Reviewerly/exHarmony huggingface dataset:
Robust human position estimation in cooperative robotic cells

Robots are increasingly present in our lives, sharing the workspace and tasks with human co-workers. However, existing interfaces for human-robot interaction / cooperation (HRI/C) have limited levels of intuitiveness to use and safety is a major concern when humans and robots share the same workspace. Many times, this is due to the lack of a reliable estimation of the human pose in space which is the primary input to calculate the human-robot minimum distance (required for safety and collision avoidance) and HRI/C featuring machine learning algorithms classifying human behaviours / gestures. Each sensor type has its own characteristics resulting in problems such as occlusions (vision) and drift (inertial) when used in an isolated fashion. In this paper, it is proposed a combined system that merges the human tracking provided by a 3D vision sensor with the pose estimation provided by a set of inertial measurement units (IMUs) placed in human body limbs. The IMUs compensate the gaps in occluded areas to have tracking continuity. To mitigate the lingering effects of the IMU offset we propose a continuous online calculation of the offset value. Experimental tests were designed to simulate human motion in a human-robot collaborative environment where the robot moves away to avoid unexpected collisions with de human. Results indicate that our approach is able to capture the human\textsc's position, for example the forearm, with a precision in the millimetre range and robustness to occlusions.
Universal Adversarial Perturbations Generative Network For Speaker Recognition

Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the adversarial examples, which have been intentionally perturbed to remain almost imperceptible for human. In this paper, we demonstrated the existence of the universal adversarial perturbations (UAPs) for the speaker recognition systems. We proposed a generative network to learn the mapping from the low-dimensional normal distribution to the UAPs subspace, then synthesize the UAPs to perturbe any input signals to spoof the well-trained speaker recognition model with high probability. Experimental results on TIMIT and LibriSpeech datasets demonstrate the effectiveness of our model.
Watermarking in Deep Neural Networks via Error Back-propagation

Recent advances in deep learning (DL) have led to great success in tasks of computer vision and pattern recognition. Sharing pre-trained DL models has been an important means to promote the rapid progress of research community and development of DL based systems. However, it also raises challenges to model authentication. It is quite necessary to protect the ownership of the DL models to be released. In this paper, we present a digital watermarking technique to deep neural networks (DNNs). We propose to mark a DNN by inserting an independent neural network that allows us to use selective weights for watermarking. The independent neural network is only used in the training phase and watermark verification phase, and will not be released publicly. Experiments have shown that, the performance of marked DNN on its original task will not be degraded significantly. Meantime, the watermark can be successfully embedded and extracted with a low neural network loss even under the common attacks including model fine-tuning and compression, which has shown the superiority and applicability of the proposed work.
An Improved Nonparallel Support Vector Machine

In this article, an improved nonparallel support vector machine (INPSVM) is proposed for pattern classification. INPSVM inherits almost all advantages of nonparallel support vector machine (NPSVM), i.e., the kernel trick can be directly applied for the nonlinear case and the matrix inversion is avoided. These are completely different from the twin support vector machine (TSVM). Moreover, the INPSVM classifier has some incomparable advantages over TSVM and NPSVM. First, it can effectively eliminate the negative effect of noise, especially feature noise around the decision boundary. Second, the novel classifier has higher classification accuracy for both linear and nonlinear data sets compared with the other algorithms. Finally, a large number of experiments show that INPSVM is superior to other algorithms in efficiency, accuracy, and robustness.
NeuSpell: A Neural Spelling Correction Toolkit

We introduce NeuSpell, an open-source toolkit for spelling correction in English.Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources.We find that many systems do not adequately leverage the context around the misspelt token.To remedy this, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated misspellings; and (ii) use contextual representations.By training on our synthetic examples, correction rates improve by 9% (absolute) compared to the case when models are trained on randomly sampled character perturbations.Using richer contextual representations boosts the correction rate by another 3%.Our toolkit enables practitioners to use our proposed and existing spelling correction systems, both via a unified command line, as well as a web interface.Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings.The toolkit can be accessed at neuspell.github.io.
Modeling Protagonist Emotions for Emotion-Aware Storytelling

Emotions and their evolution play a central role in creating a captivating story.In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling.We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist.Our models include Emotion Supervision (Emo-Sup) and two Emotion-Reinforced (EmoRL) models.The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning.Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.
Dynamic characteristic of a new fractional-order chaotic system based on the Hopfield Neural Network and its digital circuit implementation

Abstract In this paper, based on the principle of activation function between the neurons, we designed a Hopfield neural network (HNN) chaotic system. And then we defined a fractional-order HNN chaotic system by Caputo definition. The solution of the fractional-order HNN chaotic system is calculated by Adomain decomposition method (ADM). Then the dynamic performances of the fractional-order HNN chaotic system are analyzed through attractor phase diagram, bifurcation diagram, Lyapunov exponent spectrum, fractal dimension, chaotic diagram and SE complexity. In addition, the system is digital circuit implemented based on DSP platform. The experimental results show that the fractional-order HNN chaotic system not only has rich dynamic behavior, but also has complex nonlinear phenomena such as attractor coexistence which is sensitive to initial value. Therefore, this system has good potential application value, it can be used as multi-source pseudo-random number generator, and the generated pseudo-random sequence can be used in chaotic cryptography and secure communication.
Time Series Simulation by Conditional Generative Adversarial Net

Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions can be both categorical and continuous variables containing different kinds of auxiliary information. Our simulation studies show that CGAN is able to learn different kinds of normal and heavy tail distributions, as well as dependent structures of different time series and it can further generate conditional predictive distributions consistent with the training data distributions. We also provide an in-depth discussion on the rationale of GAN and the neural network as hierarchical splines to draw a clear connection with the existing statistical method for distribution generation. In practice, CGAN has a wide range of applications in the market risk and counterparty risk analysis: it can be applied to learn the historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES) and predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate CGAN is able to outperform the Historic Simulation, a popular method in market risk analysis for the calculation of VaR. CGAN can also be applied in the economic time series modeling and forecasting, and an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN is given at the end of the paper.
Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.
A Hierarchical Memory Network for Knowledge Tracing

• Our model simulates human memory by the proposed memory network. • Our model outperforms the traditional deep knowledge tracing models. • Our model finds a more logical correlation between skills. Knowledge Tracing (KT) is a task to acquire students’ mastery level of skills based on their performance in learning process. The existing KT models have gradually achieved improvements in prediction performance. However, they do not well simulate working memory and long-term memory in human memory mechanism, which is closely related to learning process. In our paper, we propose a Hierarchical Memory Network (HMN) to fit human memory mechanism better in KT. The hierarchical memory, an essential component of HMN, is achieved by an external memory matrix and two mechanisms (divide mechanism, decay mechanism). The matrix simulates working memory by working storage and long-term memory by long-term storage through divide mechanism. Furthermore, the working storage can be changed directly, while the long-term storage is changed according to decay rates obtained from decay mechanism. Experiments demonstrate that our model outperforms several classical models in four public datasets.
FlexGraph

Graph neural networks (GNNs) aim to learn a low-dimensional feature for each vertex in the graph from its input high-dimensional feature, by aggregating the features of the vertex's neighbors iteratively. This paper presents Flex-Graph, a distributed framework for training GNN models. FlexGraph is able to efficiently train GNN models with flexible definitions of neighborhood and hierarchical aggregation schemes, which are the two main characteristics associated with GNNs. In contrast, existing GNN frameworks are usually designed for GNNs having fixed definitions and aggregation schemes. They cannot support different kinds of GNN models well simultaneously. Underlying FlexGraph are a simple GNN programming abstraction called NAU and a compact data structure for modeling various aggregation operations. To achieve better performance, FlexGraph is equipped with a hybrid execution strategy to select proper and efficient operations according to different contexts during aggregating neighborhood features, an application-driven workload balancing strategy to balance GNN training workload and reduce synchronization overhead, and a pipeline processing strategy to overlap computations and communications. Using real-life datasets and GNN models GCN, PinSage and MAGNN, we verify that NAU makes FlexGraph more expressive than prior frameworks (e.g., DGL and Euler) which adopt GAS-like programming abstractions, e.g., it can handle MAGNN that is beyond the reach of DGL and Euler. The evaluation further shows that FlexGraph outperforms the state-of-the-art GNN frameworks such as DGL and Euler in training time by on average 8.5× on GCN and PinSage.
Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
Non-Markovian Quantum Process Tomography

Characterization protocols have so far played a central role in the development of noisy intermediate-scale quantum (NISQ) computers capable of impressive quantum feats. This trajectory is expected to continue in building the next generation of devices—ones that can surpass classical computers for particular tasks—but progress in characterization must keep up with the complexities of intricate device noise. A missing piece in the zoo of characterization procedures is tomography, which can completely describe non-Markovian dynamics over a given time frame. Here, we formally introduce a generalization of quantum process tomography, which we call process tensor tomography. We detail the experimental requirements, construct the necessary postprocessing algorithms for maximum-likelihood estimation, outline the best-practice aspects for accurate results, and make the procedure efficient for low-memory processes. The characterization is a pathway to diagnostics and informed control of correlated noise. As an example application of the hardware-agnostic technique, we show how its predictive control can be used to substantially improve multitime circuit fidelities on superconducting quantum devices. Our methods could form the core for carefully developed software that may help hardware consistently pass the fault-tolerant noise threshold.4 MoreReceived 11 August 2021Revised 22 December 2021Accepted 10 May 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020344Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum controlQuantum correlations in quantum informationQuantum Information
Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps

With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of these agents is challenging, as the environments in which they act have large state spaces, and their decision-making can be affected by delayed rewards, making it difficult to analyze their behavior. To address this problem, several approaches have been developed. Some approaches attempt to convey the global behavior of the agent, describing the actions it takes in different states. Other approaches devised local explanations which provide information regarding the agent's decision-making in a particular state. In this paper, we combine global and local explanation methods, and evaluate their joint and separate contributions, providing (to the best of our knowledge) the first user study of combined local and global explanations for RL agents. Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to. Our results show that the choice of what states to include in the summary (global information) strongly affects people's understanding of agents: participants shown summaries that included important states significantly outperformed participants who were presented with agent behavior in a set of world-states that are likely to appear during gameplay. We find mixed results with respect to augmenting demonstrations with saliency maps (local information), as the addition of saliency maps, in the form of raw heat maps, did not significantly improve performance in most cases. However, we do find some evidence that saliency maps can help users better understand what information the agent relies on during its decision-making, suggesting avenues for future work that can further improve explanations of RL agents.
Teaching from a Distance—Math Lessons during COVID-19 in Germany and Spain

In 2020, Germany and Spain experienced lockdowns of their school systems. This resulted in a new challenge for learners and teachers: lessons moved from the classroom to the children’s homes. Therefore, teachers had to set rules, implement procedures and make didactical–methodical decisions regarding how to handle this new situation. In this paper, we focus on the roles of mathematics teachers in Germany and Spain. The article first describes how mathematics lessons were conducted using distance learning. Second, problems encountered throughout this process were examined. Third, teachers drew conclusions from their mathematics teaching experiences during distance learning. To address these research interests, a questionnaire was answered by N = 248 teachers (N1 = 171 German teachers; N2 = 77 Spanish teachers). Resulting from a mixed methods approach, differences between the countries can be observed, e.g., German teachers conducted more lessons asynchronously. In contrast, Spanish teachers used synchronous teaching more frequently, but still regard the lack of personal contact as a main challenge. Finally, for both countries, the digitization of mathematics lessons seems to have been normalized by the pandemic.
Myscéal 2.0: A Revised Experimental Interactive Lifelog Retrieval System for LSC'21

Building an interactive retrieval system for lifelogging contains many challenges due to massive multi-modal personal data besides the requirement of accuracy and rapid response for such a tool. The Lifelog Search Challenge (LSC) is the international lifelog retrieval competition that inspires researchers to develop their systems to cope with the challenges and evaluates the effectiveness of their solutions. In this paper, we upgrade our previous Myscéal and present Myscéal 2.0 system for the LSC'21 with the improved features inspired by the novice users experiments. The experiments show that a novice user achieved more than half of the expert score on average. To mitigate the gap of them, some potential enhancements were identified and integrated to the enhanced version.
Knowledge hiding – a systematic review and research agenda

Because of its impact on organisational growth, innovation and employee performance, knowledge hiding (KH) as a construct has gained increased attention from scholars and practitioners in recent years. The purpose of this paper is to conduct a systematic review of the existing literature on KH and take the stock of the current literature, identify research streams and offer recommendations on areas where KH may be investigated further.,In this study, the authors used systematic review methods to investigate the current state of KH research, and using thematic coding, the authors identified the current research streams and offer directions for future research.,The review of literature identified geographic representation of KH research, methodological approaches to explore KH and the prominent theories adopted to investigate KH, and through research synthesis, the antecedents and moderators/mediators of KH were identified. Subsequently, the authors also found seven research streams where KH has been predominantly studied. Finally, the authors provide suggestions of where the future research in KH might be headed.,This paper is one of the few to offer systematic review of KH literature and identify unexplored areas to be investigated in future research – which is the integral part of knowledge management process.
Scalable multiphoton quantum metrology with neither pre- nor post-selected measurements

The quantum statistical fluctuations of the electromagnetic field establish a limit, known as the shot-noise limit, on the sensitivity of optical measurements performed with classical technologies. However, quantum technologies are not constrained by this shot-noise limit. In this regard, the possibility of using every photon produced by quantum sources of light to estimate small physical parameters, beyond the shot-noise limit, constitutes one of the main goals of quantum optics. Here we experimentally demonstrate a scalable protocol for quantum-enhanced optical phase estimation across a broad range of phases, with neither pre- nor post-selected measurements. This is achieved through the efficient design of a source of spontaneous parametric down-conversion in combination with photon-number-resolving detection. The robustness of two-mode squeezed vacuum states against loss allows us to outperform schemes based on N00N states, in which the loss of a single photon is enough to remove all phase information from a quantum state. In contrast to other schemes that rely on N00N states or conditional measurements, the sensitivity of our technique could be improved through the generation and detection of high-order photon pairs. This unique feature of our protocol makes it scalable. Our work is important for quantum technologies that rely on multiphoton interference such as quantum imaging, boson sampling and quantum networks.