k-pass PVO, is able to carry 1 bit data in our method. In other words, a larger amount of pixels are able to carry data bits in our scheme compared with k-pass PVO. Abundant experimental results reveal that the proposed method achieves preferable embedding performance compared with the previous work, especially when a larger payload is required.
Algebraic secret sharing using privacy homomorphisms for IoT-basedhealthcare systems

Healthcare industry is one of the promising fields adopting the Internet of Things (IoT) solutions. In this paper, we study secret sharing mechanisms towards resolving privacy and security issues in IoT-based healthcare applications. In particular, we show how multiple sources are possible to share their data amongst a group of participants without revealing their own data to one another as well as the dealer. Only an authorised subset of participants is able to reconstruct the data. A collusion of fewer participants has no better chance of guessing the private data than a non-participant who has no shares at all. To realise this system, we introduce a novel research upon secret sharing in the encrypted domain. In modern healthcare industry, a patientos health record often contains data acquired from various sensor nodes. In order to protect information privacy, the data from sensor nodes is encrypted at once and shared among a number of cloud servers of medical institutions via a gateway device. The complete health record will be retrieved for diagnosis only if the number of presented shares meets the access policy. The retrieval procedure does not involve decryption and therefore the scheme is favourable in some time-sensitive circumstances such as a surgical emergency. We analyse the pros and cons of several possible solutions and develop practical secret sharing schemes for IoT- based healthcare systems.
Understanding Kinaesthetic Creativity in Dance

Kinaesthetic creativity refers to the body's ability to generate alternate futures in activities such as role-playing in participatory design workshops. This has relevance not only to the design of methods for inspiring creativity but also to the design of systems that promote engaging experiences via bodily interaction. This paper probes this creative process by studying how dancers interact with technology to generate ideas. We developed a series of parameterized interactive visuals and asked dance practitioners to use them in generating movement materials. From our study, we define a taxonomy that comprises different relationships and movement responses dancers form with the visuals. Against this taxonomy, we describe six types of interaction patterns and demonstrate how dance creativity is driven by the ability to shift between these patterns. We then propose a set of interaction design qualities to support kinaesthetic creativity.
Forensic source identification using JPEG image headers: The case of smartphones

A common problem in forensic investigations is the identification of the source of multimedia data, i.e., determining the model, make or individual device that recorded media content. In contrast to methods based on sensor noise, source linkage based on header information of media items allows for easy automation. Such header information involves metadata like EXIF tags and the parameterization of the JPEG algorithm. While traditional digital cameras typically had a fixed software stack that makes it straightforward to fingerprint a device, modern mobile devices vary considerably in their software stack over time. We perform a large-scale study of JPEG header information from Apple smartphones to investigate the effect of this development on the possibility to perform source identification. Our analysis shows that identification of the concrete hardware is much harder for smartphones than it is for traditional cameras. However, identification of software stack, particularly the operating system version and selected apps, is well feasible.
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization

Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. We believe these benchmarks provide an easy and efficient way to conduct reproducible experiments for neural hyperparameter search. Our benchmarks consist of a large grid of configurations of a feed forward neural network on four different regression datasets including architectural hyperparameters and hyperparameters concerning the training pipeline. Based on this data, we performed an in-depth analysis to gain a better understanding of the properties of the optimization problem, as well as of the importance of different types of hyperparameters. Second, we exhaustively compared various different state-of-the-art methods from the hyperparameter optimization literature on these benchmarks in terms of performance and robustness.
HireNet: A Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews

New technologies drastically change recruitment techniques. Some research projects aim at designing interactive systems that help candidates practice job interviews. Other studies aim at the automatic detection of social signals (e.g. smile, turn of speech, etc...) in videos of job interviews. These studies are limited with respect to the number of interviews they process, but also by the fact that they only analyze simulated job interviews (e.g. students pretending to apply for a fake position). Asynchronous video interviewing tools have become mature products on the human resources market, and thus, a popular step in the recruitment process. As part of a project to help recruiters, we collected a corpus of more than 7000 candidates having asynchronous video job interviews for real positions and recording videos of themselves answering a set of questions. We propose a new hierarchical attention model called HireNet that aims at predicting the hirability of the candidates as evaluated by recruiters. In HireNet, an interview is considered as a sequence of questions and answers containing salient socials signals. Two contextual sources of information are modeled in HireNet: the words contained in the question and in the job position. Our model achieves better F1-scores than previous approaches for each modality (verbal content, audio and video). Results from early and late multimodal fusion suggest that more sophisticated fusion schemes are needed to improve on the monomodal results. Finally, some examples of moments captured by the attention mechanisms suggest our model could potentially be used to help finding key moments in an asynchronous job interview.
How do infinite width bounded norm networks look in function space

We consider the question of what functions can be captured by ReLU networks with an unbounded number of units (infinite width), but where the overall network Euclidean norm (sum of squares of all weights in the system, except for an unregularized bias term for each unit) is bounded; or equivalently what is the minimal norm required to approximate a given function. For functions $f : \mathbb R \rightarrow \mathbb R$ and a single hidden layer, we show that the minimal network norm for representing $f$ is $\max(\int |f''(x)| dx, |f'(-\infty) + f'(+\infty)|)$, and hence the minimal norm fit for a sample is given by a linear spline interpolation.