Reviewerly/exHarmony huggingface dataset:
­Use of Artificial Intelligence for Public Health Surveillance: A case study to develop a Machine Learning-algorithm to estimate the incidence of Diabetes Mellitus in France

Abstract Background The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years. Methods We selected a training data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS) to develop a ML-algorithm for estimating the incidence of diabetes. To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. validation of model with test data set and viii. selection of the model. Results The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3,468 variables, which were similar in SNDS and CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last two years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66 – 0.68]. Conclusions Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions and to calculate the contribution of various risk factors on developing type 2 diabetes.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at this https URL.
Adaptive Fuzzy Systems for Predictive Diagnostics of Railway Facilities

The problem of the railway infrastructure predictive diagnostics is formulated and reviewed as semi-structured and weekly formalized problem. This paper presents an approach to build evolving fuzzy systems to solve such problems. Phase space granulation and fuzzy adaptation algorithm are suggested. Effectiveness of the proposed approach is demonstrated on adaptations to cascades of bifurcations and nonlinear behavior of the logistic mapping.
Rotational-Linear Attack: A New Framework of Cryptanalysis on ARX Ciphers with Applications to Chaskey

In this paper, we formulate a new framework of cryptanalysis called rotational-linear attack on ARX ciphers. We firstly build an efficient distinguisher for the cipher E consisted of the rotational attack and the linear attack together with some intermediate variables. Then a key recovery technique is introduced with which we can recover some bits of the last whitening key in the related-key scenario. To decrease data complexity of our attack, we also apply a new method, called bit flipping, in the rotational cryptanalysis for the first time and the effective partitioning technique to the key-recovery part.Applying the new framework of attack to the MAC algorithm Chaskey, we build a full-round distinguisher over it. Besides, we have recovered 21 bits of information of the key in the related-key scenario, for keys belonging to a large weak-key class based on 6-round distinguisher. The data complexity is \(2^{38.8}\) and the time complexity is \(2^{46.8}\). Before our work, the rotational distinguisher can only be used to reveal key information by checking weak-key conditions. This is the first time it is applied in a last-rounds key-recovery attack. We build a 17-round rotational-linear distinguisher for ChaCha permutation as an improvement compared to single rotational cryptanalysis over it.
Research on target tracking method of sports video based on multi-template matching

The video tracking method based on single template matching is affected by the shape and transformation of single template, resulting in large tracking error and poor tracking effect. In this paper, a method of object tracking in sports video based on multi template matching is proposed. After preprocessing the video image, the background difference is used to detect the target in the video. Based on the selected benchmark template, the matching template is generated by multi-level geometric transformation and the template updating strategy is formulated. Extract the features of the template and image, and complete the tracking of sports video target by calculating the matching degree between multiple templates and image features. Through the contrast experiment, it is proved that the multi template matching tracking method has the advantages of short tracking time, high precision and better tracking effect.
An Index of African Monetary Integration (IAMI)

This study improves the African Regional Integration Index (ARII) proposed by the African Union, the African Development Bank and the United Nations Economic Commission for Africa by providing a theoretical framework and addressing shortcomings related to weighting and aggregation of the indicator. This paper measures monetary integration in the eight African Regional Economic Communities (RECs) by constructing an Index of African Monetary Integration (IAMI). It proposes an Optimal Currency Area as theoretical framework and uses a panel approach to appreciate the dynamics of the index over different periods of time. The findings show that: (i) inflation and finance (trade and mobility) present the highest (lowest) score while ECOWAS is (EAC and IGAD are) the highest (least) performing. (ii) Surprisingly, in most RECs, the highest contributors to wealth creation are not the top performers in regional monetary integration. (iii) The RECs in Africa are characterized by a stable monetary integration which is different from the gradual process usually observed in monetary integration because with the exception of the EAC and UMA, the dynamics of IAMI show a steady trend in the overall index across time. Policy implications are discussed.
Tutorial: Decentralized Digital Identity with Blockchain

In the digital world, identity data and records are scattered across each digital service‚Äôs database. Government entities, public and private companies hold their own instances of a person‚Äôs set of credentials which are often outdated and seldom integrated. Such dissemination of data increases the attack surface for cyber-criminals and companies fail too frequently to protect the information, increasing the cases of phishing and identity theft. The owner of the identity has few ‚Äìif any‚Äì means to control when, how and by whom their information is accessed or used. Blockchain offers a decentralized way of issuing and verification of people‚Äôs credentials, without the need of disclosing personal identification information and under control of the identity owner. This one-hour presentation will cover the following agenda: – Introduction to Blockchain – Self-sovereign identity use case – Review existing use cases and applications – Decentralized identity with Blockchain Architecture Overview 5. – Cloud based resources to get started
Trends of Academic Mobility in Russia: Statistical Analysis and Forecasting

Introduction. Currently, the competitiveness of universities is determined by their level of internationalization, which cannot be imagined without academic mobility. This is reflected in world university rankings. Russian universities do not occupy leading positions in these ratings; they are losing their role in the global educational space. Today, it is necessary to make prompt, constructive decisions that contribute to the growth of academic mobility, however, the development of measures in this area is not possible without prospective estimates of indicators characterizing both the development of the internationalization of higher education and growth quality of education. In this regard, the purpose of the presented article is to build predictive scenarios for academic mobility in Russia. Materials and Methods. The research employs statistical methods of time series analysis and forecasting. Prospective values of the number of foreign citizens from various countries studying at Russian universities were obtained on the basis of exponential functions and adaptive forecasting methods. The choice of the model with the best forecast values is based on a formal-logical analysis coupled with a comparison of the main characteristics of accuracy and quality. Results. The article presents predictive scenarios of academic mobility in Russia: regressive, conservative, moderately optimistic. The obtained forecast results determine the possibilities of increasing the competitiveness of Russian education and improving the position of Russian universities in the world rankings only if highquality digital education is formed and developed on the basis of an infrastructure that ensures high technical reliability. Discussion and Conclusion. The proposed methodology and assessments of the prospective values of the targets for the growth of university academic mobility not only substantiate the expansion of the possibilities of using adaptive forecasting methods, which determines the scientific contribution of the study conducted by the authors, but can also be taken into account when adjusting/updating the planned values of indicators introduced into state initiatives to stimulate internationalization of Russian higher education, which points to the high practical importance of this article.
Word-Level Style Control for Expressive, Non-attentive Speech Synthesis

This paper presents an expressive speech synthesis architecture for modeling and controlling the speaking style at a word level. It attempts to learn word-level stylistic and prosodic representations of the speech data, with the aid of two encoders. The first one models style by finding a combination of style tokens for each word given the acoustic features, and the second outputs a word-level sequence conditioned only on the phonetic information in order to disentangle it from the style information. The two encoder outputs are aligned and concatenated with the phoneme encoder outputs and then decoded with a Non-Attentive Tacotron model. An extra prior encoder is used to predict the style tokens autoregressively, in order for the model to be able to run without a reference utterance. We find that the resulting model gives both word-level and global control over the style, as well as prosody transfer capabilities.
Cooperative learning in times of COVID-19

In the context of the health emergency, due to COVID-19, teachers had to use digital competence, to adapt to remote or distance education, in such a way that some strategies could be developed in this new scenario that education faced, For this reason, the objective of this study was to determine the relationship between critical thinking, creativity and cooperative learning in public management institutions at the Secondary level within this conjuncture. A correlational quantitative approach research was used, using a population of 207 students, from the statistical processing it was concluded that there is a relationship between the study variables, which contribute to interaction and the development of team activities.
Discovering Useful Compact Sets of Sequential Rules in a Long Sequence

We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful sets of sequential rules. The rules are selected using an MDL-inspired criterion that favors compactness and relies on a novel rule-based encoding scheme for sequences. Our evaluation shows that COSSU can successfully retrieve relevant sets of closed sequential rules from a long sequence. Such rules constitute an interpretable model that exhibits competitive accuracy for the tasks of next-element prediction and classification.
Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES)

Evaluation is a cornerstone in the process of developing and deploying recommender systems. The PERSPECTIVES workshop brought together academia and industry to critically reflect on the evaluation of recommender systems. Particularly, the workshop aimed to shed light on the different, and maybe even diverging or contradictory perspectives on the evaluation of recommender systems. Papers reporting a reflection on problems regarding recommender systems evaluation and lessons learned were solicited. The workshop combined flash presentations of accepted papers, a keynote from industry, and an interactive part with discussions in break-out rooms as well as in the plenum. The workshop complemented the program of the main conference as it emphasized problems and lessons learned, fostered exchange integrating various perspectives on evaluation, and sought to move the recommender systems community forward as an outcome of the workshop.
Color-image encryption scheme based on channel fusion and spherical diffraction

A secure encryption scheme for color images based on channel fusion and spherical diffraction is proposed in this paper. In the proposed encryption scheme, a channel fusion technology based on the discrete wavelet transformation is used to transform color images into single-channel grayscale images, firstly. In the process of transformation, the hyperchaotic system is used to permutation and diffuse the information of RGB channels to reduce the correlation of channels. Then the fused image is encrypted by spherical diffraction transform. Finally, the complex-valued diffraction result is decomposed into two real parts by the improved equal module decomposition, which are the ciphertext and the private key, respectively. Compared with the traditional color image encryption schemes that encrypt RGB channels separately, the proposed scheme is highly secure and robust.
Indian Monument Recognition Using Deep Learning

Monuments are physical structures built or created dedicated to a person or event. Their importance to the region they belong to makes their documentation and preservation important. Monuments being 3D objects, pose is an issue in terms of the difference in perception of the monument images. There can be an immense variation because of the viewpoint from which the image is being taken. To overcome this issue, Data Augmentation is used in this study. Random Flip, Random Rotation, Random Translation and Random Zoom, Random Contrast, Random Hue, Random Brightness, and Random Saturation operations under Data Augmentation are applied on the dataset. This research uses Deep Learning architectures on a hybrid dataset of “Indian Monument Recognition Dataset” and “Qutub Complex Monuments' Images Dataset.” InceptionV3, MobileNet, ResNet50, AlexNet, and VGG16 recognize the monuments with an accuracy of 97.79%, 93.73%, 86.47%, 68.88%, and 61.33% respectively.
Measurement of phase refractive index directly from phase distributions detected with a spectrally resolved interferometer

A phase refractive index is measured directly from an unwrapped spectral phase distribution whose <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> <mml:mi>π<!-- π --></mml:mi> </mml:math> ambiguity is determined by fitting the spectral phase distribution with functions based on Cauchy’s equation. The phase refractive index of a quartz glass with 20 µm thickness is measured exactly from three spectral phase distributions detected in two different configurations of a spectrally resolved interferometer. Since there is a high possibility that the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> <mml:mi>π<!-- π --></mml:mi> </mml:math> ambiguity cannot be correctly determined when there is a large difference between a function of the real refractive index and Cauchy’s equation, characteristics of the fitting are examined.
Deep Learning Method for Human Emotion Detection and Text Analysis Based on Big Data

Affective analysis method is constantly exploring from shallow learning to deep learning. In deep learning, due to the deepening of learning layers of recurrent neural network (RNN), it will lead to the problem of gradient dispersion. Therefore, an ISTM network model is proposed to solve the problem. This paper proposes a neural network architecture model, multigru, which is constructed by using an important variant of LSTM, Gru (gated recurrent unit). Multigru uses Gru for multi-layer stacking to reduce information loss. The experimental results show that the performance of this model is better than that of LSTM and other models.
Deep Learning Based Formation Control of Drones

Robot swarms can accomplish demanding missions fast, efficiently, and accurately. For a robust operation, robot swarms need to be equipped with reliable localization algorithms. Usually, the global positioning system (GPS) and motion capture cameras are employed to provide robot swarms with absolute position data with high precision. However, such infrastructures make the robots dependent on certain areas and hence reduce robustness. Thus, robots should have onboard localization capabilities to demonstrate a swarm behavior in challenging scenarios such as GPS-denied environments. Motivated by the need for a reliable onboard localization framework for robot swarms, we present a distance and vision-based localization algorithm integrated into a distributed formation control framework for three-drone systems. The proposed approach is established upon the bearing angles and the relative distances between the pairs of drones in a cyclic formation where each drone follows its coleader. We equip each drone with a monocular camera sensor and derive the bearing angle between a drone and its coleader with the recently developed deep learning algorithms. The onboard measurements are then relayed back to the formation control algorithm in which every drone computes its control action in its own frame based on its neighbors only, forming a completely distributed architecture. The proposed approach enables three-drone systems to perform in coordination indepen- dent of any external infrastructure. We validate the performance of our approach in a realistic simulation environment.
Systematic generation of moment invariant bases for 2D and 3D tensor fields

Moment invariants have been successfully applied to pattern detection tasks in 2D and 3D scalar, vector, and matrix valued data. However so far no flexible basis of invariants exists, i.e., no set that is optimal in the sense that it is complete and independent for every input pattern. In this paper, we prove that a basis of moment invariants can be generated that consists of tensor contractions of not more than two different moment tensors each under the conjecture of the set of all possible tensor contractions to be complete. This result allows us to derive the first generator algorithm that produces flexible bases of moment invariants with respect to orthogonal transformations by selecting a single non-zero moment to pair with all others in these two-factor products. Since at least one non-zero moment can be found in every non-zero pattern, this approach always generates a complete set of descriptors.
Efficient particle smoothing for Bayesian inference in dynamic survival models

This article proposes an efficient Bayesian inference for piecewise exponential hazard (PEH) models, which allow the effect of a covariate on the survival time to vary over time. The proposed inference methodology is based on a particle smoothing algorithm that depends on three particle filters. Efficient proposal (importance) distributions for the particle filters tailored to the nature of survival data and PEH models are developed using the Laplace approximation of the posterior distribution and linear Bayes theory. The algorithm is applied to both simulated and real data, and the results show that it is faster and more efficient than a state-of-the-art MCMC sampler, and scales well in high-dimensional and relatively large data.
Measurement-device-independenization of quantum key distribution protocols

Quantum key distribution (QKD) allows the legitimate partner to establish a secret key whose security only depends on physical laws. In recent years, research on QKD by employing insecure measurement devices, namely measurement-device-independent QKD (MDI-QKD) is increased. MDI-QKD removes all attacks on measurement devices and thus an untrusted third party can be employed for measuring. However, a weakness of previous MDI-QKD protocols is the need for joint measurements such as Bell measurements whose efficiency is low in practice. On the other hand, can all QKD protocols become measurement-device-independent remains a problem. In this paper, we present a scheme making prepare-measure QKD protocols become MDI-QKD protocols, called ′measurement−device−independenization′, which does not need to employ joint measurements and could be efficiently implemented by weak coherence sources. The protocol might look like the detector-device-independent (DDI) protocols but it is also secure under the Trojan horse attack. To illustrate this, we investigate the photon-number-adding (PNA) attack and present a scheme, called ′photon−number−purification′, which can also be employed to close loopholes for previous protocols such as DDI and plug-and-play ones.
SegVisRL: development of a robot's neural visuomotor and planning system for lunar exploration

In this paper, we present an approach for the learning of a visuomotor system for a robotic rover using reinforcement learning (RL) within a simulation that combines both proprioceptive and exteroceptive information. With a rising interest in private lunar exploration, compact, power-efficient and cost-efficient rover concepts are becoming numerous. Many of these still use LIDARs for obstacle avoidance, however, using a camera could help achieve a more optimal system. Visual data contains structured information that can be used for understanding texture, localization and object recognition. That said, it is hard to use RL with raw high-resolution visual data due to the dimensional size, with over 6 million inputs in an image alone. By preprocessing the images to be segmented and down sampling to reduce size, we are able to achieve stable learning, and to teach a robot to understand footage to avoid obstacles and reach its goals. This method combines exteroceptive data from the camera and the goal location information along with proprioceptive information, such as the robot”s angular rotation. Moreover, whereas most approaches feed the RL output to a motion controller or trajectory generator, our RL agent is able to directly control the actuator outputs of the rover.