Reviewerly/exHarmony huggingface dataset:
Nationwide prediction of type 2 diabetes comorbidities

Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Leveraging logistic regression-, random forest- and gradient boosting models and register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we predicted five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.04, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 1.97 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.
Privacy Protection Scheme Based on CP-ABE in Crowdsourcing-IoT for Smart Ocean

Crowdsourcing is a novel distributed problem-solving mechanism that can provide the collection and share of marine data in the Internet of Things for the smart ocean. Nevertheless, the privacy leakage issue caused by multirole information interaction in crowdsourcing brings a serious challenge to the smart ocean. In this article, we propose a crowdsourcing privacy protection scheme based on multiauthority ciphertext-policy attribute-based encryption to enhance privacy protection in the data sharing environment. In this scheme, we design an independent key component distribution approach through multiple authorities, which could effectively disperse the security responsibility from the crowdsourcing platform. Then, we present the idea of partial decryption on the platform to reduce the computing cost of mobile users and prevent the platform from snooping on users' data. Moreover, we put forward an efficient attribute revocation mechanism and a task search function in the scheme to achieve dynamic on-demand services while ensuring the forward and backward security of tasks. Theoretical analysis proves the correctness of both decryption and keywords matching, and the security of each involved entity. The simulation results show that our proposed scheme achieves a significant improvement in reducing time consumption compared with several related schemes.
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network

The most vital step in mining data’s in order to have a proper decision making is the classification, it is remains important in multiple of human activities such as the industrial applications, marketing campaigns, research process and the scientific endeavors. The process of classifying involves the objects categorization into classes that are already defined. These categorizations are developed according to the identical attributes of the items or the objects. Multitudes of methods were devised to improve the accuracy in the classification to devour an enhanced performance in terms of faster convergence speed. The algorithm based on water cycle that includes the evaporation, condensation and precipitation (WC-ECP), which is a population based metaheuristic is used in the paper to improve the accuracy in the feed forward neural network (PNN-probabilistic neural network) to standardizes its random constraint choice and in turn improvise the accuracy of the categorization and the speed of the convergence. The proposed method was tested with the five dataset of UCI machine learning repository and was evinced that the WCECP-PNN performed better compared to the other evolutionary algorithms such as the GA which is also a population based Meta-heuristics
Practical Product Proofs for Lattice Commitments

We construct a practical lattice-based zero-knowledge argument for proving multiplicative relations between committed values. The underlying commitment scheme that we use is the currently most efficient one of Baum et al. (SCN 2018), and the size of our multiplicative proof (9 KB) is only slightly larger than the 7 KB required for just proving knowledge of the committed values. We additionally expand on the work of Lyubashevsky and Seiler (Eurocrypt 2018) by showing that the above-mentioned result can also apply when working over rings $$\mathbb {Z}_q[X]/(X^d+1)$$ where $$X^d+1$$ splits into low-degree factors, which is a desirable property for many applications (e.g. range proofs, multiplications over $$\mathbb {Z}_q$$ ) that take advantage of packing multiple integers into the NTT coefficients of the committed polynomial.
Emotion Detection in Hinglish(Hindi+English) Code-Mixed Social Media Text

Human communication is often embedded with emotion and it can be expressed via different mediums like vocal interaction, texts, non-verbal communication like facial expressions and gestures. Even though textual communication is a more common way of interaction the rapid utilization of social media has taken it to another level. Social media open an easy way for people to express their emotions. So people around the world utilize this opportunity and express themselves in social media platforms through texts over various subjects. People make use of these platforms to exhibit their like or dislike towards something, how they felt about a situation, their reaction to a government decision and so on. Hence, understanding the emotion expressed in such social media texts has a significant number of applications emphasizing the need to detect it. The human brain is quite intelligent to sense such kind of emotion associated with a text but for a machine to gain such perception is quite difficult. In Natural Language Processing, emotion recognition and classification is a commonly researched task where a model can detect these type of emotions. It is quite challenging when it comes to Indian Languages due to the lack of data, as well as being a multilingual society people tend to use code-mixed pattern in social media. The lack of annotated corpus in the Hindi-English code-mixed domain and unavailability of the standard model to classify, left this area of research still an exploring region. In this paper, to analyze such data, we created a dataset of 12000 Hindi-English code-mixed texts collected from various sources and annotated them with emotions Happy, Sad and Anger. In our work, a pretrained bilingual model is used to generate feature vectors and deep neural networks are employed as classification models. It is observed that with the selected features, CNN-BiLSTM gave better performance compared to other experimented models with 83.21% classification accuracy.
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight

Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm.As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model.However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear.This impedes the learning of those data-driven neural dialogue models.Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples.In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data.Note that, the proposed data manipulation framework is fully data-driven and learnable.It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples.Extensive experiments show that our framework can improve the dialogue generation performance with respect to various automatic evaluation metrics and human judgments.
Towards Emotion-aided Multi-modal Dialogue Act Classification

The task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively.But these studies limit themselves to text.Non-verbal features (change of tone, facial expressions etc.) can provide cues to identify DAs, thus stressing the benefit of incorporating multi-modal inputs in the task.Also, the emotional state of the speaker has a substantial effect on the choice of the dialogue act, since conversations are often influenced by emotions.Hence, the effect of emotion too on automatic identification of DAs needs to be studied.In this work, we address the role of both multi-modality and emotion recognition (ER) in DAC.DAC and ER help each other by way of multi-task learning.One of the major contributions of this work is a new dataset-multimodal Emotion aware Dialogue Act dataset called EMOTyDA, collected from open-sourced dialogue datasets.To demonstrate the utility of EMOTyDA, we build an attention based (self, inter-modal, inter-task) multi-modal, multi-task Deep Neural Network (DNN) for joint learning of DAs and emotions.We show empirically that multimodality and multi-tasking achieve better performance of DAC compared to uni-modal and single task DAC variants.
When Do GNNs Work: Understanding and Improving Neighborhood Aggregation

Graph Neural Networks (GNNs) have been shown to be powerful in a wide range of graph-related tasks. While there exists various GNN models, a critical common ingredient is neighborhood aggregation, where the embedding of each node is updated by referring to the embedding of its neighbors. This paper aims to provide a better understanding of this mechanisms by asking the following question: Is neighborhood aggregation always necessary and beneficial? In short, the answer is no. We carve out two conditions under which neighborhood aggregation is not helpful: (1) when a node's neighbors are highly dissimilar and (2) when a node's embedding is already similar with that of its neighbors. We propose novel metrics that quantitatively measure these two circumstances and integrate them into an Adaptive-layer module. Our experiments show that allowing for node-specific aggregation degrees have significant advantage over current GNNs.
Activity recognition using wearable sensors for tracking the elderly

Abstract A population group that is often overlooked in the recent revolution of self-tracking is the group of older people. This growing proportion of the general population is often faced with increasing health issues and discomfort. In order to come up with lifestyle advice towards the elderly, we need the ability to quantify their lifestyle, before and after an intervention. This research focuses on the task of activity recognition (AR) from accelerometer data. With that aim, we collect a substantial labelled dataset of older individuals wearing multiple devices simultaneously and performing a strict protocol of 16 activities (the GOTOV dataset, $$N=28$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>28</mml:mn> </mml:mrow> </mml:math> ). Using this dataset, we trained Random Forest AR models, under varying sensor set-ups and levels of activity description granularity. The model that combines ankle and wrist accelerometers (GENEActiv) produced the best results (accuracy $$&gt;80\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&gt;</mml:mo> <mml:mn>80</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ) for 16-class classification. At the same time, when additional physiological information is used, the accuracy increased ( $$&gt;85\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&gt;</mml:mo> <mml:mn>85</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ). To further investigate the role of granularity in our predictions, we developed the LARA algorithm, which uses a hierarchical ontology that captures prior biological knowledge to increase or decrease the level of activity granularity (merge classes). As a result, a 12-class model in which the different paces of walking were merged showed a performance above $$93\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>93</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> . Testing this 12-class model in labelled free-living pilot data, the mean balanced accuracy appeared to be reasonably high, while using the LARA algorithm, we show that a 7-class model (lying down, sitting, standing, household, walking, cycling, jumping) was optimal for accuracy and granularity. Finally, we demonstrate the use of the latter model in unlabelled free-living data from a larger lifestyle intervention study. In this paper, we make the validation data as well as the derived prediction models available to the community.
Multi-lingual scene text detection and language identification

Scene text analysis is a field of research that poses challenges to researchers owing to the background complexities, image quality, text orientation, text size, etc. The problem gets more complex when the image contains multi-lingual texts. Most scene text detection techniques approach the problem as either a feature-based or deep learning-based problem. In this work, an end-to-end system is proposed for scene text detection, localization and language identification to combine feature-based and deep learning-based approaches. The model uses Maximally Stable Extremal Regions and Stroke Width Transform for generating text proposals, followed by proposal refinement using Generative Adversarial Network. Finally, a Convolution Neural Network based model is used for language identification of the detected scene texts. Experiments have been conducted on standard datasets like KAIST, COCO, CTW1500, CVSI and ICDAR along with an in-house multi-lingual Indic scene text dataset for which the proposed model achieves satisfactory results.
Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA)

Abstract Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or ‘compositional’, in that they add up to a finite sum (e.g. 8 h of work or 100% work time). Due to their properties, compositional data need to be processed and analyzed using specifically adapted methods. Compositional data analysis (CoDA) has become a particularly established framework to handle such data in various scientific fields such as nutritional epidemiology, geology, and chemistry, but has only recently gained attention in public and occupational health sciences. In this paper, we introduce the reader to CoDA by explaining why CoDA should be used when dealing with compositional time-use data, showing how to perform CoDA, including a worked example, and pointing at some remaining challenges in CoDA. The paper concludes by emphasizing that CoDA in occupational research is still in its infancy, and stresses the need for further development and experience in the use of CoDA for time-based occupational exposures. We hope that the paper will encourage researchers to adopt and apply CoDA in studies of work exposures and health.
Seven Ways to Evaluate the Utility of Synthetic Data

Access to individual-level health data is going to be critical for managing the COVID-19 pandemic and enabling society to return to some form of (new) normal functioning. Broader data access is already starting to happen. At the same time, there has been growing alarm by the privacy community about the extent and manner of the level of data sharing that is going on with such sensitive information. In South Korea, broad data sharing has already resulted in some patients being reidentified and experiencing judgment and ridicule, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,2</sup> and some governments have begun to reduce the amount of information being shared about COVID-19 cases. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3-8</sup> Data synthesis can provide a solution by enabling access to useful information while ensuring reasonable privacy protections.
Optimizing Implementations of Linear Layers

In this paper, we propose a new heuristic algorithm to search efficient implementations (in terms of Xor count) of linear layers used in symmetric-key cryptography. It is observed that the implementation cost of an invertible matrix is related to its matrix decomposition if sequential-Xor (s-Xor) metric is considered, thus reducing the implementation cost is equivalent to constructing an optimized matrix decomposition. The basic idea of this work is to find various matrix decompositions for a given matrix and optimize those decompositions to pick the best implementation. In order to optimize matrix decompositions, we present several matrix multiplication rules over F2, which are proved to be very powerful in reducing the implementation cost. We illustrate this heuristic by searching implementations of several matrices proposed recently and matrices already used in block ciphers and Hash functions, and the results show that our heuristic performs equally good or outperforms Paar’s and Boyar-Peralta’s heuristics in most cases.
Experimental Estimation of Quantum State Properties from Classical Shadows

Full quantum tomography of high-dimensional quantum systems is experimentally infeasible due to the exponential scaling of the number of required measurements on the number of qubits in the system. However, several ideas were proposed recently for predicting the limited number of features for these states, or estimating the expectation values of operators, without the need for full state reconstruction. These ideas go under the general name of shadow tomography. Here we provide an experimental demonstration of property estimation based on classical shadows proposed in [H.-Y. Huang, R. Kueng, J. Preskill. Nat. Phys. https://doi.org/10.1038/s41567-020-0932-7 (2020)] and study its performance in the quantum optical experiment with high-dimensional spatial states of photons. We show on experimental data how this procedure outperforms conventional state reconstruction in fidelity estimation from a limited number of measurements.
Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation

Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal and suffer from a high false alarm rate. To address these issues, we employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD. The result is a flexible method that allows the user to indicate whether change points should be sought in the time domain, frequency domain or both. Detectable change points include abrupt changes in the slope, mean, variance, autocorrelation function and frequency spectrum. We demonstrate that our proposed method is consistently highly competitive or superior to baseline methods on diverse simulated and real-life benchmark data sets. Finally, we mitigate the issue of false detection alarms through the use of a postprocessing procedure that combines a matched filter and a newly proposed change point score. We show that this combination drastically improves the performance of our method as well as all baseline methods.
Attacks on state-of-the-art face recognition using attentional adversarial attack generative network

Abstract With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network ( A 3 G N ), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person.
Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks

With the continuous development of deep learning, face detection methods have made the greatest progress. For real-time detection, cascade CNN based on the lightweight model is still the dominant structure that predicts face in a coarse-to-fine manner with strong generalization ability. Compared to other methods, it is not required for a fixed size of the input. However, MTCNN still has poor performance in detecting tiny targets. To improve model generalization ability, we propose a Receptive Field Enhanced Multi-Task Cascaded CNN. This network takes advantage of the Inception-V2 block and receptive field block to enhance the feature discriminability and robustness for small targets. The experimental results show that the performance of our network is improved by 1.08% on the AFW, 2.84% on the PASCAL FACE, 1.31% on the FDDB, and 2.3%, 2.1%, and 6.6% on the three sub-datasets of the WIDER FACE benchmark in comparison with MTCNN respectively. Furthermore, our structure uses 16% fewer parameters.
Dynamical Entanglement

Unlike the entanglement of quantum states, very little is known about the entanglement of bipartite channels, called dynamical entanglement. Here we work with the partial transpose of a superchannel, and use it to define computable measures of dynamical entanglement, such as the negativity. We show that a version of it, the max-logarithmic negativity, represents the exact asymptotic dynamical entanglement cost. We discover a family of dynamical entanglement measures that provide necessary and sufficient conditions for bipartite channel simulation under local operations and classical communication and under operations with positive partial transpose.